Two Novel Myxozoans from Pirate Perch Aphredoderus sayanus (Gilliams, 1824) in the Upper Mississippi River, Including the First North American Species of Hennegoides Lom, Tonguthai, & Dyková, 1991.

The pirate perch Aphredoderus sayanus is a relatively small fish species found in rivers throughout much of the eastern United States. Due to their cryptic nature, relatively little is known regarding their parasite fauna. A survey of pirate perch from the upper Mississippi River revealed 2 novel myxozoans. Hennegoides flockae n. sp. was observed in heavily infected gills where the lamellae featured irregular expansion by bulbous myxozoan polysporic plasmodia, typically affecting the middle to distal half of the filaments. When severe, infection of sequential filaments was such that the filaments were fused, forming what appeared as multicystic/lobular parasitic aggregates subdivided by fine epithelial cords. The total myxospore length of Hennegoides flockae was 35.4-46.4 (41.3 ± 3.3) and the spore body, asymmetrically ovoid in valvular view, was 15.4-18.7 (17.0 ± 0.7) × 7.1-8.7 (7.9 ± 0.4). Henneguya marcquenskiae n. sp. was observed in the liver with plasmodia present randomly and infrequently in the hepatocellular parenchyma. The total myxospore length for Henneguya marcquenskiae was 39.5-55.9 (48.4 ± 4.2), with the spore body being lanceolate, 13.9-16.5 (15.4 ± 0.7) × 7.1-9.0 (8.3 ± 0.5). Phylogenetic analysis of the SSU rRNA gene placed both Hennegoides flockae and Henneguya marcquenskiae as sisters to each other in a clade containing other Myxozoans known to infect the gills of esocids, percids, and centrarchids. These parasites represent the first reports of Henneguya and Hennegoides from pirate perch, with the latter being the first report of this genus outside of the Asian continent.

reactIDR: evaluation of the statistical reproducibility of high-throughput structural analyses towards a robust RNA structure prediction.

BACKGROUND: Recently, next-generation sequencing techniques have been applied for the detection of RNA secondary structures, which is referred to as high-throughput RNA structural (HTS) analyses, and many different protocols have been used to detect comprehensive RNA structures at single-nucleotide resolution. However, the existing computational analyses heavily depend on the experimental methodology to generate data, which results in difficulties associated with statistically sound comparisons or combining the results obtained using different HTS methods. RESULTS: Here, we introduced a statistical framework, reactIDR, which can be applied to the experimental data obtained using multiple HTS methodologies. Using this approach, nucleotides are classified into three structural categories, loop, stem/background, and unmapped. reactIDR uses the irreproducible discovery rate (IDR) with a hidden Markov model to discriminate between the true and spurious signals obtained in the replicated HTS experiments accurately, and it is able to incorporate an expectation-maximization algorithm and supervised learning for efficient parameter optimization. The results of our analyses of the real-life HTS data showed that reactIDR had the highest accuracy in the classification of ribosomal RNA stem/loop structures when using both individual and integrated HTS datasets, and its results corresponded the best to the three-dimensional structures. CONCLUSIONS: We have developed a novel software, reactIDR, for the prediction of stem/loop regions from the HTS analysis datasets. For the rRNA structure analyses, reactIDR was shown to have robust accuracy across different datasets by using the reproducibility criterion, suggesting its potential for increasing the value of existing HTS datasets. reactIDR is publicly available at https://github.com/carushi/reactIDR .

The influence of ignoring secondary structure on divergence time estimates from ribosomal RNA genes.

