In Silico Target Prediction of Overexpressed microRNAs from LPS-Challenged Zebrafish (Danio rerio) Treated with the Novel Anti-Inflammatory Peptide TnP

miRNAs regulate gene expression post-transcriptionally in various processes, e.g., immunity, development, and diseases. Since their experimental analysis is complex, in silico target prediction is important for directing investigations. TnP is a candidate peptide for anti-inflammatory therapy, first discovered in the venom of Thalassophryne nattereri, which led to miRNAs overexpression in LPS-inflamed zebrafish post-treatment. This work aimed to predict miR-21, miR-122, miR-731, and miR-26 targets using overlapped results of DIANA microT-CDS and TargetScanFish software. This study described 513 miRNAs targets using highly specific thresholds. Using Gene Ontology over-representation analysis, we identified their main roles in regulating gene expression, neurogenesis, DNA-binding, transcription regulation, immune system process, and inflammatory response. miRNAs act in post-transcriptional regulation, but we revealed that their targets are strongly related to expression regulation at the transcriptional level, e.g., transcription factors proteins. A few predicted genes participated concomitantly in many biological processes and molecular functions, such as foxo3a, rbpjb, rxrbb, tyrobp, hes6, zic5, smad1, e2f7, and npas4a. Others were particularly involved in innate immunity regulation: il17a/f2, pik3r3b, and nlrc6. Together, these findings not only provide new insights into the miRNAs mode of action but also raise hope for TnP therapy and may direct future experimental investigations.

A Novel Netrin-1-Derived Peptide Enhances Protection against Neuronal Death and Mitigates of Intracerebral Hemorrhage in Mice

It has been reported that Netrin-1 is involved in neuroprotection following injury to the central nervous system. However, the minimal functional domain of Netrin-1 which can preserve the neuroprotection but avoid the major side effects of Netrin remains elusive. Here, we investigated the neuroprotective effect of a peptide E1 derived from Netrin-1′s EGF3 domain (residues 407–422). We found that it interacts with deleted colorectal carcinoma (DCC) to activate focal adhesion kinase phosphorylation exhibiting neuroprotection. The administration of the peptide E1 was able to improve functional recovery through reduced apoptosis in an experimental murine model of intracerebral hemorrhage (ICH). In summary, we reveal a functional sequence of Netrin-1 that is involved in the recovery process after ICH and identify a candidate peptide for the treatment of ICH.

Immunoinformatics Identification of B- and T-Cell Epitopes in the RNA-Dependent RNA Polymerase of SARS-CoV-2

SARS-CoV-2 (Severe acute respiratory syndrome coronavirus-2) is a newly emerged beta coronavirus and etiolating agent of COVID-19. Considering the unprecedented increasing number of COVID-19 cases, the World Health Organization declared a public health emergency internationally on 11th March 2020. However, existing drugs are insufficient in dealing with this contagious virus infection; therefore, a vaccine is exigent to curb this pandemic disease. In the present study, B- and T-cell immune epitopes were identified for RdRp (RNA-dependent RNA polymerase) protein using immunoinformatic techniques, which is proved to be a rapid and efficient method to explore the candidate peptide vaccine. Subsequently, antigenicity and interactions with HLA (human leukocyte antigen) alleles were estimated. Further, physicochemical properties, allergenicity, toxicity, and stability of RdRp protein were evaluated to demonstrate the specificity of the epitope candidates. Interestingly, we identified a total of 36 B-cell and 16 T-cell epitopes using epitopes predictive tools. Among the predicted epitopes, 26 B-cell and 9 T-cell epitopes showed non-allergenic, non-toxic, and highly antigenic properties. Altogether, our study revealed that RdRp of SARS-CoV-2 (an epitope-based peptide fragment) can be a potentially good candidate for the development of a vaccine against SARS-CoV-2.

