Identification of neurotoxic compounds in cyanobacteria exudate mixtures.

Release of toxic cyanobacterial secondary metabolites threatens biosecurity, foodwebs and public health. Microcystis aeruginosa (Ma), the dominant species in global freshwater cyanobacterial blooms, produces exudates (MaE) that cause adverse outcomes including nerve damage. Previously, we identified > 300 chemicals in MaE. It is critical to investigate neurotoxicity mechanisms of active substances among this suite of Ma compounds. Here, we screened 103 neurotoxicity assays from the ToxCast database to reveal targets of action of MaE using machine learning. We then built a potential Adverse Outcome Pathway (AOP) to identify neurotoxicity mechanisms of MaE as well as key targets. Finally, we selected potential neurotoxins matched with those targets using molecular docking. We found 38 targets that were inhibited and eight targets that were activated, collectively mainly related to neurotransmission (i.e. cholinergic, dopaminergic and serotonergic neurotransmitter systems). The potential AOP of MaE neurotoxicity could be caused by blocking calcium voltage-gated channel (CACNA1A), because of antagonizing neurotransmitter receptors, or because of inhibiting solute carrier transporters. We identified nine neurotoxic MaE compounds with high affinity to those targets, including LysoPC(16:0), 2-acetyl-1-alkyl-sn-glycero-3-phosphocholine, egonol glucoside, polyoxyethylene (600) monoricinoleate, and phytosphingosine. Our study enhances understanding of neurotoxicity mechanisms and identifies neurotoxins in cyanobacterial bloom exudates, which may help identify priority compounds for cyanobacteria management.

A comparison of stable and fluctuating resources with respect to evolutionary adaptation and life-history traits using individual-based modeling and machine learning.

There are three non-mutually-exclusive key strategies evolved by gene pools to cope with fluctuating food resource availability, including evolutionary adaptation, phenotypic plasticity, and migration. We focus primarily on evolutionary adaptation and behavioral plasticity, which is a type of phenotypic plasticity, resulting in life-history changes as ways of dealing with fluctuations in food resource availability. Using EcoSim, a predator-prey individual-based model, we compare individuals with stable food resources with those in environments where there are fluctuating food resources in terms of adaptation through behavioral plasticity and evolution. The purpose of our study is to determine whether evolution and behavioral plasticity truly play a role in adapting to an environment with fluctuating food resources, as well as to determine whether there are specific gene divergences between gene pools in fluctuating food resource environments versus gene pools where food resources are relatively stable. An important result of our study is that individuals in environments that are unstable with respect to food resource availability exhibited significant differences in behaviors versus those in environments with stable food resources. Although behavioral plasticity facilitates a rapid response to unstable food conditions, our study revealed the evolution of perceptual traits such as vision range in reaction to fluctuating food resources, indicating the importance of evolution in adapting to unstable resource environments in the long run. Moreover, using decision trees, we found that there were significant behavioral gene divergences between individuals in environments with fluctuating food resources as opposed to individuals in environments with stable food resources.

Optimization and performance testing of a sequence processing pipeline applied to detection of nonindigenous species.

Genetic taxonomic assignment can be more sensitive than morphological taxonomic assignment, particularly for small, cryptic or rare species. Sequence processing is essential to taxonomic assignment, but can also produce errors because optimal parameters are not known a priori. Here, we explored how sequence processing parameters influence taxonomic assignment of 18S sequences from bulk zooplankton samples produced by 454 pyrosequencing. We optimized a sequence processing pipeline for two common research goals, estimation of species richness and early detection of aquatic invasive species (AIS), and then tested most optimal models' performances through simulations. We tested 1,050 parameter sets on 18S sequences from 20 AIS to determine optimal parameters for each research goal. We tested optimized pipelines' performances (detectability and sensitivity) by computationally inoculating sequences of 20 AIS into ten bulk zooplankton samples from ports across Canada. We found that optimal parameter selection generally depends on the research goal. However, regardless of research goal, we found that metazoan 18S sequences produced by 454 pyrosequencing should be trimmed to 375-400 bp and sequence quality filtering should be relaxed (1.5 ≤ maximum expected error ≤ 3.0, Phred score = 10). Clustering and denoising were only viable for estimating species richness, because these processing steps made some species undetectable at low sequence abundances which would not be useful for early detection of AIS. With parameter sets optimized for early detection of AIS, 90% of AIS were detected with fewer than 11 target sequences, regardless of whether clustering or denoising was used. Despite developments in next-generation sequencing, sequence processing remains an important issue owing to difficulties in balancing false-positive and false-negative errors in metabarcoding data.

Speciation without Pre-Defined Fitness Functions.

