Environmental niche modeling (ENM) is commonly used to develop probabilistic maps of species distribution. Among available ENM techniques, MaxEnt has become one of the most popular tools for modeling species distribution, with hundreds of peer-reviewed articles published each year. MaxEnt’s popularity is mainly due to the use of a graphical interface and automatic parameter configuration capabilities. However, recent studies have shown that using the default automatic configuration may not be always appropriate because it can produce non-optimal models; particularly when dealing with a small number of species presence points. Thus, the recommendation is to evaluate the best potential combination of parameters (feature classes and regularization multiplier) to select the most appropriate model. In this work we reviewed 244 articles published between 2013 and 2015 to assess whether researchers are following recommendations to avoid using the default parameter configuration when dealing with small sample sizes, or if they are using MaxEnt as a “black box tool.” Our results show that in only 16% of analyzed articles authors evaluated best feature classes, in 6.9% evaluated best regularization multipliers, and in a meager 3.7% evaluated simultaneously both parameters before producing the definitive distribution model. We analyzed 20 articles to quantify the potential differences in resulting outputs when using software default parameters instead of the alternative best model. Results from our analysis reveal important differences between the use of default parameters and the best model approach, especially in the total area identified as suitable for the assessed species and the specific areas that are identified as suitable by both modelling approaches. These results are worrying, because publications are potentially reporting over-complex or over-simplistic models that can undermine the applicability of their results. Of particular importance are studies used to inform policy making. Therefore, researchers, practitioners, reviewers and editors need to be very judicious when dealing with MaxEnt, particularly when the modelling process is based on small sample sizes.

Bats are unique in their potential to serve as reservoir hosts for intracellular pathogens. Recently, the impact of COVID-19 has relegated bats from biomedical darkness to the frontline of public health as bats are the natural reservoir of many viruses, including SARS-Cov-2. Many bat genomes have been sequenced recently, and sequences coding for antimicrobial peptides are available in the public databases. Here we provide a structural analysis of genome-predicted bat cathelicidins as components of their innate immunity. A total of 32 unique protein sequences were retrieved from the NCBI database. Interestingly, some bat species contained more than one cathelicidin. We examined the conserved cysteines within the cathelin-like domain and the peptide portion of each sequence and revealed phylogenetic relationships and structural dissimilarities. The antibacterial, antifungal, and antiviral activity of peptides was examined using bioinformatic tools. The peptides were modeled and subjected to docking analysis with the region binding domain (RBD) region of the SARS-CoV-2 Spike protein. The appearance of multiple forms of cathelicidins verifies the complex microbial challenges encountered by these species. Learning more about antiviral defenses of bats and how they drive virus evolution will help scientists to investigate the function of antimicrobial peptides in these species.

In the current worldwide pandemic situation caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and the newest coronavirus disease (COVID-19), therapeutics and prophylactics are urgently needed for a large population. Some of the prophylaxis strategies are based on the development of antibodies targeting viral proteins. IgY antibodies are a type of immunoglobulin present in birds, amphibians, and reptiles. They are usually obtained from egg yolk of hyper-immunized hens and represent a relatively inexpensive source of antibodies. Specific IgY can be produced by immunizing chickens with the target antigen and then purifying from the egg yolk. Chicken IgY has been widely explored as a clinical anti-infective material for prophylaxis, preventive medicine, and therapy of infectious diseases. Administered non-systemically, IgY antibodies are safe and effective drugs. Moreover, passive immunization with avian antibodies could become an effective alternative therapy, as these can be obtained relatively simply, cost-efficiently, and produced on a large scale. Here, we highlight the potential use of polyclonal avian IgY antibodies as an oral prophylactic treatment for respiratory viral diseases, such as COVID-19, for which no vaccine is yet available.

Ruminants produce considerable amounts of methane during their digestive process, which makes the livestock industry as one of the largest sources of anthropogenic greenhouse gases. To tackle this situation, several solutions have been proposed, including vaccination of ruminants against microorganisms responsible for methane synthesis in the rumen. In this review, we summarize the research done on this topic and describe the state of the art of this strategy. The different steps implied in this approach are described: experimental design, animal model (species, age), antigen (whole cells, cell parts, recombinant proteins, peptides), adjuvant (Freund’s, Montanide, saponin, among others), vaccination schedule (booster intervals and numbers) and measurements of treatment success (immunoglobulin titers and/or effects on methanogens and methane production). Highlighting both the advances made and knowledge gaps in the use of vaccines to inhibit ruminant methanogen activity, this research review opens the door to future studies. This will enable improvements in the methodology and systemic approaches so as to ensure the success of this proposal for the sustainable mitigation of methane emission.

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