Genes coding for ribosomal RNA molecules (rDNA) are among the most popular markers in molecular phylogenetics and evolution. However, coevolution of sites that code for pairing regions (stems) in the RNA secondary structure can make it challenging to obtain accurate results from such loci. While the influence of ignoring secondary structure on multiple sequence alignment and tree topology has been investigated in numerous studies, its effect on molecular divergence time estimates is still poorly known. Here, I investigate this issue in Bayesian Markov Chain Monte Carlo (BMCMC) and penalized likelihood (PL) frameworks, using empirical datasets from dragonflies (Odonata: Anisoptera) and glass sponges (Porifera: Hexactinellida). My results indicate that highly biased inferences under substitution models that ignore secondary structure only occur if maximum-likelihood estimates of branch lengths are used as input to PL dating, whereas in a BMCMC framework and in PL dating based on Bayesian consensus branch lengths, the effect is far less severe. I conclude that accounting for coevolution of paired sites in molecular dating studies is not as important as previously suggested, as long as the estimates are based on Bayesian consensus branch lengths instead of ML point estimates. This finding is especially relevant for studies where computational limitations do not allow the use of secondary-structure specific substitution models, or where accurate consensus structures cannot be predicted. I also found that the magnitude and direction (over- vs. underestimating node ages) of bias in age estimates when secondary structure is ignored was not distributed randomly across the nodes of the phylogenies, a phenomenon that requires further investigation.

Ion Torrent-based transcriptional assessment of a Corynebacterium pseudotuberculosis equi strain reveals denaturing high-performance liquid chromatography a promising rRNA depletion method.

Corynebacterium pseudotuberculosis equi is a Gram-positive pathogenic bacterium which affects a variety of hosts. Besides the great economic losses it causes to horse-breeders, this organism is also known to be an important infectious agent to cattle and buffaloes. As an outcome of the efforts in characterizing the molecular basis of its virulence, several complete genome sequences were made available in recent years, enabling the large-scale assessment of genes throughout distinct isolates. Meanwhile, the RNA-seq stood out as the technology of choice for comprehensive transcriptome studies, which may bring valuable information regarding active genomic regions, despite of the still impeditive associated costs. In an attempt to increase the use of generated reads per instrument run, by effectively eliminating unwanted rRNAs from total RNA samples without relying on any commercially available kits, we applied denaturing high-performance liquid chromatography (DHPLC) as an alternative method to assess the transcriptional profile of C. pseudotuberculosis. We have found that the DHPLC depletion method, allied to Ion Torrent sequencing, allows mapping of transcripts in a comprehensive way and identifying novel transcripts when a de novo approach is used. These data encourage us to use DHPLC in future transcriptional evaluations in C. pseudotuberculosis.

A molecular phylogeny of Hemiptera inferred from mitochondrial genome sequences.

Classically, Hemiptera is comprised of two suborders: Homoptera and Heteroptera. Homoptera includes Cicadomorpha, Fulgoromorpha and Sternorrhyncha. However, according to previous molecular phylogenetic studies based on 18S rDNA, Fulgoromorpha has a closer relationship to Heteroptera than to other hemipterans, leaving Homoptera as paraphyletic. Therefore, the position of Fulgoromorpha is important for studying phylogenetic structure of Hemiptera. We inferred the evolutionary affiliations of twenty-five superfamilies of Hemiptera using mitochondrial protein-coding genes and rRNAs. We sequenced three mitogenomes, from Pyrops candelaria, Lycorma delicatula and Ricania marginalis, representing two additional families in Fulgoromorpha. Pyrops and Lycorma are representatives of an additional major family Fulgoridae in Fulgoromorpha, whereas Ricania is a second representative of the highly derived clade Ricaniidae. The organization and size of these mitogenomes are similar to those of the sequenced fulgoroid species. Our consensus phylogeny of Hemiptera largely supported the relationships (((Fulgoromorpha,Sternorrhyncha),Cicadomorpha),Heteroptera), and thus supported the classic phylogeny of Hemiptera. Selection of optimal evolutionary models (exclusion and inclusion of two rRNA genes or of third codon positions of protein-coding genes) demonstrated that rapidly evolving and saturated sites should be removed from the analyses.

The devil in the details: interactions between the branch-length prior and likelihood model affect node support and branch lengths in the phylogeny of the Psoraceae.