The forces promoting and constraining speciation are often studied in theoretical models because the process is hard to observe, replicate, and manipulate in real organisms. Most models analyzed to date include pre-defined functions influencing fitness, leaving open the question of how speciation might proceed without these built-in determinants. To consider the process of speciation without pre-defined functions, we employ the individual-based ecosystem simulation platform EcoSim. The environment is initially uniform across space, and an evolving behavioural model then determines how prey consume resources and how predators consume prey. Simulations including natural selection (i.e., an evolving behavioural model that influences survival and reproduction) frequently led to strong and distinct phenotypic/genotypic clusters between which hybridization was low. This speciation was the result of divergence between spatially-localized clusters in the behavioural model, an emergent property of evolving ecological interactions. By contrast, simulations without natural selection (i.e., behavioural model turned off) but with spatial isolation (i.e., limited dispersal) produced weaker and overlapping clusters. Simulations without natural selection or spatial isolation (i.e., behaviour model turned off and high dispersal) did not generate clusters. These results confirm the role of natural selection in speciation by showing its importance even in the absence of pre-defined fitness functions.

Can we predict the unpredictable?

Time series forecasting is of fundamental importance for a variety of domains including the prediction of earthquakes, financial market prediction, and the prediction of epileptic seizures. We present an original approach that brings a novel perspective to the field of long-term time series forecasting. Nonlinear properties of a time series are evaluated and used for long-term predictions. We used financial time series, medical time series and climate time series to evaluate our method. The results we obtained show that the long-term prediction of complex nonlinear time series is no longer unrealistic. The new method has the ability to predict the long-term evolutionary trend of stock market time series, and it attained an accuracy level with 100% sensitivity and specificity for the prediction of epileptic seizures up to 17 minutes in advance based on data from 21 epileptic patients. Our new method also predicted the trend of increasing global temperature in the last 30 years with a high level of accuracy. Thus, our method for making long-term time series predictions is vastly superior to existing methods. We therefore believe that our proposed method has the potential to be applied to many other domains to generate accurate and useful long-term predictions.

A new species abundance distribution model based on model combination.

Species abundance distribution (SAD) is one of the important measures of biodiversity and one of the most significant concepts in ecology communities. Using this concept, the biologists can infer a lot of information from their collected data. In this article, we proposed a new method for predicting SAD. This method is based on the combination of several measures parameterized by machine learning techniques and decomposition of the model in sub-ranges having their proper combination. The goal is to use the combination of several individual models to design a better and more informative model. We show in this article by using many datasets representing different ecological situations that our new method is more robust and outperforms the predictive capacity of the other existing models.

Speciation with gene flow in a heterogeneous virtual world: can physical obstacles accelerate speciation?

The origin of species remains one of the most controversial and least understood topics in evolution. While it is being widely accepted that complete cessation of gene-flow between populations owing to long-lasting geographical barriers results in a steady, irreversible increase of divergence and eventually speciation, the extent to which various degrees of habitat heterogeneity influences speciation rates is less well understood. Here, we investigate how small, randomly distributed physical obstacles influence the distribution of populations and species, the level of population connectivity (e.g. gene flow), as well as the mode and tempo of speciation in a virtual ecosystem composed of prey and predator species. We adapted an existing individual-based platform, EcoSim, to allow fine tuning of the gene flow's level between populations by adding various numbers of obstacles in the world. The platform implements a simple food chain consisting of primary producers, herbivores (prey) and predators. It allows complex intra- and inter-specific interactions, based on individual evolving behavioural models, as well as complex predator-prey dynamics and coevolution in spatially homogenous and heterogeneous worlds. We observed a direct and continuous increase in the speed of evolution (e.g. the rate of speciation) with the increasing number of obstacles in the world. The spatial distribution of species was also more compact in the world with obstacles than in the world without obstacles. Our results suggest that environmental heterogeneity and other factors affecting demographic stochasticity can directly influence speciation and extinction rates.

43 genes support the lungfish-coelacanth grouping related to the closest living relative of tetrapods with the Bayesian method under the coalescence model.

BACKGROUND: Since the discovery of the "living fossil" in 1938, the coelacanth (Latimeria chalumnae) has generally been considered to be the closest living relative of the land vertebrates, and this is still the prevailing opinion in most general biology textbooks. However, the origin of tetrapods has not been resolved for decades. Three principal hypotheses (lungfish-tetrapod, coelacanth-tetrapod, or lungfish-coelacanth sister group) have been proposed. FINDINGS: We used the Bayesian method under the coalescence model with the latest published program (Bayesian Estimation of Species Trees, or BEST) to perform a phylogenetic analysis for seven relevant taxa and 43 nuclear protein-coding genes with the jackknife method for taxon sub-sampling. The lungfish-coelacanth sister group was consistently reconstructed with the Bayesian method under the coalescence model in 17 out of 21 taxon sets with a Bayesian posterior probability as high as 99%. Lungfish-tetrapod was only inferred from BCLS and BACLS. Neither coelacanth-tetrapod nor lungfish-coelacanth-tetrapod was recovered out of all 21 taxon sets. CONCLUSIONS: Our results provide strong evidence in favor of accepting the hypothesis that lungfishes and coelacanths form a monophyletic sister-group that is the closest living relative of tetrapods. This clade was supported by high Bayesian posterior probabilities of the branch (a lungfish-coelacanth clade) and high taxon jackknife supports.

