Comparative transcriptomics of fungal endophytes in co-culture with their moss host Dicranum scoparium reveals fungal trophic lability and moss unchanged to slightly increased growth rates.

Mosses harbor fungi whose interactions within their hosts remain largely unexplored. Trophic ranges of fungal endophytes from the moss Dicranum scoparium were hypothesized to encompass saprotrophism. This moss is an ideal host to study fungal trophic lability because of its natural senescence gradient, and because it can be grown axenically. Dicranum scoparium was co-cultured with each of eight endophytic fungi isolated from naturally occurring D. scoparium. Moss growth rates, and gene expression levels (RNA sequencing) of fungi and D. scoparium, were compared between axenic and co-culture treatments. Functional lability of two fungal endophytes was tested by comparing their RNA expression levels when colonizing living vs dead gametophytes. Growth rates of D. scoparium were unchanged, or increased, when in co-culture. One fungal isolate (Hyaloscyphaceae sp.) that promoted moss growth was associated with differential expression of auxin-related genes. When grown with living vs dead gametophytes, Coniochaeta sp. switched from having upregulated carbohydrate transporter activity to upregulated oxidation-based degradation, suggesting an endophytism to saprotrophism transition. However, no such transition was detected for Hyaloscyphaceae sp. Individually, fungal endophytes did not negatively impact growth rates of D. scoparium. Our results support the long-standing hypothesis that some fungal endophytes can switch to saprotrophism.

Heterospecific Neighbor Plants Impact Root Microbiome Diversity and Molecular Function of Root Fungi.

Within the forest community, competition and facilitation between adjacent-growing conspecific and heterospecific plants are mediated by interactions involving common mycorrhizal networks. The ability of plants to alter their neighbor's microbiome is well documented, but the molecular biology of plant-fungal interactions during competition and facilitation has not been previously examined. We used a common soil-plant bioassay experiment to study molecular plant-microbial interactions among rhizosphere communities associated with Pinus taeda (native host) and Populus trichocarpa (non-native host). Gene expression of interacting fungal and bacterial rhizosphere communities was compared among three plant-pairs: Populus growing with Populus, Populus with Pinus, and Pinus with Pinus. Our results demonstrate that heterospecific plant partners affect the assembly of root microbiomes, including the changes in the structure of host specific community. Comparative metatranscriptomics reveals that several species of ectomycorrhizal fungi (EMF) and saprotrophic fungi exhibit different patterns of functional and regulatory gene expression with these two plant hosts. Heterospecific plants affect the transcriptional expression pattern of EMF host-specialists (e.g., Pinus-associated Suillus spp.) on both plant species, mainly including the genes involved in the transportation of amino acids, carbohydrates, and inorganic ions. Alteration of root microbiome by neighboring plants may help regulate basic plant physiological processes via modulation of molecular functions in the root microbiome.

An assembly-free method of phylogeny reconstruction using short-read sequences from pooled samples without barcodes.

A current strategy for obtaining haplotype information from several individuals involves short-read sequencing of pooled amplicons, where fragments from each individual is identified by a unique DNA barcode. In this paper, we report a new method to recover the phylogeny of haplotypes from short-read sequences obtained using pooled amplicons from a mixture of individuals, without barcoding. The method, AFPhyloMix, accepts an alignment of the mixture of reads against a reference sequence, obtains the single-nucleotide-polymorphisms (SNP) patterns along the alignment, and constructs the phylogenetic tree according to the SNP patterns. AFPhyloMix adopts a Bayesian inference model to estimate the phylogeny of the haplotypes and their relative abundances, given that the number of haplotypes is known. In our simulations, AFPhyloMix achieved at least 80% accuracy at recovering the phylogenies and relative abundances of the constituent haplotypes, for mixtures with up to 15 haplotypes. AFPhyloMix also worked well on a real data set of kangaroo mitochondrial DNA sequences.

Timing the Evolutionary Advent of Cyanobacteria and the Later Great Oxidation Event Using Gene Phylogenies of a Sunscreen.

