Role of efflux pumps, their inhibitors, and regulators in colistin resistance.

Colistin is highly promising against multidrug-resistant and extensively drug-resistant bacteria clinically. Bacteria are resistant to colistin mainly through mcr and chromosome-mediated lipopolysaccharide (LPS) synthesis-related locus variation. However, the current understanding cannot fully explain the resistance mechanism in mcr-negative colistin-resistant strains. Significantly, the contribution of efflux pumps to colistin resistance remains to be clarified. This review aims to discuss the contribution of efflux pumps and their related transcriptional regulators to colistin resistance in various bacteria and the reversal effect of efflux pump inhibitors on colistin resistance. Previous studies suggested a complex regulatory relationship between the efflux pumps and their transcriptional regulators and LPS synthesis, transport, and modification. Carbonyl cyanide 3-chlorophenylhydrazone (CCCP), 1-(1-naphthylmethyl)-piperazine (NMP), and Phe-Arg-ß-naphthylamide (PAßN) all achieved the reversal of colistin resistance, highlighting the role of efflux pumps in colistin resistance and their potential for adjuvant development. The contribution of the efflux pumps to colistin resistance might also be related to specific genetic backgrounds. They can participate in colistin tolerance and heterogeneous resistance to affect the treatment efficacy of colistin. These findings help understand the development of resistance in mcr-negative colistin-resistant strains.

DeepHE: Accurately predicting human essential genes based on deep learning.

Accurately predicting essential genes using computational methods can greatly reduce the effort in finding them via wet experiments at both time and resource scales, and further accelerate the process of drug discovery. Several computational methods have been proposed for predicting essential genes in model organisms by integrating multiple biological data sources either via centrality measures or machine learning based methods. However, the methods aiming to predict human essential genes are still limited and the performance still need improve. In addition, most of the machine learning based essential gene prediction methods are lack of skills to handle the imbalanced learning issue inherent in the essential gene prediction problem, which might be one factor affecting their performance. We propose a deep learning based method, DeepHE, to predict human essential genes by integrating features derived from sequence data and protein-protein interaction (PPI) network. A deep learning based network embedding method is utilized to automatically learn features from PPI network. In addition, 89 sequence features were derived from DNA sequence and protein sequence for each gene. These two types of features are integrated to train a multilayer neural network. A cost-sensitive technique is used to address the imbalanced learning problem when training the deep neural network. The experimental results for predicting human essential genes show that our proposed method, DeepHE, can accurately predict human gene essentiality with an average performance of AUC higher than 94%, the area under precision-recall curve (AP) higher than 90%, and the accuracy higher than 90%. We also compare DeepHE with several widely used traditional machine learning models (SVM, Naïve Bayes, Random Forest, and Adaboost) using the same features and utilizing the same cost-sensitive technique to against the imbalanced learning issue. The experimental results show that DeepHE significantly outperforms the compared machine learning models. We have demonstrated that human essential genes can be accurately predicted by designing effective machine learning algorithm and integrating representative features captured from available biological data. The proposed deep learning framework is effective for such task.