Predicting current and future biological invasions: both native and invaded ranges matter.

The classical approach to predicting the geographical extent of species invasions consists of training models in the native range and projecting them in distinct, potentially invasible areas. However, recent studies have demonstrated that this approach could be hampered by a change of the realized climatic niche, allowing invasive species to spread into habitats in the invaded ranges that are climatically distinct from those occupied in the native range. We propose an alternative approach that involves fitting models with pooled data from all ranges. We show that this pooled approach improves prediction of the extent of invasion of spotted knapweed (Centaurea maculosa) in North America on models based solely on the European native range. Furthermore, it performs equally well on models based on the invaded range, while ensuring the inclusion of areas with similar climate to the European niche, where the species is likely to spread further. We then compare projections from these models for 2080 under a severe climate warming scenario. Projections from the pooled models show fewer areas of intermediate climatic suitability than projections from the native or invaded range models, suggesting a better consensus among modelling techniques and reduced uncertainty.

Predicting global change impacts on plant species' distributions: Future challenges

Given the rate of projected environmental change for the 21st century, urgent adaptation and mitigation measures are required to slow down the on-going erosion of biodiversity. Even though increasing evidence shows that recent human-induced environmental changes have already triggered species' range shifts, changes in phenology and species' extinctions, accurate projections of species' responses to future environmental changes are more difficult to ascertain. This is problematic, since there is a growing awareness of the need to adopt proactive conservation planning measures using forecasts of species' responses to future environmental changes. There is a substantial body of literature describing and assessing the impacts of various scenarios of climate and land-use change on species' distributions. Model predictions include a wide range of assumptions and limitations that are widely acknowledged but compromise their use for developing reliable adaptation and mitigation strategies for biodiversity. Indeed, amongst the most used models, few, if any, explicitly deal with migration processes, the dynamics of population at the "trailing edge" of shifting populations, species' interactions and the interaction between the effects of climate and land-use. In this review, we propose two main avenues to progress the understanding and prediction of the different processes A occurring on the leading and trailing edge of the species' distribution in response to any global change phenomena. Deliberately focusing on plant species, we first explore the different ways to incorporate species' migration in the existing modelling approaches, given data and knowledge limitations and the dual effects of climate and land-use factors. Secondly, we explore the mechanisms and processes happening at the trailing edge of a shifting species' distribution and how to implement them into a modelling approach. We finally conclude this review with clear guidelines on how such modelling improvements will benefit conservation strategies in a changing world. (c) 2007 Rubel Foundation, ETH Zurich. Published by Elsevier GrnbH. All rights reserved.

Apoptosis induced by death receptors.

Death receptors belong to the TNF receptor family and are characterised by an intracellular death domain that serves to recruit adapter proteins such as TRADD and FADD and cysteine proteases such as Caspase-8. Activation of Caspase-8 on the aggregated receptor leads to apoptosis. Triggering of death receptors is mediated through the binding of specific ligands of the TNF family, which are homotrimeric type-2 membrane proteins displaying three receptor binding sites. There are various means of modulating the activation of death receptors. The status of the ligand (membrane-bound vs. soluble) is critical in the activation of Fas and of TRAIL receptors. Cleavage of membrane-bound FasL to a soluble form (sFasL) does not affect its ability to bind to Fas but drastically decreases its cytotoxic activity. Conversely, cross-linking epitope-tagged sFasL with anti-tag antibodies to mimic membrane-bound ligand results in a 1000-fold increase in cytotoxicity. This suggests that more than three Fas molecules need to be aggregated to efficiently signal apoptosis. Death receptors can also be regulated by decoy receptors. The cytotoxic ligand TRAIL interacts with five receptors, only two of which (TRAIL-R1 and -R2) have a death domain. TRAIL-R3 is anchored to the membrane by a glycolipid and acts as a dominant negative inhibitor of TRAIL-mediated apoptosis when overexpressed on TRAIL-sensitive cells. Intracellular proteins interacting with the apoptotic pathway are potential modulators of death receptors. FLIP resembles Caspase-8 in structure but lacks protease activity. It interacts with both FADD and Caspase-8 to inhibits the apoptotic signal of death receptors and, at the same time, can activate other signalling pathways such as that leading to NF-kappa B activation.

