Foam nest construction is a unique oviposition method that evolved independently in anurans lineages from the Neotropics, Asia, and Africa. The nest’s formation involves protein secretions from the oviduct follow by a series of fast kicking of the hind legs by the amplectant pair. One of their potential functions is protection against pathogens, but with a lack of supportive evidence. In this sense, the overall aims of our project is to understand whether skin’s antimicrobial peptides (AMPs), produced by dermal glands may be incorporated to the foam during nest formation and modulate its microbiome. This study represents the first steps on this regard. Here, we analyzed transcriptomes from dorsal skin region of a male and a female of Physalaemus santafecinus to assess AMP gene expression. To assemble the transcriptome, we used Trinity and Spades, and Transdecoder and Orfpredictor for translation. Using conserved sequences from the signal peptide region of described prepro-peptides in amphibians, over 40 mature peptides were found in male, 37 in the female, and only 2 in oviduct. The two peptides expressed in the oviduct were also present in skin of both sexes. More than 10 peptides were expressed in both sexes, and other several exhibited similar sequences. These are newly described peptides, sharing <50% similarity with the 3569+ AMPs in the APD (Antimicrobial Peptide Database). Physicochemical analysis revealed varied charges, hydrophobicity, and 3D structures. This transcriptomic characterization of Physalaemus santafecinus skin, coupled with biochemical and microbiological data, offers crucial insights into reproductive mechanism and the role of secretions in foam nest construction.

Biofilms in chronic wounds, including diabetic foot ulcers, pressure ulcers, and venous leg ulcers, pose a major challenge to wound management. Herein, we report a Janus-type antimicrobial dressing for eradication of biofilms in chronic wounds. The dressing consists of electrospun nanofiber membranes coupled with dissolvable microneedle arrays to enable effective delivery of a database-designed antimicrobial peptide to both inside and outside biofilms. This antimicrobial dressing exhibited high efficacy against a broad spectrum of resistant pathogens in vitro. Importantly, such a dressing was able to eradicate methicillin resistant Staphylococcus aureus (MRSA) biofilms in both an ex vivo human skin wound infection model and a type II diabetic mouse wound infection model after daily treatment without applying surgical debridement. Most importantly, the dressing can also completely remove the Pseudomonas aeruginosa and MRSA, dual-species biofilm in an ex vivo human skin infection model. In addition, our computational simulations also suggested that microneedles were more effective in the delivery of peptides to the biofilms than free drugs. Our results indicate that the Janus-type antimicrobial dressings may provide an effective treatment and management of chronic wound polymicrobial infections.

Antimicrobial peptides (AMPs) have emerged as a promising approach in the development of antibiotics. In contrast to traditional chemical-based antibiotics, AMPs exert their effects through a "physical" mechanism. Specific AMPs have the capability to physically disrupt the cell membrane of bacteria, leading to their demise. Nevertheless, it is crucial to consider the interaction between AMPs and normal cells. AMPs that indiscriminately eliminate all types of cells cannot be employed as pharmaceuticals, as they would also interfere with the regular physiological functions within our bodies.

The primary goal of this study is to mitigate the extent of hemolysis caused by the synthesized AMP sequences. Computational methods are employed to identify potential AMP sequences, as this approach proves to be cost-effective compared to the actual synthesis of the sequences. Hence, the early screening of sequences with the potential to induce hemolysis offers distinct advantages.

To accomplish this, a variety of ensemble classification models were constructed to ascertain whether a peptide sequence would induce a particular degree of hemolysis under specified peptide concentrations based on the dataset form DBAASP. These models were developed by integrating diverse machine learning techniques, including support vector machines, random forests, AdaBoost, multilayer perceptron, k-nearest neighbors, and XGBoost. In general, the results of this study demonstrate an accuracy of approximately 0.82, 0.8 and 0.81 in predicting whether a peptide sequence is hemolytic under a 10%, 20% and 40% hemolysis threshold, respectvely.

The secretion of amphibians has been used for many years for different purposes such as religion, culture, agro-economics or even as therapeutic agents. Although these secretions are a mixture of different molecules, especially the active peptides therein have been studied intensively during the past 30 years. Peptides have shown to be able to inhibit the growth of different human microorganisms (making from them good candidates for potential therapeutic agents), and soil microorganisms. The objectives of this work was to analyze the secretion of Leptodactylus labyrinthicus in order to identify bioactive peptides, with emphasis on their biological activity in an ecological context. Results reported here shown that, when fractioning the skin secretion of L. labyrinthicus, the growth of Staphylococcus aureus and Burkholderia cepacia were slowly delayed by fractions 8 and 9, but not any delaying effect was observed on Escherichia coli. Interestingly, the whole secretion, have shown a growth promotion for Burkholderia cepacia, but not any effect on Staphylococcus aureus and Escherichia coli. In addition, two peptides already described in the literature, known as Pentadactylin (which inhibit E. coli, P. aeruginosa and S. aureus), and Ocellatin-F1 (which inhibit E.coli and P. aeruginosa), were found in fractions 8 and 9 of this frog skin secretion. In view of these results we hypothesize that, the secretion of Leptodactylus labyrinthicus is a mix of molecules that act in synergy as a bio-regulator mechanism selecting or benefiting microorganisms on the frog’s skin population for ecological, immunological or other purposes not well understood to the moment.

