Mapping Conformational Changes in the Saliva Proteome Potentially Associated with Oral Cancer Aggressiveness.

Diverse proteomics-based strategies have been applied to saliva to quantitatively identify diagnostic and prognostic targets for oral cancer. Considering that these targets may be regulated by events that do not imply variation in protein abundance levels, we hypothesized that changes in protein conformation can be associated with diagnosis and prognosis, revealing biological processes and novel targets of clinical relevance. For this, we employed limited proteolysis-mass spectrometry in saliva samples to explore structural alterations, comparing the proteome of healthy control and oral squamous cell carcinoma (OSCC) patients with and without lymph node metastasis. Thirty-six proteins with potential structural rearrangements were associated with clinical patient features including transketolase and its interacting partners. Moreover, N-glycosylated peptides contribute to structural rearrangements of potential diagnostic and prognostic markers. Altogether, this approach utilizes saliva proteins to search for targets for diagnosing and prognosing oral cancer and can guide the discovery of potential regulated sites beyond protein-level abundance.

Typic: A Practical and Robust Tool to Rank Proteotypic Peptides for Targeted Proteomics.

The selection of a suitable proteotypic peptide remains a challenge for designing a targeted quantitative proteomics assay. Although the criteria are well-established in the literature, the selection of these peptides is often performed in a subjective and time-consuming manner. Here, we have developed a practical and semiautomated workflow implemented in an open-source program named Typic. Typic is designed to run in a command line and a graphical interface to help selecting a list of proteotypic peptides for targeted quantitation. The tool combines the input data and downloads additional data from public repositories to produce a file per protein as output. Each output file includes relevant information to the selection of proteotypic peptides organized in a table, a colored ranking of peptides according to their potential value as targets for quantitation and auxiliary plots to assist users in the task of proteotypic peptides selection. Taken together, Typic leads to a practical and straightforward data extraction from multiple data sets, allowing the identification of most suitable proteotypic peptides based on established criteria, in an unbiased and standardized manner, ultimately leading to a more robust targeted proteomics assay.

Connecting multiple microenvironment proteomes uncovers the biology in head and neck cancer.

The poor prognosis of head and neck cancer (HNC) is associated with metastasis within the lymph nodes (LNs). Herein, the proteome of 140 multisite samples from a 59-HNC patient cohort, including primary and matched LN-negative or -positive tissues, saliva, and blood cells, reveals insights into the biology and potential metastasis biomarkers that may assist in clinical decision-making. Protein profiles are strictly associated with immune modulation across datasets, and this provides the basis for investigating immune markers associated with metastasis. The proteome of LN metastatic cells recapitulates the proteome of the primary tumor sites. Conversely, the LN microenvironment proteome highlights the candidate prognostic markers. By integrating prioritized peptide, protein, and transcript levels with machine learning models, we identify nodal metastasis signatures in blood and saliva. We present a proteomic characterization wiring multiple sites in HNC, thus providing a promising basis for understanding tumoral biology and identifying metastasis-associated signatures.

A Multiomics Approach Unravels New Toxins With Possible In Silico Antimicrobial, Antiviral, and Antitumoral Activities in the Venom of Acanthoscurria rondoniae.

The Araneae order is considered one of the most successful groups among venomous animals in the world. An important factor for this success is the production of venoms, a refined biological fluid rich in proteins, short peptides and cysteine-rich peptides (CRPs). These toxins may present pharmacologically relevant biological actions, as antimicrobial, antiviral and anticancer activities, for instance. Therefore, there is an increasing interest in the exploration of venom toxins for therapeutic reasons, such as drug development. However, the process of peptide sequencing and mainly the evaluation of potential biological activities of these peptides are laborious, considering the low yield of venom extraction and the high variability of toxins present in spider venoms. Here we show a robust methodology for identification, sequencing, and initial screening of potential bioactive peptides found in the venom of Acanthoscurria rondoniae. This methodology consists in a multiomics approach involving proteomics, peptidomics and transcriptomics analyses allied to in silico predictions of antibacterial, antifungal, antiviral, and anticancer activities. Through the application of this strategy, a total of 92,889 venom gland transcripts were assembled and 84 novel toxins were identified at the protein level, including seven short peptides and 10 fully sequenced CRPs (belonging to seven toxin families). In silico analysis suggests that seven CRPs families may have potential antimicrobial or antiviral activities, while two CRPs and four short peptides are potentially anticancer. Taken together, our results demonstrate an effective multiomics strategy for the discovery of new toxins and in silico screening of potential bioactivities. This strategy may be useful in toxin discovery, as well as in the screening of possible activities for the vast diversity of molecules produced by venomous animals.

A multiomics approach unravels new toxins with possible in silico antimicrobial, antiviral, and antitumoral activities in the venom of Acanthoscurria rondoniae

The Araneae order is considered one of the most successful groups among venomous animals in the world. An important factor for this success is the production of venoms, a refined biological fluid rich in proteins, short peptides and cysteine-rich peptides (CRPs). These toxins may present pharmacologically relevant biological actions, as antimicrobial, antiviral and anticancer activities, for instance. Therefore, there is an increasing interest in the exploration of venom toxins for therapeutic reasons, such as drug development. However, the process of peptide sequencing and mainly the evaluation of potential biological activities of these peptides are laborious, considering the low yield of venom extraction and the high variability of toxins present in spider venoms. Here we show a robust methodology for identification, sequencing, and initial screening of potential bioactive peptides found in the venom of Acanthoscurria rondoniae. This methodology consists in a multiomics approach involving proteomics, peptidomics and transcriptomics analyses allied to in silico predictions of antibacterial, antifungal, antiviral, and anticancer activities. Through the application of this strategy, a total of 92,889 venom gland transcripts were assembled and 84 novel toxins were identified at the protein level, including seven short peptides and 10 fully sequenced CRPs (belonging to seven toxin families). In silico analysis suggests that seven CRPs families may have potential antimicrobial or antiviral activities, while two CRPs and four short peptides are potentially anticancer. Taken together, our results demonstrate an effective multiomics strategy for the discovery of new toxins and in silico screening of potential bioactivities. This strategy may be useful in toxin discovery, as well as in the screening of possible activities for the vast diversity of molecules produced by venomous animals.