Optimizing insect metabarcoding using replicated mock communities.

1: Metabarcoding (high-throughput sequencing of marker gene amplicons) has emerged as a promising and cost-effective method for characterizing insect community samples. Yet, the methodology varies greatly among studies and its performance has not been systematically evaluated to date. In particular, it is unclear how accurately metabarcoding can resolve species communities in terms of presence-absence, abundances, and biomass. 2: Here we use mock community experiments and a simple probabilistic model to evaluate the effect of different DNA extraction protocols on metabarcoding performance. Specifically, we ask four questions: (Q1) How consistent are the recovered community profiles across replicate mock communities?; (Q2) How does the choice of lysis buffer affect the recovery of the original community?; (Q3) How are community estimates affected by differing lysis times and homogenization?; and (Q4) Is it possible to obtain adequate species abundance estimates through the use of biological spike-ins? 3: We show that estimates are quite variable across community replicates. In general, a mild lysis protocol is better at reconstructing species lists and approximate counts, while homogenization is better at retrieving biomass composition. Small insects are more likely to be detected in lysates, while some tough species require homogenization to be detected. Results are less consistent across biological replicates for lysates than for homogenates. Some species are associated with strong PCR amplification bias, which complicates the reconstruction of species counts. Yet, with adequate spike-in data, species abundance can be determined with roughly 40% standard error for homogenates, and with roughly 50% standard error for lysates, under ideal conditions. In the latter case, however, this often requires species-specific reference data, while spike-in data generalizes better across species for homogenates. 4: We conclude that a non-destructive, mild lysis approach shows the highest promise for presence/absence description of the community, while also allowing future morphological or molecular work on the material. However, homogenization protocols perform better for characterizing community composition, in particular in terms of biomass.

Bacterial-induced calcium oscillations are common to nitrogen-fixing associations of nodulating legumes and nonlegumes.

Plants that form root-nodule symbioses are within a monophyletic 'nitrogen-fixing' clade and associated signalling processes are shared with the arbuscular mycorrhizal symbiosis. Central to symbiotic signalling are nuclear-associated oscillations in calcium ions (Ca(2+) ), occurring in the root hairs of several legume species in response to the rhizobial Nod factor signal. In this study we expanded the species analysed for activation of Ca(2+) oscillations, including nonleguminous species within the nitrogen-fixing clade. We showed that Ca(2+) oscillations are a common feature of legumes in their association with rhizobia, while Cercis, a non-nodulating legume, does not show Ca(2+) oscillations in response to Nod factors from Sinorhizobium fredii NGR234. Parasponia andersonii, a nonlegume that can associate with rhizobia, showed Nod factor-induced calcium oscillations to S. fredii NGR234 Nod factors, but its non-nodulating sister species, Trema tomentosa, did not. Also within the nitrogen-fixing clade are actinorhizal species that associate with Frankia bacteria and we showed that Alnus glutinosa induces Ca(2+) oscillations in root hairs in response to exudates from Frankia alni, but not to S. fredii NGR234 Nod factors. We conclude that the ability to mount Ca(2+) oscillations in response to symbiotic bacteria is a common feature of nodulating species within the nitrogen-fixing clade.

Activation of symbiosis signaling by arbuscular mycorrhizal fungi in legumes and rice.

Establishment of arbuscular mycorrhizal interactions involves plant recognition of diffusible signals from the fungus, including lipochitooligosaccharides (LCOs) and chitooligosaccharides (COs). Nitrogen-fixing rhizobial bacteria that associate with leguminous plants also signal to their hosts via LCOs, the so-called Nod factors. Here, we have assessed the induction of symbiotic signaling by the arbuscular mycorrhizal (Myc) fungal-produced LCOs and COs in legumes and rice (Oryza sativa). We show that Myc-LCOs and tetra-acetyl chitotetraose (CO4) activate the common symbiosis signaling pathway, with resultant calcium oscillations in root epidermal cells of Medicago truncatula and Lotus japonicus. The nature of the calcium oscillations is similar for LCOs produced by rhizobial bacteria and by mycorrhizal fungi; however, Myc-LCOs activate distinct gene expression. Calcium oscillations were activated in rice atrichoblasts by CO4, but not the Myc-LCOs, whereas a mix of CO4 and Myc-LCOs activated calcium oscillations in rice trichoblasts. In contrast, stimulation of lateral root emergence occurred following treatment with Myc-LCOs, but not CO4, in M. truncatula, whereas both Myc-LCOs and CO4 were active in rice. Our work indicates that legumes and non-legumes differ in their perception of Myc-LCO and CO signals, suggesting that different plant species respond to different components in the mix of signals produced by arbuscular mycorrhizal fungi.

Endopolyploidy as a potential alternative adaptive strategy for Arabidopsis leaf size variation in response to UV-B.