The biosynthesis of the unique cyanobacterial (oxyphotobacterial) indole-phenolic UVA sunscreen, scytonemin, is coded for in a conserved operon that contains both core metabolic genes and accessory, aromatic amino acid biosynthesis genes dedicated to supplying scytonemin's precursors. Comparative genomics shows conservation of this operon in many, but not all, cyanobacterial lineages. Phylogenetic analyses of the operon's aromatic amino acid genes indicate that five of them were recruited into the operon after duplication events of their respective housekeeping cyanobacterial cognates. We combined the fossil record of cyanobacteria and relaxed molecular clock models to obtain multiple estimates of these duplication events, setting a minimum age for the evolutionary advent of scytonemin at 2.1 ± 0.3 billion years. The same analyses were used to estimate the advent of cyanobacteria as a group (and thus the appearance of oxygenic photosynthesis), at 3.6 ± 0.2 billion years before present. Post hoc interpretation of 16S rRNA-based Bayesian analyses was consistent with these estimates. Because of physiological constraints on the use of UVA sunscreens in general, and the biochemical constraints of scytonemin in particular, scytonemin's age must postdate the time when Earth's atmosphere turned oxic, known as the Great Oxidation Event (GOE). Indeed, our biological estimate is in agreement with independent geochemical estimates for the GOE. The difference between the estimated ages of oxygenic photosynthesis and the GOE indicates the long span (on the order of a billion years) of the era of "oxygen oases," when oxygen was available locally but not globally.IMPORTANCE The advent of cyanobacteria, with their invention of oxygenic photosynthesis, and the Great Oxidation Event are arguably among the most important events in the evolutionary history of life on Earth. Oxygen is a significant toxicant to all life, but its accumulation in the atmosphere also enabled the successful development and proliferation of many aerobic organisms, especially metazoans. The currently favored dating of the Great Oxidation Event is based on the geochemical rock record. Similarly, the advent of cyanobacteria is also often drawn from the same estimates because in older rocks paleontological evidence is scarce or has been discredited. Efforts to obtain molecular evolutionary alternatives have offered widely divergent estimates. Our analyses provide a novel means to circumvent these limitations and allow us to estimate the large time gap between the two events.

accuMUlate: a mutation caller designed for mutation accumulation experiments.

Summary: Mutation accumulation (MA) is the most widely used method for directly studying the effects of mutation. By sequencing whole genomes from MA lines, researchers can directly study the rate and molecular spectra of spontaneous mutations and use these results to understand how mutation contributes to biological processes. At present there is no software designed specifically for identifying mutations from MA lines. Here we describe accuMUlate, a probabilistic mutation caller that reflects the design of a typical MA experiment while being flexible enough to accommodate properties unique to any particular experiment. Availability and implementation accuMUlate is available from https://github.com/dwinter/accuMUlate. Supplementary information: Supplementary data are available at Bioinformatics online.

Estimating error models for whole genome sequencing using mixtures of Dirichlet-multinomial distributions.

MOTIVATION: Accurate identification of genotypes is an essential part of the analysis of genomic data, including in identification of sequence polymorphisms, linking mutations with disease and determining mutation rates. Biological and technical processes that adversely affect genotyping include copy-number-variation, paralogous sequences, library preparation, sequencing error and reference-mapping biases, among others. RESULTS: We modeled the read depth for all data as a mixture of Dirichlet-multinomial distributions, resulting in significant improvements over previously used models. In most cases the best model was comprised of two distributions. The major-component distribution is similar to a binomial distribution with low error and low reference bias. The minor-component distribution is overdispersed with higher error and reference bias. We also found that sites fitting the minor component are enriched for copy number variants and low complexity regions, which can produce erroneous genotype calls. By removing sites that do not fit the major component, we can improve the accuracy of genotype calls. AVAILABILITY AND IMPLEMENTATION: Methods and data files are available at https://github.com/CartwrightLab/WuEtAl2017/ (doi:10.5281/zenodo.256858). CONTACT: cartwright@asu.edu. SUPPLEMENTARY INFORMATION: Supplementary data is available at Bioinformatics online.

Low Base-Substitution Mutation Rate in the Germline Genome of the Ciliate Tetrahymena thermophil.