Topological comparison of methods for predicting transcriptional cooperativity in yeast

Background: The cooperative interaction between transcription factors has a decisive role in the control of the fate of the eukaryotic cell. Computational approaches for characterizing cooperative transcription factors in yeast, however, are based on different rationales and provide a low overlap between their results. Because the wealth of information contained in protein interaction networks and regulatory networks has proven highly effective in elucidating functional relationships between proteins, we compared different sets of cooperative transcription factor pairs (predicted by four different computational methods) within the frame of those networks. Results: Our results show that the overlap between the sets of cooperative transcription factors predicted by the different methods is low yet significant. Cooperative transcription factors predicted by all methods are closer and more clustered in the protein interaction network than expected by chance. On the other hand, members of a cooperative transcription factor pair neither seemed to regulate each other nor shared similar regulatory inputs, although they do regulate similar groups of target genes. Conclusion: Despite the different definitions of transcriptional cooperativity and the different computational approaches used to characterize cooperativity between transcription factors, the analysis of their roles in the framework of the protein interaction network and the regulatory network indicates a common denominator for the predictions under study. The knowledge of the shared topological properties of cooperative transcription factor pairs in both networks can be useful not only for designing better prediction methods but also for better understanding the complexities of transcriptional control in eukaryotes.

Predicting cancer involvement of genes from heterogeneous data

Background: Systematic approaches for identifying proteins involved in different types of cancer are needed. Experimental techniques such as microarrays are being used to characterize cancer, but validating their results can be a laborious task. Computational approaches are used to prioritize between genes putatively involved in cancer, usually based on further analyzing experimental data. Results: We implemented a systematic method using the PIANA software that predicts cancer involvement of genes by integrating heterogeneous datasets. Specifically, we produced lists of genes likely to be involved in cancer by relying on: (i) protein-protein interactions; (ii) differential expression data; and (iii) structural and functional properties of cancer genes. The integrative approach that combines multiple sources of data obtained positive predictive values ranging from 23% (on a list of 811 genes) to 73% (on a list of 22 genes), outperforming the use of any of the data sources alone. We analyze a list of 20 cancer gene predictions, finding that most of them have been recently linked to cancer in literature. Conclusion: Our approach to identifying and prioritizing candidate cancer genes can be used to produce lists of genes likely to be involved in cancer. Our results suggest that differential expression studies yielding high numbers of candidate cancer genes can be filtered using protein interaction networks.

Predicting species distributions for conservation decisions.

Species distribution models (SDMs) are increasingly proposed to support conservation decision making. However, evidence of SDMs supporting solutions for on-ground conservation problems is still scarce in the scientific literature. Here, we show that successful examples exist but are still largely hidden in the grey literature, and thus less accessible for analysis and learning. Furthermore, the decision framework within which SDMs are used is rarely made explicit. Using case studies from biological invasions, identification of critical habitats, reserve selection and translocation of endangered species, we propose that SDMs may be tailored to suit a range of decision-making contexts when used within a structured and transparent decision-making process. To construct appropriate SDMs to more effectively guide conservation actions, modellers need to better understand the decision process, and decision makers need to provide feedback to modellers regarding the actual use of SDMs to support conservation decisions. This could be facilitated by individuals or institutions playing the role of 'translators' between modellers and decision makers. We encourage species distribution modellers to get involved in real decision-making processes that will benefit from their technical input; this strategy has the potential to better bridge theory and practice, and contribute to improve both scientific knowledge and conservation outcomes.

Autosis is a Na+,K+-ATPase-regulated form of cell death triggered by autophagy-inducing peptides, starvation, and hypoxia-ischemia.

