Using Biotic Interaction Networks for Prediction in Biodiversity and Emerging Diseases

Networks offer a powerful tool for understanding and visualizing inter-species interactions within an ecology. Previously considered examples, such as trophic networks, are just representations of experimentally observed direct interactions. However, species interactions are so rich and complex it is not feasible to directly observe more than a small fraction. In this paper, using data mining techniques, we show how potential interactions can be inferred from geographic data, rather than by direct observation. An important application area for such a methodology is that of emerging diseases, where, often, little is known about inter-species interactions, such as between vectors and reservoirs. Here, we show how using geographic data, biotic interaction networks that model statistical dependencies between species distributions can be used to infer and understand inter-species interactions. Furthermore, we show how such networks can be used to build prediction models. For example, for predicting the most important reservoirs of a disease, or the degree of disease risk associated with a geographical area. We illustrate the general methodology by considering an important emerging disease - Leishmaniasis. This data mining approach allows for the use of geographic data to construct inferential biotic interaction networks which can then be used to build prediction models with a wide range of applications in ecology, biodiversity and emerging diseases

Using Biotic Interaction Networks for Prediction in Biodiversity and Emerging Diseases

Abstract: Networks offer a powerful tool for understanding and visualizing inter-species ecological and evolutionary interactions. Previously considered examples, such as trophic networks, are just representations of experimentally observed direct interactions. However, species interactions are so rich and complex it is not feasible to directly observe more than a small fraction. In this paper, using data mining techniques, we show how potential interactions can be inferred from geographic data, rather than by direct observation. An important application area for this methodology is that of emerging diseases, where, often, little is known about inter-species interactions, such as between vectors and reservoirs. Here, we show how using geographic data, biotic interaction networks that model statistical dependencies between species distributions can be used to infer and understand inter-species interactions. Furthermore, we show how such networks can be used to build prediction models. For example, for predicting the most important reservoirs of a disease, or the degree of disease risk associated with a geographical area. We illustrate the general methodology by considering an important emerging disease -Leishmaniasis. This data mining methodology allows for the use of geographic data to construct inferential biotic interaction networks which can then be used to build prediction models with a wide range of applications in ecology, biodiversity and emerging diseases

Linoleic acid improves PIEZO2 dysfunction in a mouse model of Angelman Syndrome

Angelman syndrome (AS) is a neurogenetic disorder characterized by intellectual disability and atypical behaviors. AS results from loss of expression of the E3 ubiquitin-protein ligase UBE3A from the maternal allele in neurons. Individuals with AS display impaired coordination, poor balance, and gait ataxia. PIEZO2 is a mechanosensitive ion channel essential for coordination and balance. Here, we report that PIEZO2 activity is reduced in Ube3a deficient male and female mouse sensory neurons, a human Merkel cell carcinoma cell line and female human iPSC-derived sensory neurons with UBE3A knock-down, and de-identified stem cell-derived neurons from individuals with AS. We find that loss of UBE3A decreases actin filaments and reduces PIEZO2 expression and function. A linoleic acid (LA)-enriched diet increases PIEZO2 activity, mechano-excitability, and improves gait in male AS mice. Finally, LA supplementation increases PIEZO2 function in stem cell-derived neurons from individuals with AS. We propose a mechanism whereby loss of UBE3A expression reduces PIEZO2 function and identified a fatty acid that enhances channel activity and ameliorates AS-associated mechano-sensory deficits.This work was supported by the Neuroscience Institute at UTHSC (Research Associate Matching Salary Support to J.L.), the Federico Baur endowed chair in Nanotechnology (to F.J.S.-V., 0020206BA1), a pilot research award from the Foundation for Prader-Willi Research (to L.T.R.), the Neuroscience Institute Research Supports Grant 2020 program (to V.V., and J.F.C.-M.), and the National Institutes of Health (R01GM133845 to V.V. and R01GM125629 to J.F.C.-M.)

Ecologic Niche Modeling and Potential Reservoirs for Chagas Disease, Mexico.

Ecologic niche modeling may improve our understanding of epidemiologically relevant vector and parasite-reservoir distributions. We used this tool to identify host relationships of Triatoma species implicated in transmission of Chagas disease. Associations have been documented between the protracta complex (Triatoma: Triatominae: Reduviidae) with packrat species (Neotoma spp.), providing an excellent case study for the broader challenge of developing hypotheses of association. Species pairs that were identified coincided exactly with those in previous studies, suggesting that local interactions between Triatoma and Neotoma species and subspecies have implications at a geographic level. Nothing is known about sylvatic associates of T. barberi, which are considered the primary Chagas vector in Mexico; its geographic distribution coincided closely with that of N. mexicana, suggesting interaction. The presence of the species was confirmed in two regions where it had been predicted but not previously collected. This approach may help in identifying Chagas disease risk areas, planning vector-control strategies, and exploring parasite-reservoir associations for other emerging diseases

Using Biotic Interaction Networks for Prediction in Biodiversity and Emerging Diseases