The extent of endoreduplication in leaf growth is group- or even species-specific, and its adaptive role is still unclear. A survey of Arabidopsis accessions for variation at the level of endopolyploidy, cell number, and cell size in leaves revealed extensive genetic variation in endopolyploidy level. High endopolyploidy is associated with increased leaf size, both in natural and in genetically unstructured (mapping) populations. The underlying genes were identified as quantitative trait loci that control endopolyploidy in nature by modulating the progression of successive endocycles during organ development. This complex genetic architecture indicates an adaptive mechanism that allows differential organ growth over a broad geographic range and under stressful environmental conditions. UV-B radiation was identified as a significant positive climatic predictor for high endopolyploidy. Arabidopsis accessions carrying the increasing alleles for endopolyploidy also have enhanced tolerance to UV-B radiation. UV-absorbing secondary metabolites provide an additional protective strategy in accessions that display low endopolyploidy. Taken together, these results demonstrate that high constitutive endopolyploidy is a significant predictor for organ size in natural populations and is likely to contribute to sustaining plant growth under high incident UV radiation. Endopolyploidy may therefore form part of the range of UV-B tolerance mechanisms that exist in natural populations.

The role of DMI1 in establishing Ca (2+) oscillations in legume symbioses.

Calcium (Ca (2+)) is a key secondary messenger in many plant signaling pathways. One such pathway is the SYM pathway, required in the establishment of both arbuscular mycorrhizal and rhizobial root symbioses with legume host plants. (1) When the host plant has perceived the diffusible signals from the microbial symbionts, one of the earliest physiological responses are Ca (2+) oscillations in and around the nucleus. (2) These oscillations are essential for activating downstream gene expression, but the precise mechanisms of encoding and decoding the Ca (2+) signals are unclear and still under intense investigation. Here we put forward a hypothesis for the mechanism of the cation channel DMI1.

Buffering capacity explains signal variation in symbiotic calcium oscillations.

Legumes form symbioses with rhizobial bacteria and arbuscular mycorrhizal fungi that aid plant nutrition. A critical component in the establishment of these symbioses is nuclear-localized calcium (Ca(2+)) oscillations. Different components on the nuclear envelope have been identified as being required for the generation of the Ca(2+) oscillations. Among these an ion channel, Doesn't Make Infections1, is preferentially localized on the inner nuclear envelope and a Ca(2+) ATPase is localized on both the inner and outer nuclear envelopes. Doesn't Make Infections1 is conserved across plants and has a weak but broad similarity to bacterial potassium channels. A possible role for this cation channel could be hyperpolarization of the nuclear envelope to counterbalance the charge caused by the influx of Ca(2+) into the nucleus. Ca(2+) channels and Ca(2+) pumps are needed for the release and reuptake of Ca(2+) from the internal store, which is hypothesized to be the nuclear envelope lumen and endoplasmic reticulum, but the release mechanism of Ca(2+) remains to be identified and characterized. Here, we develop a mathematical model based on these components to describe the observed symbiotic Ca(2+) oscillations. This model can recapitulate Ca(2+) oscillations, and with the inclusion of Ca(2+)-binding proteins it offers a simple explanation for several previously unexplained phenomena. These include long periods of frequency variation, changes in spike shape, and the initiation and termination of oscillations. The model also predicts that an increase in buffering capacity in the nucleoplasm would cause a period of rapid oscillations. This phenomenon was observed experimentally by adding more of the inducing signal.

BaSAR-A tool in R for frequency detection.

Many biological processes are periodic, for example cell cycle expression, circadian rhythms and calcium oscillations. However, measured time series from these processes are commonly short and noisy, and finding frequencies in such data can be challenging. Here we present BaSAR, Bayesian Spectrum Analysis in R, a package for extracting frequency information from time series data. The software uses advanced techniques of Bayesian inference that are well suited for handling typical biological data. The core functions are designed for detecting a single key frequency, without the need for data pre-processing such as detrending. The package is freely available at CRAN - The Comprehensive R Archive Network: http://cran.r-project.org/web/packages/BaSAR.

Automated Bayesian model development for frequency detection in biological time series.

BACKGROUND: A first step in building a mathematical model of a biological system is often the analysis of the temporal behaviour of key quantities. Mathematical relationships between the time and frequency domain, such as Fourier Transforms and wavelets, are commonly used to extract information about the underlying signal from a given time series. This one-to-one mapping from time points to frequencies inherently assumes that both domains contain the complete knowledge of the system. However, for truncated, noisy time series with background trends this unique mapping breaks down and the question reduces to an inference problem of identifying the most probable frequencies. RESULTS: In this paper we build on the method of Bayesian Spectrum Analysis and demonstrate its advantages over conventional methods by applying it to a number of test cases, including two types of biological time series. Firstly, oscillations of calcium in plant root cells in response to microbial symbionts are non-stationary and noisy, posing challenges to data analysis. Secondly, circadian rhythms in gene expression measured over only two cycles highlights the problem of time series with limited length. The results show that the Bayesian frequency detection approach can provide useful results in specific areas where Fourier analysis can be uninformative or misleading. We demonstrate further benefits of the Bayesian approach for time series analysis, such as direct comparison of different hypotheses, inherent estimation of noise levels and parameter precision, and a flexible framework for modelling the data without pre-processing. CONCLUSIONS: Modelling in systems biology often builds on the study of time-dependent phenomena. Fourier Transforms are a convenient tool for analysing the frequency domain of time series. However, there are well-known limitations of this method, such as the introduction of spurious frequencies when handling short and noisy time series, and the requirement for uniformly sampled data. Biological time series often deviate significantly from the requirements of optimality for Fourier transformation. In this paper we present an alternative approach based on Bayesian inference. We show the value of placing spectral analysis in the framework of Bayesian inference and demonstrate how model comparison can automate this procedure.