Matrix models for quantifying competitive intransitivity from species abundance data

In a network of competing species, a competitive intransitivity occurs when the ranking of competitive abilities does not follow a linear hierarchy (A > B > C but C > A). A variety of mathematical models suggests that intransitive networks can prevent or slow down competitive exclusion and maintain biodiversity by enhancing species coexistence. However, it has been difficult to assess empirically the relative importance of intransitive competition because a large number of pairwise species competition experiments are needed to construct a competition matrix that is used to parameterize existing models. Here we introduce a statistical framework for evaluating the contribution of intransitivity to community structure using species abundance matrices that are commonly generated from replicated sampling of species assemblages. We provide metrics and analytical methods for using abundance matrices to estimate species competition and patch transition matrices by using reverse-engineering and a colonization-competition model. These matrices provide complementary metrics to estimate the degree of intransitivity in the competition network of the sampled communities. Benchmark tests reveal that the proposed methods could successfully detect intransitive competition networks, even in the absence of direct measures of pairwise competitive strength. To illustrate the approach, we analyzed patterns of abundance and biomass of five species of necrophagous Diptera and eight species of their hymenopteran parasitoids that co-occur in beech forests in Germany. We found evidence for a strong competitive hierarchy within communities of flies and parasitoids. However, for parasitoids, there was a tendency towards increasing intransitivity in higher weight classes, which represented larger resource patches. These tests provide novel methods for empirically estimating the degree of intransitivity in competitive networks from observational datasets. They can be applied to experimental measures of pairwise species interactions, as well as to spatio-temporal samples of assemblages in homogenous environments or environmental gradients.

Alpha1a-adrenoceptor genetic variant induces cardiomyoblast-to-fibroblast-like cell transition via distinct signaling pathways.

The role of naturally occurring human α1a-Adrenergic Receptor (α1aAR) genetic variants associated with cardiovascular disorders is poorly understood. Here, we present the novel findings that expression of human α1aAR-247R (247R) genetic variant in cardiomyoblasts leads to transition of cardiomyoblasts into a fibroblast-like phenotype, evidenced by morphology and distinct de novo expression of characteristic genes. These fibroblast-like cells exhibit constitutive, high proliferative capacity and agonist-induced hypertrophy compared with cells prior to transition. We demonstrate that constitutive, synergistic activation of EGFR, Src and ERK kinases is the potential molecular mechanism of this transition. We also demonstrate that 247R triggers two distinct EGFR transactivation-dependent signaling pathways: 1) constitutive Gq-independent β-arrestin-1/Src/MMP/EGFR/ERK-dependent hyperproliferation and 2) agonist-induced Gq- and EGFR/STAT-dependent hypertrophy. Interestingly, in cardiomyoblasts agonist-independent hyperproliferation is MMP-dependent, but in fibroblast-like cells it is MMP-independent, suggesting that expression of α1aAR genetic variant in cardiomyocytes may trigger extracellular matrix remodeling. Thus, these novel findings demonstrate that EGFR transactivation by α1aAR-247R leads to hyperproliferation, hypertrophy and alterations in cardiomyoblasts, suggesting that these unique genetically-mediated alterations in signaling pathways and cellular function may lead to myocardial fibrosis. Such extracellular matrix remodeling may contribute to the genesis of arrhythmias in certain types of heart failure.