Design of a novel antimicrobial peptide 1018M targeted ppGpp to inhibit MRSA biofilm formation

Methicillin-resistant staphylococcus aureus (MRSA) and its biofilm infection were considered as one of the main international health issues. There are still many challenges for treatment using traditional antibiotics. In this study, a mutant peptide of innate defense regulator (IDR-)1018 named 1018M was designed based on molecular docking and amino acid substitution technology. The antibacterial/biofilm activity and mechanisms against MRSA of 1018M were investigated for the first time. The minimum inhibitory concentration (MIC) of 1018M was reduced 1 time (MIC = 2 μg/mL) compared to IDR-1018. After treatment with 32 μg/mL 1018M for 24 h, the percentage of biofilm decreased by 78.9%, which was more effective than the parental peptide. The results of mechanisms exploration showed that 1018M was more potent than IDR-1018 at destructing bacterial cell wall, permeating cell membrane (20.4%–50.1% vs 1.45%–10.6%) and binding to stringent response signaling molecule ppGpp (increased 27.9%). Additionally, the peptides could also exert their activity by disrupting genomic DNA, regulating the expression of ppGpp metabolism and biofilm forming related genes (RSH, relP, relQ, rsbU, sigB, spA, codY, agrA and icaD). Moreover, the higher temperature, pH and pepsase stabilities provide 1018M better processing, storage and internal environmental tolerance. These data indicated that 1018M may be a potential candidate peptide for the treatment of MRSA and its biofilm infections.

LB-18. Broad and Prevalent SARS-CoV-2 CD8+ T Cell Response in Recovered COVID-19 Individuals Demonstrates Kinetics of Early Differentiation

Background Understanding the diversity, breadth, magnitude, and functional profile of the T cell response against SARS-CoV-2 in recovered COVID-19 individuals is critical to evaluate the contribution of T cells to produce a potentially protective immune response. Methods We used a multiplexed peptide-MHC tetramer approach to screen a total of 408 SARS-CoV-2 candidate peptide epitopes for CD8+ T cell recognition in a cohort of 30 individuals recovered from COVID-19. The peptides spanned the whole viral genome and were restricted to six prevalent HLA alleles; T cells were simultaneously characterized by a 28-marker phenotypic panel. The evolution of the SARS-CoV-2 T cell responses was then statistically modeled against time from diagnosis, and in relation to humoral and inflammatory response. Workflow for Study. A multiplexed peptide-MHC tetramer approach was used to screen SARS-CoV-2 candidate peptide epitopes in a cohort of 30 COVID-19 recovered patients across 6 prevalent HLA alleles, and T cells profiled with a 28-marker phenotypic panel. Multiplex tetramer screen. One representative COVID-19 recovered patient and one healthy donor were screened for HLA- relevant SARS-CoV-2 epitopes, as well as epitopes for CMV, EBV, Influenza, Adenovirus and MART-1. Shown are the frequencies of tetramer-positive CD8 T cells from 2 technical replicates per subject. Results Almost all individuals screened showed a T cell response against SARS-CoV-2 (29/30): 132 SARS-CoV-2-specific CD8+ T cells hits were detected, corresponding to 52 unique reactive epitopes. Twelve of the 52 unique SARS-CoV-2-specific epitopes were recognized by more than 40% of the individuals screened, indicating high prevalence in the subjects. Importantly, these CD8+ T cell responses were directed against both structural and non-structural viral proteins, with the highest magnitude against nucleocapsid derived peptides, but without any antigen-driven bias in the phenotype of specific T cells. Overall, SARS-CoV-2 T cells showed specific states of differentiation (stem-cell memory and transitional memory), which differed from those of MART-1, influenza, CMV and EBV-specific T cells. UMAP visualization revealed a phenotypic profile of SARS-CoV-2-specific CD8 T cells in COVID-19 convalescent donors that is distinct from other viral specificities, such as influenza, CMV, EBV and Adenovirus. SARS-CoV-2 epitope screening revealed CD8+ T cell responses directed against both structural and non-structural viral proteins, with the highest magnitude response against nucleocapsid derived peptides Conclusion The kinetics modeling demonstrates a dynamic, evolving immune response characterized by a time-dependent decrease in overall inflammation, increase in neutralizing antibody titer, and progressive differentiation of a broad SARS-CoV-2 CD8 T cell response. It could be desirable to aim at recapitulating the hallmarks of this robust CD8 T cell response in the design of protective COVID-19 vaccines. Disclosures Hassen Kared, PhD, ImmunoScape (Shareholder) Alessandra Nardin, DvM, ImmunoScape (Shareholder) Hermi Sumatoh, BSc, Dip MTech, ImmunoScape (Shareholder) Faris Kairi, BSc, ImmunoScape (Shareholder) Daniel Carbajo, PhD, ImmunoScape (Shareholder) Brian Abel, PhD, MBA, ImmunoScape (Shareholder) Evan Newell, PhD, ImmunoScape (Shareholder)

