What factors increase the vulnerability of native birds to the impacts of alien birds?

Biodiversity impacts caused by alien species can be severe, including those caused by alien birds. In order to protect native birds, we aimed to identify factors that influence their vulnerability to the impacts of alien birds. We first reviewed the literature to identify native bird species sustaining such impacts. We then assigned impact severity scores to each native bird species, depending on the severity of the impacts sustained, and performed two types of analyses. First, we used contingency table tests to examine the distribution of impacts across their severity, type and location, and across native bird orders. Second, we used mixed-effects models to test factors hypothesised to influence the vulnerability of native birds to the impacts of alien birds. Ground-nesting shorebirds and seabirds were more prone to impacts through predation, while cavity-nesting woodpeckers and parrots were more prone to impacts through competition. Native bird species were more vulnerable when they occupied islands, warm regions, regions with climatic conditions similar to those in the native range of the invading alien species, and when they were physically smaller than the invading alien species. To a lesser extent, they were also vulnerable when they shared habitat preferences with the invading alien species. By considering the number and type of native bird species affected by alien birds, we demonstrate predation impacts to be more widespread than previously indicated, but also that damaging predation impacts may be underreported. We identify vulnerable orders of native birds, which may require conservation interventions; characteristics of native birds that increase their vulnerability, which may be used to inform risk assessments; and regions where native birds are most vulnerable, which may direct management interventions. The impacts sustained by native birds may be going unnoticed in many regions of the world: there is a clear need to identify and manage them

Identification of Novel Native Autoantigens in Rheumatoid Arthritis

The majority of patients diagnosed with rheumatoid arthritis (RA) have developed autoantibodies against neoepitopes in proteins that have undergone post-translational modification, e.g., citrullination or carbamylation. There is growing evidence of their molecular relevance and their potential utility to improve diagnosis, patient stratification, and prognosis for precision medicine. Autoantibodies reacting to native proteins may also have a role in RA pathogenesis, however, their reactivity patterns remain much less studied. We hypothesized that a high-density protein array technology could shed light onto the normal and disease-related autoantibodies produced in healthy and RA patient subgroups. In an exploratory study, we investigated the global reactivity of autoantibodies in plasma pools from 15 anti-cyclic citrullinated peptide (CCP)-positive and 10 anti-CCP-negative RA patients and 10 healthy donors against more than 1600 native and unmodified human proteins using a high-density protein array. A total of 102 proteins recognized by IgG autoantibodies were identified, hereof 86 were recognized by antibodies from CCP-positive RA patients and 76 from anti-CCP-negative RA patients, but not by antibodies from healthy donors. Twenty-four of the identified autoantigens have previously been identified in synovial fluid. Multiple human proteins in their native conformation are recognized by autoantibodies from anti-CCP-positive as well as anti-CCP-negative RA patients

Identification of potential autoantigens in anti-CCP-positive and anti-CCP-negative rheumatoid arthritis using citrulline-specific protein arrays

Abstract The presence or absence of autoantibodies against citrullinated proteins (ACPAs) distinguishes two main groups of rheumatoid arthritis (RA) patients with different etiologies, prognoses, disease severities, and, presumably, disease pathogenesis. The heterogeneous responses of RA patients to various biologics, even among ACPA-positive patients, emphasize the need for further stratification of the patients. We used high-density protein array technology for fingerprinting of ACPA reactivity. Identification of the proteome recognized by ACPAs may be a step to stratify RA patients according to immune reactivity. Pooled plasma samples from 10 anti-CCP-negative and 15 anti-CCP-positive RA patients were assessed for ACPA content using a modified protein microarray containing 1631 different natively folded proteins citrullinated in situ by protein arginine deiminases (PADs) 2 and PAD4. IgG antibodies from anti-CCP-positive RA plasma showed high-intensity binding to 87 proteins citrullinated by PAD2 and 99 proteins citrullinated by PAD4 without binding significantly to the corresponding native proteins. Curiously, the binding of IgG antibodies in anti-CCP-negative plasma was also enhanced by PAD2- and PAD4-mediated citrullination of 29 and 26 proteins, respectively. For only four proteins, significantly more ACPA binding occurred after citrullination with PAD2 compared to citrullination with PAD4, while the opposite was true for one protein. We demonstrate that PAD2 and PAD4 are equally efficient in generating citrullinated autoantigens recognized by ACPAs. Patterns of proteins recognized by ACPAs may serve as a future diagnostic tool for further subtyping of RA patients

Relating bisimulations with attractors in boolean network models

When studying a biological regulatory network, it is usual to use boolean network models. In these models, boolean variables represent the behavior of each component of the biological system. Taking in account that the size of these state transition models grows exponentially along with the number of components considered, it becomes important to have tools to minimize such models. In this paper, we relate bisimulations, which are relations used in the study of automata (general state transition models) with attractors, which are an important feature of biological boolean models. Hence, we support the idea that bisimulations can be important tools in the study some main features of boolean network models.We also discuss the differences between using this approach and other well-known methodologies to study this kind of systems and we illustrate it with some examples