Amphibian skin secretion has been an important source of broad-spectrum and membrane-targeting antimicrobial peptides, which promise to tackle the antibiotic resistance crisis.Callimedusa ecuatoriana from Ecuador is an example of an unexplored species, that can hold a library of novel chemical scaffolds with antibiotic action. In this study, we report a novel skin peptide (PTR-CE1) identified by molecular cloning of mRNA precursor. We demonstrated that it lacks of antimicrobial activity. So, using the natural sequence of PTR-CE1 as a template, we designed and synthesized two analogs (PTR-CE1a and PTR-CE1b). Both engineered peptides displayed high antibacterial activity, even against the ampicillin-resistant bacterial strains. While PTR-CE1b showed MIC values of 106.5-212.99 mM and less than 10% of damage to red blood cells at 3.02 mM, PTR-CE1a displayed a more potent broad-spectrum effect against all the tested microorganisms, with MIC values of 3.02-12.06 mM, and low hemolytic properties at 6.66 mM. This study highlights the role of the secondary structure for antimicrobial activity and shows how inactive peptides can be useful as a template for the generation of new molecules with high activity and low toxicity.

This poster will provide a summary on the recent research results on KR-12, a 12-residuecationic antimicrobial peptide derived from human cathelicidin LL-37. KR-12 has been shown to have a selective toxic effect on bacteria but not on human cells. The positive charges of KR-12 allow it to interact with negatively charged bacterial membranes. Moreover, KR-12 has been found to possess anti-inflammatory properties useful for development of novel wound dressings. KR-12 has been shown to promote the osteogenic differentiation of human bone marrow stem cells by stimulating BMP/SMAD signaling. In addition, different forms of KR-12 have been designed, including conjugated hybrids, lipidated analogs, and cyclic peptides. Finally, KR-12 has been immobilized on various surfaces to prevent biofilm formation. In conclusion, KR-12 has shown promise for various applications in medicine, food, animal husbandry, agriculture, and aquaculture.

Plant-associated fungi, including pathogens as well as mutualists, secrete small proteins, typically referred to as effectors, to support their colonization of host tissues. Although essentially described as modulators of plant immunity, effectors may also function as antimicrobials antagonizing the growth of bacterial and fungal competitors in plant microbiota. Such antimicrobial effectors have recently been identified in the soil-borne pathogen Verticillium dahliae, and their occurrence and conservation throughout the fungal kingdom remain enigmatic. We aimed to annotate genes encoding putative antimicrobial effectors in fungal genomes. Predictors of antimicrobial activity have previously been developed but are mostly dedicated to short peptides and therefore unadapted to fungal effectors. After curating a large set of previously characterized antimicrobial proteins, we trained a classifier that can accurately predict the antimicrobial activity of fungal effectors, relying on sequence- and structure-derived physicochemical properties. This tool was used to predict antimicrobial effector catalogs in fungal genomes, and allows us to screen protein databases to discover novel antimicrobial effector families.

The rising incidence of fungal infections has prompted the exploration of novel therapeutic avenues, with antimicrobial peptides (AMPs) emerging as promising candidates for antifungal therapies. Various computational methodologies, including template-based approaches, docking simulations, alignment methods, and machine learning techniques, have been harnessed for predicting and designing antifungal peptides (AFPs).

In this study, we developed an artificial neural network (ANN) based deep-learning model to predict antifungal activity of peptides using their amino acid sequence. Leveraging a diverse dataset of experimentally validated antifungal peptides, our model predicts antifungal activity and facilitates the design of new peptides with high in silico predicted efficacy.

