An integrative framework of coexistence mechanisms in competitive metacommunities

Species distribution in a metacommunity varies according to their traits, the distribution of environmental conditions and connectivity among localities. These ingredients contribute to coexistence across spatial scales via species sorting, patch dynamics, mass effects and neutral dynamics. These mechanisms however seldom act in isolation and the impact of landscape configuration on their relative importance remains poorly understood. We present a new model of metacommunity dynamics that simultaneously considers these four possible mechanisms over spatially explicit landscapes and propose a statistical approach to partition their contribution to species distribution. We find that landscape configuration can induce dispersal limitations that have negative consequences for local species richness. This result was more pronounced with neutral dynamics and mass effect than with species sorting or patch dynamics. We also find that the relative importance of the four mechanisms varies not only among landscape configurations, but also among species, with some species being mostly constrained by dispersal and/or drift and others by sorting. Changes in landscape properties might lead to a shift in coexistence mechanisms and, by extension, to a change in community composition. This confirms the importance of considering landscape properties for conservation and management. Our results illustrate the idea that ecological communities are the results of multiple mechanisms acting at the same time and complete our understanding of spatial processes in competitive metacommunities.     

Signal-to-signal neural networks for improved spike estimation from calcium imaging data

Spiking information of individual neurons is essential for functional and behavioral analysis in neuroscience research. Calcium imaging techniques are generally employed to obtain activities of neuronal populations. However, these techniques result in slowly-varying fluorescence signals with low temporal resolution. Estimating the temporal positions of the neuronal action potentials from these signals is a challenging problem. In the literature, several generative model-based and data-driven algorithms have been studied with varied levels of success. This article proposes a neural network-based signal-to-signal conversion approach, where it takes as input raw-fluorescence signal and learns to estimate the spike information in an end-to-end fashion. Theoretically, the proposed approach formulates the spike estimation as a single channel source separation problem with unknown mixing conditions. The source corresponding to the action potentials at a lower resolution is estimated at the output. Experimental studies on the spikefinder challenge dataset show that the proposed signal-to-signal conversion approach significantly outperforms state-of-the-art-methods in terms of Pearson’s correlation coefficient, Spearman’s rank correlation coefficient and yields comparable performance for the area under the receiver operating characteristics measure. We also show that the resulting system: (a) has low complexity with respect to existing supervised approaches and is reproducible; (b) is layer-wise interpretable, and (c) has the capability to generalize across different calcium indicators.     

Bayesian spatial modeling of moose count data: increasing estimator efficiency and exploring ecological hypotheses

Moose management throughout much of Alaska and Canada relies on aerial count data, which are commonly collected using the geospatial population estimator (GSPE) protocol. The GSPE uses a model-based analytical approach and finite-population block kriging to estimate abundance from a collection of sampled survey units. Widespread implementation and well-documented analytical software have resulted in reliable estimates of moose abundance, density, and composition across a large proportion of their range. Analysis is conducted almost exclusively using the GSPE software, which fits a fixed model structure to data collected within a single year. The downside of this approach to analysis is that the fixed model structure is inefficient for estimation, leading to more field effort than would otherwise be necessary to achieve a desired level of estimator precision. We developed a more easily modified and flexible Bayesian spatial general additive model approach (BSG) that accommodates spatial and temporal covariates (e.g., habitat characteristics, trend), multiple survey events, prior information, and incomplete detection. Using a series of 6 GSPE surveys conducted in Yukon-Charley Rivers National Preserve, Alaska, USA, from 2003–2019, we established the equivalence of the 2 approaches under similar model structures. We then extended the BSG to demonstrate how a more comprehensive approach to analysis can affect estimator precision and be used to assess ecological relationships. The precision of annual abundance estimators from the BSG were improved by an average of 43% over those based on the standard GSPE analysis, highlighting the very real costs of assuming a fixed (i.e., suboptimal) model structure. The population increased at a rate of 2.3%/year (95% CrI = 0.8–3.8%), and the increase was largely explained by a parallel increase in wildfire extent (i.e., high quality moose habitat). These results suggest that our approach could be used to increase estimator efficiency or decrease future survey costs without any modifications to the basic protocol. While modification of the GSPE software is possible, practitioners may find the BSG approach more convenient for quickly developing model structures for a particular application, thereby allowing them to extract more information from existing and future datasets.     

Combinations of PARP Inhibitors with Temozolomide Drive PARP1 Trapping and Apoptosis in Ewing’s Sarcoma

Ewing’s sarcoma is a malignant pediatric bone tumor with a poor prognosis for patients with metastatic or recurrent disease. Ewing’s sarcoma cells are acutely hypersensitive to poly (ADP-ribose) polymerase (PARP) inhibition and this is being evaluated in clinical trials, although the mechanism of hypersensitivity has not been directly addressed. PARP inhibitors have efficacy in tumors with BRCA1/2 mutations, which confer deficiency in DNA double-strand break (DSB) repair by homologous recombination (HR). This drives dependence on PARP1/2 due to their function in DNA single-strand break (SSB) repair. PARP inhibitors are also cytotoxic through inhibiting PARP1/2 auto-PARylation, blocking PARP1/2 release from substrate DNA. Here, we show that PARP inhibitor sensitivity in Ewing’s sarcoma cells is not through an apparent defect in DNA repair by HR, but through hypersensitivity to trapped PARP1-DNA complexes. This drives accumulation of DNA damage during replication, ultimately leading to apoptosis. We also show that the activity of PARP inhibitors is potentiated by temozolomide in Ewing’s sarcoma cells and is associated with enhanced trapping of PARP1-DNA complexes. Furthermore, through mining of large-scale drug sensitivity datasets, we identify a subset of glioma, neuroblastoma and melanoma cell lines as hypersensitive to the combination of temozolomide and PARP inhibition, potentially identifying new avenues for therapeutic intervention. These data provide insights into the anti-cancer activity of PARP inhibitors with implications for the design of treatment for Ewing’s sarcoma patients with PARP inhibitors.     