Genome-wide EST data mining approaches to resolving incongruence of molecular phylogenies.

Thirty-six single genes of 6 plants inferred 18 unique trees using maximum parsimony. Such incongruence is an important challenge. How to reconstruct the congruent tree is still one of the most challenges in molecular phylogenetics. For resolving this problem, a genome-wide EST data mining approach was systematically investigated by retrieving a large size of EST data of 144 shared genes of 6 green plants from GenBank. The results show that the concatenated alignments approach overcame incongruence among single-gene phylogenies and successfully reconstructed the congruent tree of 6 species with 100% jackknife support across each branch when 144 genes was used. Jackknife supports of correct branches increased with number of genes linearly, but the number of wrong branches also increased linearly. For inferring the congruent tree, a minimum of 30 genes were required. This approach may provide potential power in resolving conflictions of phylogenies.

Regularity analysis of an individual-based ecosystem simulation.

We analyze the results of a large simulation of an evolving ecosystem to evaluate its complexity. In particular, we are interested to know how close to a stochastic or a deterministic behavior our simulation is. Four methods have been used for this analysis: Higuchi fractal dimension, correlation dimension, largest Lyapunov exponent, and P&H method. Besides, we use a surrogate data test to reach a final decision about analysis. As we expect, our results show that there is a deterministic and chaotic behavior in ecosystem simulation.

An individual-based evolving predator-prey ecosystem simulation using a fuzzy cognitive map as the behavior model.

We present an individual-based predator-prey model with, for the first time, each agent behavior being modeled by a fuzzy cognitive map (FCM), allowing the evolution of the agent behavior through the epochs of the simulation. The FCM enables the agent to evaluate its environment (e.g., distance to predator or prey, distance to potential breeding partner, distance to food, energy level) and its internal states (e.g., fear, hunger, curiosity), and to choose several possible actions such as evasion, eating, or breeding. The FCM of each individual is unique and is the result of the evolutionary process. The notion of species is also implemented in such a way that species emerge from the evolving population of agents. To our knowledge, our system is the only one that allows the modeling of links between behavior patterns and speciation. The simulation produces a lot of data, including number of individuals, level of energy by individual, choice of action, age of the individuals, and average FCM associated with each species. This study investigates patterns of macroevolutionary processes, such as the emergence of species in a simulated ecosystem, and proposes a general framework for the study of specific ecological problems such as invasive species and species diversity patterns. We present promising results showing coherent behaviors of the whole simulation with the emergence of strong correlation patterns also observed in existing ecosystems.

Analysis of proteomes using the molecular scanner.

INTRODUCTIONThe molecular scanner offers a flexible and powerful visualization tool that can create a fully annotated 2D gel electrophoresis map. Proteins separated by 2D gel electrophoresis are simultaneously digested while undergoing electrotransfer from the gel to a membrane. The peptides are subjected to peptide mass fingerprint (PMF) analysis to identify proteins directly from the PVDF membranes by MALDI-TOF-MS scanning. An ensemble of dedicated tools is then used to create, analyze, and visualize a proteome as a multidimensional image. The molecular scanner method reduces to a minimum the sample handling prior to mass analysis and decreases the sample size to a few tens of micrometers, that is, the size of the MALDI-TOF-MS laser beam impact. The process can be divided into four parts: separation and digestion of proteins, acquisition of PMF data, processing of the MS data and protein identification, and creation of multidimensional proteome maps.

Conserved transcription factor binding sites of cancer markers derived from primary lung adenocarcinoma microarrays.

Gene transcription in a set of 49 human primary lung adenocarcinomas and 9 normal lung tissue samples was examined using Affymetrix GeneChip technology. A total of 3442 genes, called the set M AD, were found to be either up- or down-regulated by at least 2-fold between the two phenotypes. Genes assigned to a particular gene ontology term were found, in many cases, to be significantly unevenly distributed between the genes in and outside M AD. Terms that were overrepresented in M AD included functions directly implicated in the cancer cell metabolism. Based on their functional roles and expression profiles, genes in M AD were grouped into likely co-regulated gene sets. Highly conserved sequences in the 5 kb region upstream of the genes in these sets were identified with the motif discovery tool, MoDEL. Potential oncogenic transcription factors and their corresponding binding sites were identified in these conserved regions using the TRANSFAC 8.3 database. Several of the transcription factors identified in this study have been shown elsewhere to be involved in oncogenic processes. This study searched beyond phenotypic gene expression profiles in cancer cells, in order to identify the more important regulatory transcription factors that caused these aberrations in gene expression.

