Foreseeing the future of mutualistic communities beyond collapse

International audienc

How models can support ecosystem-based management of coral reefs

Despite the importance of coral reef ecosystems to the social and economic welfare of coastal communities, the condition of these marine ecosystems have generally degraded over the past decades. With an increased knowledge of coral reef ecosystem processes and a rise in computer power, dynamic models are useful tools in assessing the synergistic effects of local and global stressors on ecosystem functions. We review representative approaches for dynamically modeling coral reef ecosystems and categorize them as minimal, intermediate and complex models. The categorization was based on the leading principle for model development and their level of realism and process detail. This review aims to improve the knowledge of concurrent approaches in coral reef ecosystem modeling and highlights the importance of choosing an appropriate approach based on the type of question(s) to be answered. We contend that minimal and intermediate models are generally valuable tools to assess the response of key states to main stressors and, hence, contribute to understanding ecological surprises. As has been shown in freshwater resources management, insight into these conceptual relations profoundly influences how natural resource managers perceive their systems and how they manage ecosystem recovery. We argue that adaptive resource management requires integrated thinking and decision support, which demands a diversity of modeling approaches. Integration can be achieved through complimentary use of models or through integrated models that systemically combine all relevant aspects in one model. Such whole-of-system models can be useful tools for quantitatively evaluating scenarios. These models allow an assessment of the interactive effects of multiple stressors on various, potentially conflicting, management objectives. All models simplify reality and, as such, have their weaknesses. While minimal models lack multidimensionality, system models are likely difficult to interpret as they require many efforts to decipher the numerous interactions and feedback loops. Given the breadth of questions to be tackled when dealing with coral reefs, the best practice approach uses multiple model types and thus benefits from the strength of different models types

Spinocerebellar Ataxia Type 23: A Genetic Update

The spinocerebellar ataxia type 23 locus was identified in 2004 based on linkage analysis in a large, two-generation Dutch family. The age of onset ranged 43–56 years and the phenotype was characterized by a slowly progressive, isolated ataxia. Neuropathological examination revealed neuronal loss in the Purkinje cell layer, dentate nuclei, and inferior olives. Ubiquitin-positive intranuclear inclusions were found in nigral neurons, but were considered to be Marinesco bodies. The disease locus on chromosome 20p13-12.3 was found to span a region of approximately 6 Mb of genomic DNA, containing 97 known or predicted genes. To date, no other families have been described that also map to this SCA locus. Direct sequencing of the coding regions of 21 prioritized candidate genes did not reveal any disease-causing mutation. Apparently, the SCA23 gene is a disease gene with a different function than the genes that have been associated with other known SCA types. Work to elucidate the chromosomal organization of the SCA23 locus will eventually discover the responsible disease gene

Validating module network learning algorithms using simulated data

In recent years, several authors have used probabilistic graphical models to learn expression modules and their regulatory programs from gene expression data. Here, we demonstrate the use of the synthetic data generator SynTReN for the purpose of testing and comparing module network learning algorithms. We introduce a software package for learning module networks, called LeMoNe, which incorporates a novel strategy for learning regulatory programs. Novelties include the use of a bottom-up Bayesian hierarchical clustering to construct the regulatory programs, and the use of a conditional entropy measure to assign regulators to the regulation program nodes. Using SynTReN data, we test the performance of LeMoNe in a completely controlled situation and assess the effect of the methodological changes we made with respect to an existing software package, namely Genomica. Additionally, we assess the effect of various parameters, such as the size of the data set and the amount of noise, on the inference performance. Overall, application of Genomica and LeMoNe to simulated data sets gave comparable results. However, LeMoNe offers some advantages, one of them being that the learning process is considerably faster for larger data sets. Additionally, we show that the location of the regulators in the LeMoNe regulation programs and their conditional entropy may be used to prioritize regulators for functional validation, and that the combination of the bottom-up clustering strategy with the conditional entropy-based assignment of regulators improves the handling of missing or hidden regulators.Comment: 13 pages, 6 figures + 2 pages, 2 figures supplementary informatio

Critical slowing down as early warning for the onset and termination of depression

About 17% of humanity goes through an episode of major depression at some point in their lifetime. Despite the enormous societal costs of this incapacitating disorder, it is largely unknown how the likelihood of falling into a depressive episode can be assessed. Here, we show for a large group of healthy individuals and patients that the probability of an upcoming shift between a depressed and a normal state is related to elevated temporal autocorrelation, variance, and correlation between emotions in fluctuations of autorecorded emotions. These are indicators of the general phenomenon of critical slowing down, which is expected to occur when a system approaches a tipping point. Our results support the hypothesis that mood may have alternative stable states separated by tipping points, and suggest an approach for assessing the likelihood of transitions into and out of depression