A long-standing controversy is whether autophagy is a bona fide cause of mammalian cell death. We used a cell-penetrating autophagy-inducing peptide, Tat-Beclin 1, derived from the autophagy protein Beclin 1, to investigate whether high levels of autophagy result in cell death by autophagy. Here we show that Tat-Beclin 1 induces dose-dependent death that is blocked by pharmacological or genetic inhibition of autophagy, but not of apoptosis or necroptosis. This death, termed "autosis," has unique morphological features, including increased autophagosomes/autolysosomes and nuclear convolution at early stages, and focal swelling of the perinuclear space at late stages. We also observed autotic death in cells during stress conditions, including in a subpopulation of nutrient-starved cells in vitro and in hippocampal neurons of neonatal rats subjected to cerebral hypoxia-ischemia in vivo. A chemical screen of ~5,000 known bioactive compounds revealed that cardiac glycosides, antagonists of Na(+),K(+)-ATPase, inhibit autotic cell death in vitro and in vivo. Furthermore, genetic knockdown of the Na(+),K(+)-ATPase α1 subunit blocks peptide and starvation-induced autosis in vitro. Thus, we have identified a unique form of autophagy-dependent cell death, a Food and Drug Administration-approved class of compounds that inhibit such death, and a crucial role for Na(+),K(+)-ATPase in its regulation. These findings have implications for understanding how cells die during certain stress conditions and how such cell death might be prevented.

Fas triggers an alternative, caspase-8-independent cell death pathway using the kinase RIP as effector molecule.

Cell death is achieved by two fundamentally different mechanisms: apoptosis and necrosis. Apoptosis is dependent on caspase activation, whereas the caspase-independent necrotic signaling pathway remains largely uncharacterized. We show here that Fas kills activated primary T cells efficiently in the absence of active caspases, which results in necrotic morphological changes and late mitochondrial damage but no cytochrome c release. This Fas ligand-induced caspase-independent death is absent in T cells that are deficient in either Fas-associated death domain (FADD) or receptor-interacting protein (RIP). RIP is also required for necrotic death induced by tumor necrosis factor (TNF) and TNF-related apoptosis-inducing ligand (TRAIL). In contrast to its role in nuclear factor kappa B activation, RIP requires its own kinase activity for death signaling. Thus, Fas, TRAIL and TNF receptors can initiate cell death by two alternative pathways, one relying on caspase-8 and the other dependent on the kinase RIP.

Predicting clinical scores from magnetic resonance scans in Alzheimer's disease.

Machine learning and pattern recognition methods have been used to diagnose Alzheimer's disease (AD) and mild cognitive impairment (MCI) from individual MRI scans. Another application of such methods is to predict clinical scores from individual scans. Using relevance vector regression (RVR), we predicted individuals' performances on established tests from their MRI T1 weighted image in two independent data sets. From Mayo Clinic, 73 probable AD patients and 91 cognitively normal (CN) controls completed the Mini-Mental State Examination (MMSE), Dementia Rating Scale (DRS), and Auditory Verbal Learning Test (AVLT) within 3months of their scan. Baseline MRI's from the Alzheimer's disease Neuroimaging Initiative (ADNI) comprised the other data set; 113 AD, 351 MCI, and 122 CN subjects completed the MMSE and Alzheimer's Disease Assessment Scale-Cognitive subtest (ADAS-cog) and 39 AD, 92 MCI, and 32 CN ADNI subjects completed MMSE, ADAS-cog, and AVLT. Predicted and actual clinical scores were highly correlated for the MMSE, DRS, and ADAS-cog tests (P<0.0001). Training with one data set and testing with another demonstrated stability between data sets. DRS, MMSE, and ADAS-Cog correlated better than AVLT with whole brain grey matter changes associated with AD. This result underscores their utility for screening and tracking disease. RVR offers a novel way to measure interactions between structural changes and neuropsychological tests beyond that of univariate methods. In clinical practice, we envision using RVR to aid in diagnosis and predict clinical outcome.

Multiple interacting cell death mechanisms in the mediation of excitotoxicity and ischemic brain damage: A challenge for neuroprotection.

