The identification and validation of drug targets are crucial in biomedical research and many studies have been conducted on analyzing drug target features for getting a better understanding on principles of their mechanisms. But most of them are based on either strong biological hypotheses or the chemical and physical properties of those targets separately. In this paper, we investigated three main ways to understand the functional biomolecules based on the topological features of drug targets. There are no significant differences between targets and common proteins in the protein-protein interactions network, indicating the drug targets are neither hub proteins which are dominant nor the bridge proteins. According to some special topological structures of the drug targets, there are significant differences between known targets and other proteins. Furthermore, the drug targets mainly belong to three typical communities based on their modularity. These topological features are helpful to understand how the drug targets work in the PPI network. Particularly, it is an alternative way to predict potential targets or extract nontargets to test a new drug target efficiently and economically. By this way, a drug target’s homologue set containing 102 potential target proteins is predicted in the paper.

Immune cells have the potential to control the growth of tumor. However, this effect could be offset by immunosuppression associated with an increased production of reactive oxygen species. Multiple studies indicate that the antitumor effect of immune cells is correlated with their antioxidant capacity. This review discusses the role of reactive oxygen species in the tumor microenvironment by describing their distinct effects on different immune cells, including myeloid-derived suppressor cells, regulatory T cells, tumor-associated macrophages, cytotoxic T lymphocytes, natural killer cells, and dendritic cells. In the end, we conclude with the prospect of treatment for cancer by targeting antioxidant defense in immune cells.

The identification of drug target proteins (IDTP) plays a critical role in biometrics. The aim of this study was to retrieve potential drug target proteins (DTPs) from a collected protein dataset, which represents an overwhelming task of great significance. Previously reported methodologies for this task generally employ protein-protein interactive networks but neglect informative biochemical attributes. We formulated a novel framework utilizing biochemical attributes to address this problem. In the framework, a biased support vector machine (BSVM) was combined with the deep embedded representation extracted using a deep learning model, stacked auto-encoders (SAEs). In cases of non-drug target proteins (NDTPs) contaminated by DTPs, the framework is beneficial due to the efficient representation of the SAE and relief of the imbalance effect by the BSVM. The experimental results demonstrated the effectiveness of our framework, and the generalization capability was confirmed via comparisons to other models. This study is the first to exploit a deep learning model for IDTP. In summary, nearly 23% of the NDTPs were predicted as likely DTPs, which are awaiting further verification based on biomedical experiments.

Background: Although chimeric antigen receptor (CAR)-T cell therapy has been remarkably successful for haematological malignancies, its efficacy against solid tumors is limited. The combination of CAR-T cell therapy with immune checkpoint inhibitors (CPIs), such as PD-1, PD-L1, and CTLA-4 antibodies, is a promising strategy for enhancing the antitumor efficacy of CAR-T cells. However, because most patients acquire resistance to CPIs, investigating other strategies is necessary to further improve the antitumor efficacy of CAR-T cell therapy for solid tumors. Recently, CD40 agonist antibodies showed potential antitumor efficacy by activating the CD40 pathway. Results: Based on the piggyBac transposon system, rather than the widely used viral vectors, we constructed a meso3-CD40 CAR-T targeting region III of mesothelin (MSLN) that possessed the ability to secrete anti-CD40 antibodies. Compared with meso3 CAR-T cells, which did not secrete the anti-CD40 antibody, meso3-CD40 CAR-T cells secreted more cytokines and had a relatively higher proportion of central memory T (T_CM) cells after stimulation by the target antigen. In addition, compared with meso3 CAR-T cells, meso3-CD40 CAR-T cells had a more powerful cytotoxic effect on target cells at a relatively low effector-to-target ratio. More importantly, we demonstrated that the antitumor activity of meso3-CD40 CAR-T cells was enhanced in a human ovarian cancer xenograft model in vivo. Conclusions: In conclusion, these results highlight anti-CD40-secreting CAR-T cells generated by nonviral vectors as a potential clinical strategy for improving the efficacy of CAR-T cell therapies.

Construction and analyses of tissue specific networks is crucial to unveil the function and organizational structure of biological systems. As a direct method to detect protein dynamics, human proteome-wide expression data provide an valuable resource to investigate the tissue specificity of proteins and interactions. By integrating protein expression data with large-scale interaction network, we constructed 30 tissue/cell specific networks in human and analyzed their properties and functions. Rather than the tissue specificity of proteins, we mainly focused on the tissue specificity of interactions to distill tissue specific networks. Through comparing our tissue specific networks with those inferred from gene expression data, we found our networks have larger scales and higher reliability. Furthermore, we investigated the similar extent of multiple tissue specific networks, which proved that tissues with similar functions tend to contain more common interactions. Finally, we found that the tissue specific networks differed from the static network in multiple topological properties. The proteins in tissue specific networks are interacting looser and the hubs play more important roles than those in the static network.

The physical properties of gene products are the foundation of their biological functions. In this study, we systematically explored relationships between physical properties and biological functions. The physical properties including origin time, evolution pressure, mRNA and protein stability, molecular weight, hydrophobicity, acidity/alkaline, amino acid compositions, and chromosome location. The biological functions are defined from 4 aspects: biological process, molecular function, cellular component and cell/tissue/organ expression. We found that the proteins associated with basic material and energy metabolism process originated earlier, while the proteins associated with immune, neurological system process etc. originated later. Tissues may have a strong influence on evolution pressure. The proteins associated with energy metabolism are double-stable. Immune and peripheral cell proteins tend to be mRNA stable/protein unstable. There are very few function items with double-unstable of mRNA and protein. The proteins involved in the cell adhesion tend to consist of large proteins with high proportion of small amino acids. The proteins of organic acid transport, neurological system process and amine transport have significantly high hydrophobicity. Interestingly, the proteins involved in olfactory receptor activity tend to have high frequency of aromatic, sulfuric and hydroxyl amino acids.