In popular use of Bayesian phylogenetics, a default branch-length prior is almost universally applied without knowing how a different prior would have affected the outcome. We performed Bayesian and maximum likelihood (ML) inference of phylogeny based on empirical nucleotide sequence data from a family of lichenized ascomycetes, the Psoraceae, the morphological delimitation of which has been controversial. We specifically assessed the influence of the combination of Bayesian branch-length prior and likelihood model on the properties of the Markov chain Monte Carlo tree sample, including node support, branch lengths, and taxon stability. Data included two regions of the mitochondrial ribosomal RNA gene, the internal transcribed spacer region of the nuclear ribosomal RNA gene, and the protein-coding largest subunit of RNA polymerase II. Data partitioning was performed using Bayes' factors, whereas the best-fitting model of each partition was selected using the Bayesian information criterion (BIC). Given the data and model, short Bayesian branch-length priors generate higher numbers of strongly supported nodes as well as short and topologically similar trees sampled from parts of tree space that are largely unexplored by the ML bootstrap. Long branch-length priors generate fewer strongly supported nodes and longer and more dissimilar trees that are sampled mostly from inside the range of tree space sampled by the ML bootstrap. Priors near the ML distribution of branch lengths generate the best marginal likelihood and the highest frequency of "rogue" (unstable) taxa. The branch-length prior was shown to interact with the likelihood model. Trees inferred under complex partitioned models are more affected by the stretching effect of the branch-length prior. Fewer nodes are strongly supported under a complex model given the same branch-length prior. Irrespective of model, internal branches make up a larger proportion of total tree length under the shortest branch-length priors compared with longer priors. Relative effects on branch lengths caused by the branch-length prior can be problematic to downstream phylogenetic comparative methods making use of the branch lengths. Furthermore, given the same branch-length prior, trees are on average more dissimilar under a simple unpartitioned model compared with a more complex partitioned models. The distribution of ML branch lengths was shown to better fit a gamma or Pareto distribution than an exponential one. Model adequacy tests indicate that the best-fitting model selected by the BIC is insufficient for describing data patterns in 5 of 8 partitions. More general substitution models are required to explain the data in three of these partitions, one of which also requires nonstationarity. The two mitochondrial ribosomal RNA gene partitions need heterotachous models. We found no significant correlations between, on the one hand, the amount of ambiguous data or the smallest branch-length distance to another taxon and, on the other hand, the topological stability of individual taxa. Integrating over several exponentially distributed means under the best-fitting model, node support for the family Psoraceae, including Psora, Protoblastenia, and the Micarea sylvicola group, is approximately 0.96. Support for the genus Psora is distinctly lower, but we found no evidence to contradict the current classification.

Cultivation-based assessment of lysogeny among soil bacteria.

Lysogeny has long been proposed as an important long-term maintenance strategy for autochthonous soil bacteriophages (phages). Whole genome sequence data indicate that prophage-derived sequences pervade prokaryotic genomes, but the connection between inferred prophage sequence and an active temperate phage is tenuous. Thus, definitive evidence of phage production from lysogenic prokaryotes will be critical in determining the presence and extent of temperate phage diversity existing as prophage within bacterial genomes and within environmental contexts such as soils. This study optimized methods for systematic and definitive determination of lysogeny within a collection of autochthonous soil bacteria. Twenty bacterial isolates from a range of Delaware soil environments (five from each soil) were treated with the inducing agents mitomycin C (MC) or UV light. Six isolates (30%) carried inducible temperate phages as evidenced by an increase in virus direct counts. The magnitude of induction response was highly dependent upon specific induction conditions, and corresponding burst sizes ranged from 1 to 176. Treatment with MC for 30 min yielded the largest induction responses for three of the six lysogens. Morphological analysis revealed that four of the lysogens produced lambda-like Siphoviridae particles, whereas two produced Myoviridae particles. Additionally, pulsed-field gel electrophoresis data indicated that two of the six lysogens were polylysogens, producing more than one distinct type of phage particle. These results suggest that lysogeny is relatively common among soil bacteria.

Assessment of primers designed for the subspecies-specific discrimination among Babesia canis canis, Babesia canis vogeli and Babesia canis rossi by PCR assay.