The positive dataset comprised 1478 unique AFPs from Antifp, Uniprot, and APD3 databases, while the negative dataset consisted of an equal number of a mix of random sequences from Uniprot (not classified as AFPs or AMPs) and randomly generated sequences. Employing an 80/20 train-test split, we used one-hot encoding to transform peptide sequences into a format suitable to neural network (NN) analysis, enabling the utilization of convolutional neural networks (CNN) and long short-term memory (LSTM) layers. LSTM layers are widely recognized for their utility in capturing sequential dependencies effectively, making them the go-to choice for peptide prediction using ANNs. Our model architecture incorporates combinations of CNN with LSTM layers of varying units, accompanied by dropout layers to reduce overfitting and dense layers to extract pivotal features from peptide sequences, enabling the detection of subtle patterns associated with antifungal activity. Model performance was evaluated based on ROC (Receiver Operating Characteristic) curve area, sensitivity [SE = true positive / (true positive + false negative)], and specificity [SP = true negative / (true negative + false positive)]. Models were built using Keras framework.

The best-performing model featured a single CNN layer followed by LSTM, dense, and dropout layers. While similar architectures have been explored in previous AFP studies, a notable distinction lies in the incorporation of dropout layers with distinct dropout rates in all LSTM and dense layers. The model achieved remarkable performance metrics: Accuracy = 92.46%, SE = 92.98%, SP = 91.95%, and ROC curve area = 0.98. Furthermore, we employed the model in reverse to generate sequences with high predicted antifungal activity. Random 5-40 mer sequences were iteratively generated and subjected to model prediction. The iterations stopped when 1000 sequences surpassing a prediction threshold of 0.99 were found. This calculation took 178534 iterations (approx. 3 h) on a T4 GPU. The 5 sequences with the highest predicted value were: NPTALKKLHKAR, CTRRPCIA, KSCNVNCACVR, TKCCGVMKAVNGPCYCW, ECKCYPSCPVRHKY.

Our work's next steps entail synthesizing and evaluating the identified sequences against various fungal species. Additionally, we plan to develop and make available an online application for public use, enabling the prediction and design of AFPs using our model. These outcomes underscore the potency of ANN-based approaches in predicting biological peptide activity solely from their amino acid sequences, with significant implications for the tailored design of novel AFPs.

Botrytis cinerea is a fungal phytopathogen with the second largest worldwide impact on the agricultural industry. We evaluated the ability of four peptides from vertebrate cathelicidins to reduce B. cinerea infection in tomato leaves: two proline-rich peptides (LV-RR32 and TT-FR28) and two α-helix (AM-RV28 and TO-KL37). For this purpose, we inoculated four-week-old tomatoes third leaf with a 5 μL droplet of B. cinerea (2X105 conidia/mL) mixed with each peptide a different concentration (200, 100, 50 and 25 μM). After three days of incubation at 24ºC in dark conditions, the average lesion diameter was determined. From the proline-rich peptides examined, the results showed that LV-RR32 was the most effective, fully inhibiting the infection at 100 μM and greatly reducing it at the lowest concentration. TT-FR28, on the other hand, displayed less activity. The larger size and higher proline and arginine content could be responsible of the higher activity of LV-RR32. In the case of α-helices peptides, AM-RV28 inhibited completely the infection at 100 μM and showed good activity at lower concentrations. Meanwhile, TO-KL37 only reduced the infection at the higher concentration tested (200 μM). In this case, AM-RV28 presented the higher hydrophobicity in its α-helix, which could be related with its higher activity against this fungus.

This study is an important step toward harnessing the power of natural defense molecules to address agricultural challenges, and it lays the groundwork for further exploration of the potential of these peptides for crop protection and pathogen management.

Dehydrins, PpDHNA and PpDHNB from Physcomitrium patens provide drought and cold tolerance while PpDHNC shows antimicrobial property suggesting different dehydrins perform separate functions in P. patens. The moss Physcomitrium patens can withstand extremes of environmental condition including abiotic stress such as dehydration, salinity, low temperature and biotic stress such as pathogen attack. Osmotic stress is inflicted under both cold and drought stress conditions where dehydrins have been found to play a significant protective role. In this study, a comparative analysis was drawn for the three dehydrins PpDHNA, PpDHNB and PpDHNC from P. patens. Our data shows that PpDHNA and PpDHNB play a major role in cellular protection during osmotic stress. PpDHNB showed several fold upregulation of the gene when P. patens was subjected to cold and osmotic stress in combination. PpDHNA and PpDHNB provide protection to enzyme lactate dehydrogenase under osmotic as well as freezing conditions. PpDHNC possesses antibacterial activity and thus may have a role in biotic stress response. PpDHNC shows antimicrobial activity against Rhodococcus fascians and Bacillus subtilis. The K segments of PpDHNC are probably associated with the antimicrobial activity. Further investigations involve the use of K segments of PpDHNC alongwith PpDHNA and PpDHNB to form a supra molecule of dehydrin that may show protective properties under multi-stress conditions.