Networks offer a powerful tool for understanding and visualizing inter-species ecological and evolutionary interactions. Previously considered examples, such as trophic networks, are just representations of experimentally observed direct interactions. However, species interactions are so rich and complex it is not feasible to directly observe more than a small fraction. In this paper, using data mining techniques, we show how potential interactions can be inferred from geographic data, rather than by direct observation. An important application area for this methodology is that of emerging diseases, where, often, little is known about inter-species interactions, such as between vectors and reservoirs. Here, we show how using geographic data, biotic interaction networks that model statistical dependencies between species distributions can be used to infer and understand inter-species interactions. Furthermore, we show how such networks can be used to build prediction models. For example, for predicting the most important reservoirs of a disease, or the degree of disease risk associated with a geographical area. We illustrate the general methodology by considering an important emerging disease - Leishmaniasis. This data mining methodology allows for the use of geographic data to construct inferential biotic interaction networks which can then be used to build prediction models with a wide range of applications in ecology, biodiversity and emerging diseases

Chagas Disease Risk in Texas

Chagas disease is endemic in Texas and spread through triatomine insect vectors known as kissing bugs, assassin bugs, or cone–nosed bugs, which transmit the protozoan parasite, Trypanosoma cruzi. We examined the threat of Chagas disease due to the three most prevalent vector species and from human case occurrences and human population data at the county level. We modeled the distribution of each vector species using occurrence data from México and the United States and environmental variables. We then computed the ecological risk from the distribution models and combined it with disease incidence data to produce a composite risk map which was subsequently used to calculate the populations expected to be at risk for the disease. South Texas had the highest relative risk. We recommend mandatory reporting of Chagas disease in Texas, testing of blood donations in high risk counties, human and canine testing for Chagas disease antibodies in high risk counties, and that a joint initiative be developed between the United States and México to combat Chagas disease

Opuntia in México: Identifying Priority Areas for Conserving Biodiversity in a Multi-Use Landscape

BACKGROUND: México is one of the world's centers of species diversity (richness) for Opuntia cacti. Yet, in spite of their economic and ecological importance, Opuntia species remain poorly studied and protected in México. Many of the species are sparsely but widely distributed across the landscape and are subject to a variety of human uses, so devising implementable conservation plans for them presents formidable difficulties. Multi-criteria analysis can be used to design a spatially coherent conservation area network while permitting sustainable human usage. METHODS AND FINDINGS: Species distribution models were created for 60 Opuntia species using MaxEnt. Targets of representation within conservation area networks were assigned at 100% for the geographically rarest species and 10% for the most common ones. Three different conservation plans were developed to represent the species within these networks using total area, shape, and connectivity as relevant criteria. Multi-criteria analysis and a metaheuristic adaptive tabu search algorithm were used to search for optimal solutions. The plans were built on the existing protected areas of México and prioritized additional areas for management for the persistence of Opuntia species. All plans required around one-third of México's total area to be prioritized for attention for Opuntia conservation, underscoring the implausibility of Opuntia conservation through traditional land reservation. Tabu search turned out to be both computationally tractable and easily implementable for search problems of this kind. CONCLUSIONS: Opuntia conservation in México require the management of large areas of land for multiple uses. The multi-criteria analyses identified priority areas and organized them in large contiguous blocks that can be effectively managed. A high level of connectivity was established among the prioritized areas resulting in the enhancement of possible modes of plant dispersal as well as only a small number of blocks that would be recommended for conservation management

Clonal chromosomal mosaicism and loss of chromosome Y in elderly men increase vulnerability for SARS-CoV-2

The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, COVID-19) had an estimated overall case fatality ratio of 1.38% (pre-vaccination), being 53% higher in males and increasing exponentially with age. Among 9578 individuals diagnosed with COVID-19 in the SCOURGE study, we found 133 cases (1.42%) with detectable clonal mosaicism for chromosome alterations (mCA) and 226 males (5.08%) with acquired loss of chromosome Y (LOY). Individuals with clonal mosaic events (mCA and/or LOY) showed a 54% increase in the risk of COVID-19 lethality. LOY is associated with transcriptomic biomarkers of immune dysfunction, pro-coagulation activity and cardiovascular risk. Interferon-induced genes involved in the initial immune response to SARS-CoV-2 are also down-regulated in LOY. Thus, mCA and LOY underlie at least part of the sex-biased severity and mortality of COVID-19 in aging patients. Given its potential therapeutic and prognostic relevance, evaluation of clonal mosaicism should be implemented as biomarker of COVID-19 severity in elderly people. Among 9578 individuals diagnosed with COVID-19 in the SCOURGE study, individuals with clonal mosaic events (clonal mosaicism for chromosome alterations and/or loss of chromosome Y) showed an increased risk of COVID-19 lethality

New records of a critically endangered shrew from Mexican cloud forests (Soricidae, Cryptotis nelsoni) and prospects for future field research

The Nelson´s small-eared shrew, Cryptotis nelsoni (Merriam, 1895), is a critically endangered species, endemic to cloud forests in Los Tuxtlas, a mountain range along the Gulf of Mexico coast. This species is only known from the type locality and its surroundings. Here we present new records that extend its distribution approximately 7 km southeast of the type locality and report more specimens near to the type locality. We also identified climatically suitable areas for C. nelsoni using ecological niche modelling and investigated the sampling bias to identify poorly sampled areas in Los Tuxtlas. We suggest that the scarcity of records in other areas with suitable climatic conditions throughout Los Tuxtlas is a consequence of incomplete surveys. We strongly highlight the importance of continuing surveying this critically endangered shrew using more efficient sampling techniques to better understand its current distribution and conservation status. Despite all known localities occurring inside Los Tuxtlas Biosphere Reserve, deforestation and climate change still pose current and future threats to this species