Canine babesiosis is an infectious disease caused by either Babesia gibsoni or Babesia canis protozoans. The latter is also classified under three different phylogenetic groups, referred to as subspecies B. canis canis, B. canis vogeli and B. canis rossi. The objective of the present study was to validate and standardize a PCR assay to discriminate the organisms at the subspecies level. First, the reference sequences of the 18S rRNA, 5.8S rRNA and 28S rRNA genes, including the internal transcribed spacer 1 (ITS1) and 2 (ITS2) of the most common species and subspecies of the genus Babesia were retrieved from the GenBank database. Subspecies-specific primers (BAB3, BAB4 and BAB5) and one genus-specific primer were designed from the alignment of the sequences. The PCR assays were evaluated in three different combinations of primer pairs in order to assure complete specificity for each reaction. The results of the tests had demonstrated effectiveness of the novel primer pairs BAB1/BAB3, BAB1/BAB4 and BAB1/BAB5 for the amplification of the subspecies-specific target fragments of 746 bp (B. c. canis), 546 bp (B. c. vogeli) and 342 bp (B. c. rossi) by PCR. The original enzymatic amplification assays with novel primers reported in this paper were confirmed to be a reliable tool for the specific discrimination among B. canis subspecies by single-step PCR assays.

Comparison of the recovery of Mycobacterium bovis isolates using the BACTEC MGIT 960 system, BACTEC 460 system, and Middlebrook 7H10 and 7H11 solid media.

The BACTEC Microbacteria Growth Indicator Tube (MGIT) 960 system was evaluated to determine how it compares with the BACTEC 460 radiometric system and solid media for recovery of Mycobacterium bovis from tissue samples. A total of 506 bovine lymph node samples were collected from abattoirs in the United States and Mexico between November 2003 and September 2004. Processed samples were inoculated into an MGIT 960 tube, BACTEC 460 vial, and Middlebrook 7H10 and Middlebrook 7H11 solid media. Ziehl-Neelsen slides were prepared to check for contaminants and confirm the presence of acid-fast positive bacilli. Samples containing acid-fast bacilli were confirmed as members of the Mycobacterium tuberculosis complex by a nucleic acid assay. Niacin and nitrate biochemical tests were used to distinguish M. bovis from M. tuberculosis isolates. Statistical analyses were performed to compare recovery rate, mean time to detection, contamination rates, as well as pair-wise comparisons in each category. The results showed that the MGIT 960 system had a higher recovery rate of M. bovis (122/129) than did the BACTEC 460 (102/129) and solid media system (96/129). The average time to detection was 15.8 days for the MGIT 960 system, 28.2 days for the BACTEC 460 system, and 43.4 days for solid media. Contamination rates were 6.9% for the MGIT 960 system, 3.4% for the BACTEC 460 system, and 21.7% for solid media. These results indicate the MGIT 960 system can be used as an alternative to the BACTEC 460 system for recovering M. bovis from tissue samples.

Dependence among sites in RNA evolution.

Although probabilistic models of genotype (e.g., DNA sequence) evolution have been greatly elaborated, less attention has been paid to the effect of phenotype on the evolution of the genotype. Here we propose an evolutionary model and a Bayesian inference procedure that are aimed at filling this gap. In the model, RNA secondary structure links genotype and phenotype by treating the approximate free energy of a sequence folded into a secondary structure as a surrogate for fitness. The underlying idea is that a nucleotide substitution resulting in a more stable secondary structure should have a higher rate than a substitution that yields a less stable secondary structure. This free energy approach incorporates evolutionary dependencies among sequence positions beyond those that are reflected simply by jointly modeling change at paired positions in an RNA helix. Although there is not a formal requirement with this approach that secondary structure be known and nearly invariant over evolutionary time, computational considerations make these assumptions attractive and they have been adopted in a software program that permits statistical analysis of multiple homologous sequences that are related via a known phylogenetic tree topology. Analyses of 5S ribosomal RNA sequences are presented to illustrate and quantify the strong impact that RNA secondary structure has on substitution rates. Analyses on simulated sequences show that the new inference procedure has reasonable statistical properties. Potential applications of this procedure, including improved ancestral sequence inference and location of functionally interesting sites, are discussed.

The phylogeny of Porinaceae (Ostropomycetidae) suggests a neotenic origin of perithecia in Lecanoromycetes.