MoDEL: an efficient strategy for ungapped local multiple alignment.

We introduce a method for ungapped local multiple alignment (ULMA) in a given set of amino acid or nucleotide sequences. This method explores two search spaces using a linked optimization strategy. The first search space M consists of all possible words of a given length W, defined on the residue alphabet. An evolutionary algorithm searches this space globally. The second search space P consists of all possible ULMAs in the sequence set, each ULMA being represented by a position vector defining exactly one subsequence of length W per sequence. This search space is sampled with hill-climbing processes. The search of both spaces are coupled by projecting high scoring results from the global evolutionary search of M onto P. The hill-climbing processes then refine the optimization by local search, using the relative entropy between the ULMA and background residue frequencies as an objective function. We demonstrate some advantages of our strategy by analyzing difficult natural amino acid sequences and artificial datasets. A web interface is available at

Popitam: towards new heuristic strategies to improve protein identification from tandem mass spectrometry data.

In recent years, proteomics research has gained importance due to increasingly powerful techniques in protein purification, mass spectrometry and identification, and due to the development of extensive protein and DNA databases from various organisms. Nevertheless, current identification methods from spectrometric data have difficulties in handling modifications or mutations in the source peptide. Moreover, they have low performance when run on large databases (such as genomic databases), or with low quality data, for example due to bad calibration or low fragmentation of the source peptide. We present a new algorithm dedicated to automated protein identification from tandem mass spectrometry (MS/MS) data by searching a peptide sequence database. Our identification approach shows promising properties for solving the specific difficulties enumerated above. It consists of matching theoretical peptide sequences issued from a database with a structured representation of the source MS/MS spectrum. The representation is similar to the spectrum graphs commonly used by de novo sequencing software. The identification process involves the parsing of the graph in order to emphasize relevant sections for each theoretical sequence, and leads to a list of peptides ranked by a correlation score. The parsing of the graph, which can be a highly combinatorial task, is performed by a bio-inspired algorithm called Ant Colony Optimization algorithm.

Molecular scanner experiment with human plasma: improving protein identification by using intensity distributions of matching peptide masses.

The development of high throughput utilities to identify proteins is a major challenge in present research in the field of proteomics. One such utility, the molecular scanner, uses proteins separated by two-dimensional polyacrylamide gel electrophoresis that are digested in the gel and during transfer onto a collecting membrane. After adding a matrix, the membrane is inserted into a matrix-assisted laser desorption/ionization-time of flight mass spectrometer and a peptide mass fingerprint (PMF) is measured for every scanned site. Since the spacing between scanned sites is much smaller than the size of the most abundant protein spots, there is a certain redundancy in the data that was used in an earlier experiment with Escherichia coli [1] to improve mass calibration and PMF identification results. It was observed that the signal intensity of a peptide mass as a function of the position on the membrane showed similar patterns if peptides stemmed from the same protein. Taking account of these similarities a clustering algorithm was used to find lists of experimental masses with similar intensity distributions, which provided clearer identification of the corresponding proteins. Here, these methods are applied to a human plasma scan, where proteins were highly modified and less separated. The presence of very abundant proteins like albumin and immunoglobulins added another difficulty. The calibration of the initial PMFs was not satisfactory and masses had to be recalibrated. After discarding chemical noise, the membrane was partitioned into regions and for each region protein identification was carried out separately. A new scoring method was used, where the PMF score was multiplied by a factor that measures the similarity of matching peptides. This method proved to be more robust than the method developed in [1] if the region where a protein was found had an extended, nonspherical shape and strong overlap with regions of other proteins. Many proteins annotated on the SWISS-2D PAGE human plasma master gel could be clearly identified and many interesting properties were observed.

Visualization and analysis of molecular scanner peptide mass spectra.

The molecular scanner combines protein separation using gel electrophoresis with peptide mass fingerprinting (PMF) techniques to identify proteins in a highly automated manner. Proteins separated in a 2-dimensional polyacrylamide gel (2-D PAGE) are digested in parallel and transferred onto a membrane keeping their relative positions. The membrane is then sprayed with a matrix and inserted into a matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometer, which measures a peptide mass fingerprint at each site on the scanned grid. First, visualization of PMF data allows surveying all fingerprints at once and provides very useful information on the presence of chemical noise. Chemical noise is shown to be a potential source for erroneous identifications and is therefore purged from the mass fingerprints. Then, the correlation between neighboring spectra is used to recalibrate the peptide masses. Finally, a method that clusters peptide masses according to the similarity of the spatial distributions of their signal intensities is presented. This method allows discarding many of the false positives that usually go along with PMF identifications and allows identifying many weakly expressed proteins present in the gel.