Alternative stable states and regional community structure

Many models of local species interactions predict the occurrence of priority effects due to alternative stable equilibria (ASE). However, few empirical examples of ASE have been shown. One possible explanation for the disparity is that local ASE are difficult to maintain regionally in patch dynamic models. Here we examine two possible mechanisms for regional coexistence of species engaged in local ASE. Biotically generated heterogeneity (e.g., habitat modification that favors further invasion by conspecifics) results in regional exclusion of one species at equilibrium. In contrast, abiotic heterogeneity due to spatial variation in resource supply ratios generates local-scale ASE and ensures regional coexistence with sufficiently broad environmental gradients. Abiotic heterogeneity can result in a species that is the dominant competitor over some of its range being excluded if the area where it is dominant is too small. Biotic heterogeneity can lead to alternative stable landscapes or regional priority effects, while abiotic heterogeneity results in regional determinism. Broad environmental gradients in resource supply favor regional coexistence of species that exhibit local ASE.     

Genomewide linkage scan for split-hand/foot malformation with long-bone deficiency in a large Arab family identifies two novel susceptibility loci on chromosomes 1q42.2-q43 and 6q14.1

Split-hand/foot malformation with long-bone deficiency (SHFLD) is a rare, severe limb deformity characterized by tibia aplasia with or without split-hand/split-foot deformity. Identification of genetic susceptibility loci for SHFLD has been unsuccessful because of its rare incidence, variable phenotypic expression and associated anomalies, and uncertain inheritance pattern. SHFLD is usually inherited as an autosomal dominant trait with reduced penetrance, although recessive inheritance has also been postulated. We conducted a genomewide linkage analysis, using a 10K SNP array in a large consanguineous family (UR078) from the United Arab Emirates (UAE) who had disease transmission consistent with an autosomal dominant inheritance pattern. The study identified two novel SHFLD susceptibility loci at 1q42.2-q43 (nonparametric linkage [NPL] 9.8, P=.000065) and 6q14.1 (NPL 7.12, P=.000897). These results were also supported by multipoint parametric linkage analysis. Maximum multipoint LOD scores of 3.20 and 3.78 were detected for genomic locations 1q42.2-43 and 6q14.1, respectively, with the use of an autosomal dominant mode of inheritance with reduced penetrance. Haplotype analysis with informative crossovers enabled mapping of the SHFLD loci to a region of approximately 18.38 cM (8.4 Mb) between single-nucleotide polymorphisms rs1124110 and rs535043 on 1q42.2-q43 and to a region of approximately 1.96 cM (4.1 Mb) between rs623155 and rs1547251 on 6q14.1. The study identified two novel loci for the SHFLD phenotype in this UAE family.     

Peripheral genetic structure of Helicoverpa zea indicates asymmetrical panmixia

Seasonal climatic shifts create peripheral habitats that alternate between habitable and uninhabitable for migratory species. Such dynamic peripheral habitats are potential sites where migratory species could evolve high genetic diversity resulting from convergence of immigrants from multiple regionally distant areas. Migrant populations of Helicoverpa zea (Boddie) captured during two different seasons were assessed for genetic structure using microsatellite markers and for host plant type using stable carbon isotope analysis. Individuals (N = 568) were genotyped and divided into 13 putative populations based on collection site and time. Fixation indices (F‐statistics), analysis of molecular variance (AMOVA), and discriminant analysis of principal components (DAPC) were used to examine within and among population genetic variation. Mean number of alleles per locus was 10.25 (± 3.2 SD), and allelic richness ranged from 2.38 to 5.13 (± 3.2 SD). The observed and expected heterozygosity ranged from 0.07 to 0.48 and 0.08 to 0.62, respectively. Low F_ST (0.01 to 0.02) and high F_IS (0.08 to 0.33) values suggest captured migrants originated from breeding populations with different allele frequencies. We postulate that high genetic diversity within migrant populations and low genetic differentiation among migrant populations of H. zea are the result of asymmetrical immigration due to the high dispersal and reproductive behavior of H. zea, which may hinder the adaptation and establishment of H. zea to peripheral habitat. These findings highlight the importance of assessing peripheral population structure in relation to ecological and evolutionary dynamics of this and other highly reproductive and dispersive species.     

OligoMatcher

OligoMatcher is a web-based tool for analysis and selection of unique oligonucleotide sequences for gene silencing by antisense oligonucleotides (ASOs) or small interfering RNA (siRNA). A specific BLAST server was built for analysing sequences of ASOs that target pre-mRNA in the cell nucleus. Tissue- and cell-specific expression data of potential cross-reactive genes are integrated in the OligoMatcher program, which allows biologists to select unique oligonucleotide sequences for their target genes in specific experimental systems. AVAILABILITY: The OligoMatcher web server is available at http://shelob.cs.iupui.edu:18081/oligomatch.php. The source code is freely available for non-profit use on request to the authors. CONTACT: Mathew Palakal (mpalakal@cs.iupui.edu) or Shuyu Li (li_shuyu_dan@lilly.com).