Mutation is the ultimate source of all genetic variation and is, therefore, central to evolutionary change. Previous work on Paramecium tetraurelia found an unusually low germline base-substitution mutation rate in this ciliate. Here, we tested the generality of this result among ciliates using Tetrahymena thermophila. We sequenced the genomes of 10 lines of T. thermophila that had each undergone approximately 1,000 generations of mutation accumulation (MA). We applied an existing mutation-calling pipeline and developed a new probabilistic mutation detection approach that directly models the design of an MA experiment and accommodates the noise introduced by mismapped reads. Our probabilistic mutation-calling method provides a straightforward way of estimating the number of sites at which a mutation could have been called if one was present, providing the denominator for our mutation rate calculations. From these methods, we find that T. thermophila has a germline base-substitution mutation rate of 7.61 × 10 - 12 per-site, per cell division, which is consistent with the low base-substitution mutation rate in P. tetraurelia. Over the course of the evolution experiment, genomic exclusion lines derived from the MA lines experienced a fitness decline that cannot be accounted for by germline base-substitution mutations alone, suggesting that other genetic or epigenetic factors must be involved. Because selection can only operate to reduce mutation rates based upon the "visible" mutational load, asexual reproduction with a transcriptionally silent germline may allow ciliates to evolve extremely low germline mutation rates.

Estimation of evolutionary parameters using short, random and partial sequences from mixed samples of anonymous individuals.

BACKGROUND: Over the last decade, next generation sequencing (NGS) has become widely available, and is now the sequencing technology of choice for most researchers. Nonetheless, NGS presents a challenge for the evolutionary biologists who wish to estimate evolutionary genetic parameters from a mixed sample of unlabelled or untagged individuals, especially when the reconstruction of full length haplotypes can be unreliable. We propose two novel approaches, least squares estimation (LS) and Approximate Bayesian Computation Markov chain Monte Carlo estimation (ABC-MCMC), to infer evolutionary genetic parameters from a collection of short-read sequences obtained from a mixed sample of anonymous DNA using the frequencies of nucleotides at each site only without reconstructing the full-length alignment nor the phylogeny. RESULTS: We used simulations to evaluate the performance of these algorithms, and our results demonstrate that LS performs poorly because bootstrap 95% Confidence Intervals (CIs) tend to under- or over-estimate the true values of the parameters. In contrast, ABC-MCMC 95% Highest Posterior Density (HPD) intervals recovered from ABC-MCMC enclosed the true parameter values with a rate approximately equivalent to that obtained using BEAST, a program that implements a Bayesian MCMC estimation of evolutionary parameters using full-length sequences. Because there is a loss of information with the use of sitewise nucleotide frequencies alone, the ABC-MCMC 95% HPDs are larger than those obtained by BEAST. CONCLUSION: We propose two novel algorithms to estimate evolutionary genetic parameters based on the proportion of each nucleotide. The LS method cannot be recommended as a standalone method for evolutionary parameter estimation. On the other hand, parameters recovered by ABC-MCMC are comparable to those obtained using BEAST, but with larger 95% HPDs. One major advantage of ABC-MCMC is that computational time scales linearly with the number of short-read sequences, and is independent of the number of full-length sequences in the original data. This allows us to perform the analysis on NGS datasets with large numbers of short read fragments. The source code for ABC-MCMC is available at https://github.com/stevenhwu/SF-ABC.

A Bayesian model for classifying all differentially expressed proteins simultaneously in 2D PAGE gels.

