Dynamic Proteomic Changes in Tumor and Immune Organs Reveal Systemic Immune Response to Tumor Development.

In orthotopic mouse tumor models, tumor progression is a complex process, involving interactions among tumor cells, host cell-derived stromal cells, and immune cells. Much attention has been focused on the tumor and its tumor microenvironment, while the host's macroenvironment including immune organs in response to tumorigenesis is poorly understood. Here, we report a temporal proteomic analysis on a subcutaneous tumor and three immune organs (LN, MLN, and spleen) collected on Days 0, 3, 7, 10, 14, and 21 after inoculation of mouse forestomach cancer cells in a syngeneic mouse model. Bioinformatics analysis identified key biological processes during distinct tumor development phases, including an initial acute immune response, the attack by the host immune system, followed by the adaptive immune activation, and the build-up of extracellular matrix. Proteomic changes in LN and spleen largely recapitulated the dynamics of the immune response in the tumor, consistent with an acute defense response on D3, adaptive immune response on D10, and immune evasion by D21. In contrast, the immune response in MLN showed a gradual and sustained activation, suggesting a delayed response from a distal immune organ. Combined analyses of tumors and host immune organs allowed the identification of potential therapeutic targets. A proof-of-concept experiment demonstrated that significant growth reduction can be achieved by dual inhibition of MEK and DDR2. Together, our temporal proteomic dataset of tumors and immune organs provides a useful resource for understanding the interaction between tumors and the immune system and has the potential for identifying new therapeutic targets for cancer treatment.

SAMP: Identifying Antimicrobial Peptides by an Ensemble Learning Model Based on Proportionalized Split Amino Acid Composition.

It is projected that 10 million deaths could be attributed to drug-resistant bacteria infections in 2050. To address this concern, identifying new-generation antibiotics is an effective way. Antimicrobial peptides (AMPs), a class of innate immune effectors, have received significant attention for their capacity to eliminate drug-resistant pathogens, including viruses, bacteria, and fungi. Recent years have witnessed widespread applications of computational methods especially machine learning (ML) and deep learning (DL) for discovering AMPs. However, existing methods only use features including compositional, physiochemical, and structural properties of peptides, which cannot fully capture sequence information from AMPs. Here, we present SAMP, an ensemble random projection (RP) based computational model that leverages a new type of features called Proportionalized Split Amino Acid Composition (PSAAC) in addition to conventional sequence-based features for AMP prediction. With this new feature set, SAMP captures the residue patterns like sorting signals at around both the N-terminus and the C-terminus, while also retaining the sequence order information from the middle peptide fragments. Benchmarking tests on different balanced and imbalanced datasets demonstrate that SAMP consistently outperforms existing state-of-the-art methods, such as iAMPpred and AMPScanner V2, in terms of accuracy, MCC, G-measure and F1-score. In addition, by leveraging an ensemble RP architecture, SAMP is scalable to processing large-scale AMP identification with further performance improvement, compared to those models without RP. To facilitate the use of SAMP, we have developed a Python package freely available at https://github.com/wan-mlab/SAMP.

It Takes Two to Tangle: Microneedle Patches Co-delivering Monoclonal Antibodies and Engineered Antimicrobial Peptides Effectively Eradicate Wound Biofilms.

Wound biofilms pose a great clinical challenge. Herein, this work reports a dissolvable microneedle patch for dual delivery of monoclonal antibodies anti-PBP2a and engineers antimicrobial peptides W379. In vitro antibacterial efficacy testing with microneedle patches containing a combination of 250 ng mL-1 W379 and 250 ng mL-1 anti-BPB2a decreases the bacterial count from ≈3.31 × 107 CFU mL-1 to 1.28 × 102 CFU mL-1 within 2 h without eliciting evident cytotoxicity. Ex vivo testing indicates W379 and anti-PBP2a co-loaded microneedle patch displayed a remarkable reduction of bacterial load by ≈7.18 log CFU after administered only once within 48 h. The bacterial count is significantly diminished compared to the treatment by either W379 or anti-PBP2a-loaded alone microneedle patches. When administered twice within 48 h, no bacteria are identified. Further in vivo study also reveals that after two treatments of W379 and anti-PBP2a co-loaded PVP microneedle patches within 48 h, the bacterial colonies are undetectable in a type II diabetic mouse wound biofilm model. Taken together, W379 and anti-PBP2a co-loaded PVP microneedle patches hold great promise in treating wound biofilms.