HAMP: A Knowledgebase of Antimicrobial Peptides from Human Microbiome.

Background: Antimicrobial peptides (AMPs) can defend the hosts against various pathogens and are found in almost every life form from microorganisms to humans. As the rapid increase of drug-resistant strains in recent years is presenting a serious challenge to healthcare, antimicrobial peptides (AMPs) can revolutionize the antimicrobial development against the drug-resistant microbes. Objective: The objective was to encourage the study on the human microbiome towards inhibition of drug-resistant bacteria by the development of a database containing antimicrobial peptides from the human microbiome. Method: This database is an outcome of an extended analysis of Human metagenome, involving the prediction of coding regions, extraction of peptides, prediction of antimicrobial peptides, and modeling their structure utilizing different in silico tools. Further, an intelligent hash function-based query engine was designed to validate the novelty of specific candidate peptide over the reported knowledgebase. Result and Discussion: This knowledgebase currently focuses on antimicrobial peptide sequences (AMPs) predicted from the human microbiome along with 3D their structures modeled using various modeling and molecular dynamics approaches. It includes a total of 1087 unique AMPs from various body sites, with 454 AMPs from the oral cavity, 180 AMPs from the gastrointestinal tract, 42 AMPs from the skin, 12 AMPs from the airway, 6 AMPs from the urogenital tract and 393 AMPs from undefined body locations. A scoring matrix has been generated based on the similarity scores of the sequences that have been incorporated into the knowledgebase. Further, a Jmol applet is included in the website to help users visualize the 3D structures. Conclusion: The information and functions of the knowledgebase can offer great help in finding novel antimicrobial drugs, especially towards finding inhibitors for drug-resistant bacteria. The HAMP is freely available at https://bioserver.iiita.ac.in/amp/index.html.

Immunoinformatic design of multi epitopes peptide-based universal cancer vaccine using matrix metalloproteinase-9 protein as a target

BackgroundCancer remains a major public health hazard despite the extensive research over the years on cancer diagnostic and treatment, this is mainly due to the complex pathophysiology and genetic makeup of cancer. A new approach toward cancer treatment is the use of cancer vaccine, yet the different molecular bases of cancers reduce the effectiveness of this approach. In this work we aim to use matrix metalloproteinase-9 protein (MMP9) which is essential molecule in the survival and metastasis of all type of cancer as a target for universal cancer vaccine design.Methodreference sequence of matrix metalloproteinase-9 protein was obtained from NCBI databases along with the related sequence, which is then checked for conservation using BioEdit, furthermore the B cell and T cell related peptide were analyzed using IEDB website. The best candidate peptide were then visualized using chimera software.ResultThree Peptides found to be good candidate for interactions with B cells (SLPE, RLYT, and PALPR), while ten peptides found as a good target for interactions with MHC1 (YRYGYTRVA, YGYTRVAEM, YLYRYGYTR, WRFDVKAQM, ALWSAVTPL, LLLQKQLSL, LIADKWPAL, KLFGFCPTR, MYPMYRFTE, FLIADKWPA) with world combined coverage of 94.77%. In addition, ten peptides were also found as a good candidates for interactions with MHC2 (KMLLFSGRRLWRFDV, GRGKMLLFSGRRLWR, RGKMLLFSGRRLWRF, GKMLLFSGRRLWRFD, TFTRVYSRDADIVIQ, AVIDDAFARAFALWS, FARAFALWSAVTPLT, MLLFSGRRLWRFDVK, GNQLYLFKDGKYWRF, NQLYLFKDGKYWRFS), with world combined coverage of 90.67%.CONCLUSION23 peptide-based vaccine was designed for use as a universal cancer vaccine which has a high world population coverage for MHC1(94.77%) and MHC2 (90.67%) related alleles.