BACKGROUND: Two-dimensional polyacrylamide gel electrophoresis (2D PAGE) is commonly used to identify differentially expressed proteins under two or more experimental or observational conditions. Wu et al (2009) developed a univariate probabilistic model which was used to identify differential expression between Case and Control groups, by applying a Likelihood Ratio Test (LRT) to each protein on a 2D PAGE. In contrast to commonly used statistical approaches, this model takes into account the two possible causes of missing values in 2D PAGE: either (1) the non-expression of a protein; or (2) a level of expression that falls below the limit of detection. RESULTS: We develop a global Bayesian model which extends the previously described model. Unlike the univariate approach, the model reported here is able treat all differentially expressed proteins simultaneously. Whereas each protein is modelled by the univariate likelihood function previously described, several global distributions are used to model the underlying relationship between the parameters associated with individual proteins. These global distributions are able to combine information from each protein to give more accurate estimates of the true parameters. In our implementation of the procedure, all parameters are recovered by Markov chain Monte Carlo (MCMC) integration. The 95% highest posterior density (HPD) intervals for the marginal posterior distributions are used to determine whether differences in protein expression are due to differences in mean expression intensities, and/or differences in the probabilities of expression. CONCLUSIONS: Simulation analyses showed that the global model is able to accurately recover the underlying global distributions, and identify more differentially expressed proteins than the simple application of a LRT. Additionally, simulations also indicate that the probability of incorrectly identifying a protein as differentially expressed (i.e., the False Discovery Rate) is very low. The source code is available at https://github.com/stevenhwu/BIDE-2D.

Prediction of preeclampsia and delivery of small for gestational age babies based on a combination of clinical risk factors in high-risk women.

OBJECTIVE: To develop clinical risk tools for preeclampsia and small for gestational age (SGA) in high-risk women. METHODS: Individual risk scores based on clinical risk factors were calculated using logistic regression and validated in 1687 women with obesity in first pregnancy, chronic hypertension, or previous preeclampsia. RESULTS: The risk of preeclampsia varied from 7% in obese primiparae without hypertension to 30% when previous preeclampsia and chronic hypertension occurred together. A prediction model incorporating these risk factors had a sensitivity of 48 and 89% for preeclampsia delivered <34 weeks' gestation. CONCLUSION: Multiple clinical risk factors increase the risk of preeclampsia and SGA.

A statistical model to identify differentially expressed proteins in 2D PAGE gels.

Two dimensional polyacrylamide gel electrophoresis (2D PAGE) is used to identify differentially expressed proteins and may be applied to biomarker discovery. A limitation of this approach is the inability to detect a protein when its concentration falls below the limit of detection. Consequently, differential expression of proteins may be missed when the level of a protein in the cases or controls is below the limit of detection for 2D PAGE. Standard statistical techniques have difficulty dealing with undetected proteins. To address this issue, we propose a mixture model that takes into account both detected and non-detected proteins. Non-detected proteins are classified either as (a) proteins that are not expressed in at least one replicate, or (b) proteins that are expressed but are below the limit of detection. We obtain maximum likelihood estimates of the parameters of the mixture model, including the group-specific probability of expression and mean expression intensities. Differentially expressed proteins can be detected by using a Likelihood Ratio Test (LRT). Our simulation results, using data generated from biological experiments, show that the likelihood model has higher statistical power than standard statistical approaches to detect differentially expressed proteins. An R package, Slider (Statistical Likelihood model for Identifying Differential Expression in R), is freely available at http://www.cebl.auckland.ac.nz/slider.php.

An altered pattern of circulating apolipoprotein E3 isoforms is implicated in preeclampsia.

Preeclampsia is a common pregnancy complication that is an important cause of preterm birth and fetal growth restriction. Because there is no diagnostic test yet available for preeclampsia, we used a proteomic approach to identify novel serum/plasma biomarkers for this condition. We conducted case control studies comparing nulliparous women who developed preeclampsia at 36-38 weeks of gestation with healthy nulliparous women matched by gestational age at sampling. Serum/plasma was depleted of six abundant proteins and analyzed by two-dimensional gel electrophoresis (n = 12 per group) and difference gel electrophoresis (n = 12 per group). Differences in abundance of protein spots were detected by univariate and multivariate statistical analyses. Proteins were identified by mass spectrometry and expression of selected proteins was validated by immunoblotting. Proteins whose concentrations were selectively associated with preeclampsia included apolipoprotein E (apoE), apoC-II, complement factor C3c, fibrinogen, transthyretin, and complement factor H-related protein 2. An increase in a deglycosylated isoform of apoE3 and concomitantly decreased amounts of one apoE3 glycoisoform were identified in preeclamptic plasma and confirmed by immunoblotting. Altered production of these preeclampsia-related apoE3 isoforms might impair reverse cholesterol transport, contributing to arterial damage. These findings point to a novel mechanistic link between preeclampsia and subsequent cardiovascular disease.