The family Porinaceae (Trichotheliales) is characterized by perithecial ascomata of ascohymenial origin. The phylogenetic position of this family in the system of ascomycetes has been uncertain and is investigated using mtSSU rDNA sequences. The dataset consists of lichenized representatives of major ascomycete lineages, including those that were previously suspected as relatives of Porinaceae, such as Pyrenulaceae. The dataset was subjected to a Bayesian phylogenetic analysis implementing a Metropolis Coupled Markov Chain Monte Carlo method. The analysis confirms previous classification of the apothecial Gomphillaceae near to Graphidaceae, and suggests that the pyrenocarpous Porinaceae are also close to Graphidaceae, Gyalectaceae, and Stictidaceae. The subclass Ostropomycetidae is here suggested to include the families Gomphillaceae, Gyalectaceae, Graphidaceae (incl. Thelotremataceae), Porinaceae, and Stictidaceae. A special type of hemiangiocarpous ontogeny of the ascomata is shared throughout the Ostropomycetidae, and the closed fruit bodies of Porinaceae are apparently a result of a neotenic ontogeny. This is associated with special hymenial characters: rather thin-walled narrow asci, and a different consistency of the hymenial gels.

A phylogenetic mixture model for detecting pattern-heterogeneity in gene sequence or character-state data.

We describe a general likelihood-based 'mixture model' for inferring phylogenetic trees from gene-sequence or other character-state data. The model accommodates cases in which different sites in the alignment evolve in qualitatively distinct ways, but does not require prior knowledge of these patterns or partitioning of the data. We call this qualitative variability in the pattern of evolution across sites "pattern-heterogeneity" to distinguish it from both a homogenous process of evolution and from one characterized principally by differences in rates of evolution. We present studies to show that the model correctly retrieves the signals of pattern-heterogeneity from simulated gene-sequence data, and we apply the method to protein-coding genes and to a ribosomal 12S data set. The mixture model outperforms conventional partitioning in both these data sets. We implement the mixture model such that it can simultaneously detect rate- and pattern-heterogeneity. The model simplifies to a homogeneous model or a rate-variability model as special cases, and therefore always performs at least as well as these two approaches, and often considerably improves upon them. We make the model available within a Bayesian Markov-chain Monte Carlo framework for phylogenetic inference, as an easy-to-use computer program.

A memory-efficient dynamic programming algorithm for optimal alignment of a sequence to an RNA secondary structure.

BACKGROUND: Covariance models (CMs) are probabilistic models of RNA secondary structure, analogous to profile hidden Markov models of linear sequence. The dynamic programming algorithm for aligning a CM to an RNA sequence of length N is O(N3) in memory. This is only practical for small RNAs. RESULTS: I describe a divide and conquer variant of the alignment algorithm that is analogous to memory-efficient Myers/Miller dynamic programming algorithms for linear sequence alignment. The new algorithm has an O(N2 log N) memory complexity, at the expense of a small constant factor in time. CONCLUSIONS: Optimal ribosomal RNA structural alignments that previously required up to 150 GB of memory now require less than 270 MB.

The cost of exposing a hydrophobic loop and implications for the functional role of 4.5 S RNA in the Escherichia coli signal recognition particle.

The signal recognition particle (SRP) is an RNA-protein complex that directs ribosomes to the rough endoplasmic reticulum membrane by binding to targeting signals found on the nascent chain of proteins destined for export to the endoplasmic reticulum. We found evidence from studies with fragments of the protein component of the Escherichia coli SRP that a long hydrophobic loop (the so-called "finger loop") is detrimental to the stability of its signal peptide-binding domain, the M domain. This hydrophobic loop is highly conserved and thus may have a critical role in the function of the SRP. Given our previously reported evidence that 4.5 S RNA stabilizes the tertiary fold of the M domain (Zheng, N., and Gierasch, L. M. (1997) Mol. Cell 1, 79-87), we now propose that the functional requirement for 4.5 S RNA resides in its ability to counteract the destabilizing influence of the finger loop.

Toward assigning helical regions in alignments of ribosomal RNA and testing the appropriateness of evolutionary models.

We suggest a nucleotide substitution model that takes correlation between base-paired nucleotides into account. The model includes the estimation of the transition-transversion ratio and allows inference of the shape parameter of a discrete gamma distribution to include rate heterogeneity. A Cox-test statistic, applied to a diatom ribosomal RNA alignment, shows that the suggested correlation model explains evolution of the stem region better than usual independence models. Moreover, the Cox-test procedure is extended to shed some light upon the problem of assigning helical regions in a secondary structure based alignment. This approach provides an estimate of the percentage of stem positions that do not appear to be correlated.

A nonhyperthermophilic common ancestor to extant life forms.