There is currently no approved neuroprotective pharmacotherapy for acute conditions such as stroke and cerebral asphyxia. One of the reasons for this may be the multiplicity of cell death mechanisms, because inhibition of a particular mechanism leaves the brain vulnerable to alternative ones. It is therefore essential to understand the different cell death mechanisms and their interactions. We here review the multiple signaling pathways underlying each of the three main morphological types of cell death - apoptosis, autophagic cell death and necrosis - emphasizing their importance in the neuronal death that occurs during cerebral ischemia and hypoxia-ischemia, and we analyze the interactions between the different mechanisms. Finally, we discuss the implications of the multiplicity of cell death mechanisms for the design of neuroprotective strategies.

SPECT myocardial perfusion imaging: long-term prognostic value in diabetic patients with and without coronary artery disease.

AIM: To determine the long-term prognostic value of SPECT myocardial perfusion imaging (MPI) for the occurrence of cardiovascular events in diabetic patients. PATIENTS, METHODS: SPECT MPI of 210 consecutive Caucasian diabetic patients were analysed using Kaplan-Meier event-free survival curves and independent predictors were determined by Cox multivariate analyses. RESULTS: Follow-up was complete in 200 (95%) patients with a median period of 3.0 years (0.8-5.0). The population was composed of 114 (57%) men, age 65 +/- 10 years, 181 (90.5%) type 2 diabetes mellitus, 50 (25%) with a history of coronary artery disease (CAD) and 98 (49%) presenting chest pain prior to MPI. The prevalence of abnormal MPI was 58%. Patients with a normal MPI had neither cardiac death, nor myocardial infarction, independently of a history of coronary artery disease or chest pain. Among the independent predictors of cardiac death and myocardial infarction, the strongest was abnormal MPI (p < 0.0001), followed by history of CAD (Hazard Ratio (HR) = 15.9; p = 0.0001), diabetic retinopathy (HR = 10.0; p = 0.001) and inability to exercise (HR = 7.7; p = 0.02). Patients with normal MPI had a low revascularisation rate of 2.4% during the follow-up period. Compared to normal MPI, cardiovascular events increased 5.2 fold for reversible defects, 8.5 fold for fixed defects and 20.1 fold for the association of both defects. CONCLUSION: Diabetic patients with normal MPI had an excellent prognosis independently of history of CAD. On the opposite, an abnormal MPI led to a >5-fold increase in cardiovascular events. This emphasizes the value of SPECT MPI in predicting and risk-stratifying cardiovascular events in diabetic patients.

SESAM - a new framework integrating macroecological and species distribution models for predicting spatio-temporal patterns of species assemblages

Two different approaches currently prevail for predicting spatial patterns of species assemblages. The first approach (macroecological modelling, MEM) focuses directly on realised properties of species assemblages, whereas the second approach (stacked species distribution modelling, S-SDM) starts with constituent species to approximate assemblage properties. Here, we propose to unify the two approaches in a single 'spatially-explicit species assemblage modelling' (SESAM) framework. This framework uses relevant species source pool designations, macroecological factors, and ecological assembly rules to constrain predictions of the richness and composition of species assemblages obtained by stacking predictions of individual species distributions. We believe that such a framework could prove useful in many theoretical and applied disciplines of ecology and evolution, both for improving our basic understanding of species assembly across spatio-temporal scales and for anticipating expected consequences of local, regional or global environmental changes. In this paper, we propose such a framework and call for further developments and testing across a broad range of community types in a variety of environments.

Sudden death and cocaine abuse

We read with great interest the results of the study by Lucena et al.(1) and present the following comments related to the paper. Lucena et al. presented a series of 21-cocaine-related sudden deaths (SD) showing two main structural abnormalities, cardiac hypertrophy and atherosclerotic coronary disease. Both abnormalities have been previously considered as a consequence of a chronic cocaine abuse, but could also be related to others cardiovascular risk factors, as admitted by Lucena et al.