Selection of Tree Nut Allergen Peptide Markers: A Need for Improved Protein Sequence Databases

Background: To effectively safeguard the food-allergic population and support compliance with food-labeling regulations, the food industry and regulatory agencies require reliable methods for food allergen detection and quantification. MS-based detection of food allergens relies on the systematic identification of robust and selective target peptide markers. The selection of proteotypic peptide markers, however, relies on the availability of high-quality protein sequence information, a bottleneck for the analysis of many plant-based proteomes. Method: In this work, data were compiled for reference tree nut ingredients and evaluated using a parsimony-driven global proteomics workflow. Results: The utility of supplementing existing incomplete protein sequence databases with translated genomic sequencing data was evaluated for English walnut and provided enhanced selection of candidate peptide markers and differentiation between closely related species. Highlights: Future improvements of protein databases and release of genomics-derived sequences are expected to facilitate the development of robust and harmonized LC–tandem MS-based methods for food allergen detection.

Mass Spectrometry-Based Untargeted Proteomics for the Assessment of Food Authenticity: The Case of Farmed Versus Wild-Type Salmon

Background: Omics technologies have been widely applied in different fields, among which, proteomics is gaining increasing interest for its application to the authenticity of food products. MS, typically coupled with LC, represents a key technique for proteomics-related studies dedicated to fish and other seafood products by using a bottom-up approach. Objective and Methods: In this paper, the optimization of an untargeted proteomics-based method using LC separation and MS detection relying on a quadrupole time-of-flight mass spectrometer is described and applied to the analysis of Canadian farmed and wild-type salmon, followed by statistical analysis based on principal component (PC) analysis. Results and Conclusions: This untargeted approach, using a data-independent acquisition MS scheme, demonstrated the ability to effectively discriminate salmon belonging to the two classes. Furthermore, selected peptides showing high loadings on PC1 could represent potential candidate peptide markers able to discriminate farmed from wild-type salmon samples in the future.

Improving peptide-spectrum matching by fragmentation prediction using Hidden Markov Models

Tandem mass-spectrometry has become the method of choice for high-throughput, quantitative analysis in proteomics. However, since the link between the peptides and the proteins they originate from is typically broken, identification of the analyzed peptides relies on matching of the fragmentation spectra (MS2) to theoretical spectra of possible candidate peptides, often filtered for precursor ion mass. To this end, peptide-spectrum matching algorithms score the concordance between the experimental and the theoretical spectra of candidate peptides, by evaluating the number (or proportion) of theoretically possible fragment ions observed in the experimental spectra, without any discrimination. However, the assumption that each theoretical fragment is just as likely to be observed is inaccurate. On the contrary, MS2 spectra often have few dominant fragments.We propose a novel prediction algorithm based on a hidden Markov model, which allow for the training process to be carried out very efficiently. Using millions of MS/MS spectra generated in our lab, we found an overall good reproducibility across different fragmentation spectra, given the precursor peptide and charge state. This result implies that there is indeed a pattern to fragmentation that justifies using machine learning methods. Furthermore, the overall agreement between spectra of the same peptide at the same charge state serves as an upper limit on how well prediction algorithms can be expected to perform.We have investigated the performance of a third order HMM model, trained on several million MS2 spectra, in various ways. Compared to a mock model, in which the fragment ions and their intensities are shuffled, we see a clear difference in prediction accuracy using our model. This result indicates that our model can pick up meaningful patterns, i.e. we can indeed learn the fragmentation process. Secondly, looking at the variability of the prediction performance by varying the train/test data split, in a K-fold cross validation scheme, we observed an overall robust model that performs well independent of the specific peptides that are present in the training data.Last but not least, we propose that the real value of this model is as a pre-processing step in the peptide identification process, by discerning fragment ions that are unlikely to be intense for a given candidate peptide, rather than using the actual predicted intensities. As such, probabilistic measures of concordance between experimental and theoretical spectra, would leverage better statistics.