The G+C nucleotide content of ribosomal RNA (rRNA) sequences is strongly correlated with the optimal growth temperature of prokaryotes. This property allows inference of the environmental temperature of the common ancestor to all life forms from knowledge of the G+C content of its rRNA sequences. A model of sequence evolution, assuming varying G+C content among lineages and unequal substitution rates among sites, was devised to estimate ancestral base compositions. This method was applied to rRNA sequences of various species representing the major lineages of life. The inferred G+C content of the common ancestor to extant life forms appears incompatible with survival at high temperature. This finding challenges a widely accepted hypothesis about the origin of life.

Prediction of RNA secondary structure based on helical regions distribution.

MOTIVATION: RNAs play an important role in many biological processes and knowing their structure is important in understanding their function. Due to difficulties in the experimental determination of RNA secondary structure, the methods of theoretical prediction for known sequences are often used. Although many different algorithms for such predictions have been developed, this problem has not yet been solved. It is thus necessary to develop new methods for predicting RNA secondary structure. The most-used at present is Zuker's algorithm which can be used to determine the minimum free energy secondary structure. However many RNA secondary structures verified by experiments are not consistent with the minimum free energy secondary structures. In order to solve this problem, a method used to search a group of secondary structures whose free energy is close to the global minimum free energy was developed by Zuker in 1989. When considering a group of secondary structures, if there is no experimental data, we cannot tell which one is better than the others. This case also occurs in combinatorial and heuristic methods. These two kinds of methods have several weaknesses. Here we show how the central limit theorem can be used to solve these problems. RESULTS: An algorithm for predicting RNA secondary structure based on helical regions distribution is presented, which can be used to find the most probable secondary structure for a given RNA sequence. It consists of three steps. First, list all possible helical regions. Second, according to central limit theorem, estimate the occurrence probability of every helical region based on the Monte Carlo simulation. Third, add the helical region with the biggest probability to the current structure and eliminate the helical regions incompatible with the current structure. The above processes can be repeated until no more helical regions can be added. Take the current structure as the final RNA secondary structure. In order to demonstrate the confidence of the program, a test on three RNA sequences: tRNAPhe, Pre-tRNATyr, and Tetrahymena ribosomal RNA intervening sequence, is performed. AVAILABILITY: The program is written in Turbo Pascal 7.0. The source code is available upon request. CONTACT: Wujj@nic.bmi.ac.cn or Liwj@mail.bmi.ac.cn

Estimating substitution rates in ribosomal RNA genes.

A model is introduced describing nucleotide substitution in ribosomal RNA (rRNA) genes. In this model, substitution in the stem and loop regions of rRNA is modeled with 16- and four-state continuous time Markov chains, respectively. The mean substitution rates at nucleotide sites are assumed to follow gamma distributions that are different for the two types of regions. The simplest formulation of the model allows for explicit expressions for transition probabilities of the Markov processes to be found. These expressions were used to analyze several 16S-like rRNA genes from higher eukaryotes with the maximum likelihood method. Although the observed proportion of invariable sites was only slightly higher in the stem regions, the estimated average substitution rates in the stem regions were almost two times as high as in the loop regions. Therefore, the degree of site heterogeneity of substitution rates in the stem regions seems to be higher than in the loop regions of animal 16S-like rRNAs due to presence of a few rapidly evolving sites. The model appears to be helpful in understanding the regularities of nucleotide substitution in rRNAs and probably minimizing errors in recovering phylogeny for distantly related taxa from these genes.

The computer simulation of RNA folding involving pseudoknot formation.

The algorithm and the program for the prediction of RNA secondary structure with pseudoknot formation have been proposed. The algorithm simulates stepwise folding by generating random structures using Monte Carlo method, followed by the selection of helices to final structure on the basis of both their probabilities of occurrence in a random structure and free energy parameters. The program versions have been tested on ribosomal RNA structures and on RNAs with pseudoknots evidenced by experimental data. It is shown that the simulation of folding during RNA synthesis improves the results. The introduction of pseudoknot formation permits to predict the pseudoknotted structures and to improve the prediction of long-range interactions. The computer program is rather fast and allows to predict the structures for long RNAs without using large memory volumes in usual personal computer.