Microbial catabolic activities are naturally selected by metabolic energy harvest rate

The fundamental trade-off between yield and rate of energy harvest per unit of substrate has been largely discussed as a main characteristic for microbial established cooperation or competition. In this study, this point is addressed by developing a generalized model that simulates competition between existing and not experimentally reported microbial catabolic activities defined only based on well-known biochemical pathways. No specific microbial physiological adaptations are considered, growth yield is calculated coupled to catabolism energetics and a common maximum biomass-specific catabolism rate (expressed as electron transfer rate) is assumed for all microbial groups. Under this approach, successful microbial metabolisms are predicted in line with experimental observations under the hypothesis of maximum energy harvest rate. Two microbial ecosystems, typically found in wastewater treatment plants, are simulated, namely: (i) the anaerobic fermentation of glucose and (ii) the oxidation and reduction of nitrogen under aerobic autotrophic (nitrification) and anoxic heterotrophic and autotrophic (denitrification) conditions. The experimentally observed cross feeding in glucose fermentation, through multiple intermediate fermentation pathways, towards ultimately methane and carbon dioxide is predicted. Analogously, two-stage nitrification (by ammonium and nitrite oxidizers) is predicted as prevailing over nitrification in one stage. Conversely, denitrification is predicted in one stage (by denitrifiers) as well as anammox (anaerobic ammonium oxidation). The model results suggest that these observations are a direct consequence of the different energy yields per electron transferred at the different steps of the pathways. Overall, our results theoretically support the hypothesis that successful microbial catabolic activities are selected by an overall maximum energy harvest rate

A duplication at chromosome 11q12.2–11q12.3 is associated with spinocerebellar ataxia type 20

Spinocerebellar ataxia type 20 (SCA20) has been linked to chromosome 11q12, but the underlying genetic defect has yet to be identified. We applied single-nucleotide polymorphism genotyping to detect structural alterations in the genomic DNA of patients with SCA20. We found a 260 kb duplication within the previously linked SCA20 region, which was confirmed by quantitative polymerase chain reaction and fiber fluorescence in situ hybridization, the latter also showing its direct orientation. The duplication spans 10 known and 2 unknown genes, and is present in all affected individuals in the single reported SCA20 pedigree. While the mechanism whereby this duplication may be pathogenic remains to be established, we speculate that the critical gene within the duplicated segment may be DAGLA, the product of which is normally present at the base of Purkinje cell dendritic spines and contributes to the modulation of parallel fiber-Purkinje cell synapses

Canine Hereditary Ataxia in Old English Sheepdogs and Gordon Setters Is Associated with a Defect in the Autophagy Gene Encoding RAB24

Old English Sheepdogs and Gordon Setters suffer from a juvenile onset, autosomal recessive form of canine hereditary ataxia primarily affecting the Purkinje neuron of the cerebellar cortex. The clinical and histological characteristics are analogous to hereditary ataxias in humans. Linkage and genome-wide association studies on a cohort of related Old English Sheepdogs identified a region on CFA4 strongly associated with the disease phenotype. Targeted sequence capture and next generation sequencing of the region identified an A to C single nucleotide polymorphism (SNP) located at position 113 in exon 1 of an autophagy gene, RAB24, that segregated with the phenotype. Genotyping of six additional breeds of dogs affected with hereditary ataxia identified the same polymorphism in affected Gordon Setters that segregated perfectly with phenotype. The other breeds tested did not have the polymorphism. Genome-wide SNP genotyping of Gordon Setters identified a 1.9 MB region with an identical haplotype to affected Old English Sheepdogs. Histopathology, immunohistochemistry and ultrastructural evaluation of the brains of affected dogs from both breeds identified dramatic Purkinje neuron loss with axonal spheroids, accumulation of autophagosomes, ubiquitin positive inclusions and a diffuse increase in cytoplasmic neuronal ubiquitin staining. These findings recapitulate the changes reported in mice with induced neuron-specific autophagy defects. Taken together, our results suggest that a defect in RAB24, a gene associated with autophagy, is highly associated with and may contribute to canine hereditary ataxia in Old English Sheepdogs and Gordon Setters. This finding suggests that detailed investigation of autophagy pathways should be undertaken in human hereditary ataxia.American Kennel Club Canine Health Foundation (grant CHF 0407)American Kennel Club Canine Health Foundation (grant CHF 0925)Old English Sheepdog Club of AmericaTarTan Gordon Setter ClubEuropean Science Foundation (EURYI)Canine Health Information Center (DNA Repository