SEEI: spherical evolution with feedback mechanism for identifying epistatic interactions.

BACKGROUND: Detecting epistatic interactions (EIs) involves the exploration of associations among single nucleotide polymorphisms (SNPs) and complex diseases, which is an important task in genome-wide association studies. The EI detection problem is dependent on epistasis models and corresponding optimization methods. Although various models and methods have been proposed to detect EIs, identifying EIs efficiently and accurately is still a challenge. RESULTS: Here, we propose a linear mixed statistical epistasis model (LMSE) and a spherical evolution approach with a feedback mechanism (named SEEI). The LMSE model expands the existing single epistasis models such as LR-Score, K2-Score, Mutual information, and Gini index. The SEEI includes an adaptive spherical search strategy and population updating strategy, which ensures that the algorithm is not easily trapped in local optima. We analyzed the performances of 8 random disease models, 12 disease models with marginal effects, 30 disease models without marginal effects, and 10 high-order disease models. The 60 simulated disease models and a real breast cancer dataset were used to evaluate eight algorithms (SEEI, EACO, EpiACO, FDHEIW, MP-HS-DHSI, NHSA-DHSC, SNPHarvester, CSE). Three evaluation criteria (pow1, pow2, pow3), a T-test, and a Friedman test were used to compare the performances of these algorithms. The results show that the SEEI algorithm (order 1, averages ranks = 13.125) outperformed the other algorithms in detecting EIs. CONCLUSIONS: Here, we propose an LMSE model and an evolutionary computing method (SEEI) to solve the optimization problem of the LMSE model. The proposed method performed better than the other seven algorithms tested in its ability to identify EIs in genome-wide association datasets. We identified new SNP-SNP combinations in the real breast cancer dataset and verified the results. Our findings provide new insights for the diagnosis and treatment of breast cancer. AVAILABILITY AND IMPLEMENTATION: https://github.com/scutdy/SSO/blob/master/SEEI.zip .

Poly-l-lysine modified MOF nanoparticles with pH/ROS sensitive CIP release and CUR triggered photodynamic therapy against drug-resistant bacterial infection.

The challenge of drug resistance in bacteria caused by the over use of biotics is increasing during the therapy process, which has attracted great attentions of the clinicians and scientists around the world. Recently, photodynamic therapy (PDT) triggered by photosensitizer (PS) has become a promising treatment method because of its high efficacy, easy operation, and low side effect. Herein, the poly-l-lysine (PLL) modified metal-organic framework (MOF) nanoparticles, ZIF/PLL-CIP/CUR, were synthesized to allow both reactive oxygen species (ROS) responsive drug release and photodynamic effect for synergistic therapy against drug resistant bacterial infections. The PLL was modified on the shell of the zeolite imidazole framework (ZIF) by the ROS-responsive thioketal linker for controllable CIP release. CUR were encapsulated in ZIF as the photosensitizer for blue light mediated photodynamic effect to produce singlet oxygen (1O2) and superoxide anion radical (O2-) for efficient inhibition towards methicillin-resistant Staphylococcus aureus (MRSA). The charge conversion from negative charge (-4.6 mV) to positive charge (2.6 mV) was observed at pH 7.4 and pH 5.5, and 70.9 % CIP was found released at pH 5.5 in the presence of H2O2, which suggests the good biosafety at physiological pH and ROS-responsive drug release of the as-prepared nanoparticle in the bacterial microenvironment. The as-prepared nanoparticles could effectively kill MRSA and disrupt bacterial biofilm by combination of chemo- and photodynamic therapy. In mice model, the as-prepared nanoparticles exhibited excellent biosafety and synergistic effect with 98.81 % healing rate in treatment of MRSA infection, which is considered as a promising candidate in combating drug resistant bacterial infection.