An interpretable artificial intelligence framework for designing synthetic lethality-based anti-cancer combination therapies.

INTRODUCTION: Synthetic lethality (SL) provides an opportunity to leverage different genetic interactions when designing synergistic combination therapies. To further explore SL-based combination therapies for cancer treatment, it is important to identify and mechanistically characterize more SL interactions. Artificial intelligence (AI) methods have recently been proposed for SL prediction, but the results of these models are often not interpretable such that deriving the underlying mechanism can be challenging. OBJECTIVES: This study aims to develop an interpretable AI framework for SL prediction and subsequently utilize it to design SL-based synergistic combination therapies. METHODS: We propose a knowledge and data dual-driven AI framework for SL prediction (KDDSL). Specifically, we use gene knowledge related to the SL mechanism to guide the construction of the model and develop a method to identify the most relevant gene knowledge for the predicted results. RESULTS: Experimental and literature-based validation confirmed a good balance between predictive and interpretable ability when using KDDSL. Moreover, we demonstrated that KDDSL could help to discover promising drug combinations and clarify associated biological processes, such as the combination of MDM2 and CDK9 inhibitors, which exhibited significant anti-cancer effects in vitro and in vivo. CONCLUSION: These data underscore the potential of KDDSL to guide SL-based combination therapy design. There is a need for biomedicine-focused AI strategies to combine rational biological knowledge with developed models.

Exploring novel disease-disease associations based on multi-view fusion network.

Established taxonomy system based on disease symptom and tissue characteristics have provided an important basis for physicians to correctly identify diseases and treat them successfully. However, these classifications tend to be based on phenotypic observations, lacking a molecular biological foundation. Therefore, there is an urgent to integrate multi-dimensional molecular biological information or multi-omics data to redefine disease classification in order to provide a powerful perspective for understanding the molecular structure of diseases. Therefore, we offer a flexible disease classification that integrates the biological process, gene expression, and symptom phenotype of diseases, and propose a disease-disease association network based on multi-view fusion. We applied the fusion approach to 223 diseases and divided them into 24 disease clusters. The contribution of internal and external edges of disease clusters were analyzed. The results of the fusion model were compared with Medical Subject Headings, a traditional and commonly used disease taxonomy. Then, experimental results of model performance comparison show that our approach performs better than other integration methods. As it was observed, the obtained clusters provided more interesting and novel disease-disease associations. This multi-view human disease association network describes relationships between diseases based on multiple molecular levels, thus breaking through the limitation of the disease classification system based on tissues and organs. This approach which motivates clinicians and researchers to reposition the understanding of diseases and explore diagnosis and therapy strategies, extends the existing disease taxonomy. Availability of data and materials: The preprocessed dataset and source code supporting the conclusions of this article are available at GitHub repository https://github.com/yangxiaoxi89/mvHDN.

A benchmark study of deep learning-based multi-omics data fusion methods for cancer.

BACKGROUND: A fused method using a combination of multi-omics data enables a comprehensive study of complex biological processes and highlights the interrelationship of relevant biomolecules and their functions. Driven by high-throughput sequencing technologies, several promising deep learning methods have been proposed for fusing multi-omics data generated from a large number of samples. RESULTS: In this study, 16 representative deep learning methods are comprehensively evaluated on simulated, single-cell, and cancer multi-omics datasets. For each of the datasets, two tasks are designed: classification and clustering. The classification performance is evaluated by using three benchmarking metrics including accuracy, F1 macro, and F1 weighted. Meanwhile, the clustering performance is evaluated by using four benchmarking metrics including the Jaccard index (JI), C-index, silhouette score, and Davies Bouldin score. For the cancer multi-omics datasets, the methods' strength in capturing the association of multi-omics dimensionality reduction results with survival and clinical annotations is further evaluated. The benchmarking results indicate that moGAT achieves the best classification performance. Meanwhile, efmmdVAE, efVAE, and lfmmdVAE show the most promising performance across all complementary contexts in clustering tasks. CONCLUSIONS: Our benchmarking results not only provide a reference for biomedical researchers to choose appropriate deep learning-based multi-omics data fusion methods, but also suggest the future directions for the development of more effective multi-omics data fusion methods. The deep learning frameworks are available at https://github.com/zhenglinyi/DL-mo .

Computational methods, databases and tools for synthetic lethality prediction.

Synthetic lethality (SL) occurs between two genes when the inactivation of either gene alone has no effect on cell survival but the inactivation of both genes results in cell death. SL-based therapy has become one of the most promising targeted cancer therapies in the last decade as PARP inhibitors achieve great success in the clinic. The key point to exploiting SL-based cancer therapy is the identification of robust SL pairs. Although many wet-lab-based methods have been developed to screen SL pairs, known SL pairs are less than 0.1% of all potential pairs due to large number of human gene combinations. Computational prediction methods complement wet-lab-based methods to effectively reduce the search space of SL pairs. In this paper, we review the recent applications of computational methods and commonly used databases for SL prediction. First, we introduce the concept of SL and its screening methods. Second, various SL-related data resources are summarized. Then, computational methods including statistical-based methods, network-based methods, classical machine learning methods and deep learning methods for SL prediction are summarized. In particular, we elaborate on the negative sampling methods applied in these models. Next, representative tools for SL prediction are introduced. Finally, the challenges and future work for SL prediction are discussed.

Machine learning methods, databases and tools for drug combination prediction.

Combination therapy has shown an obvious efficacy on complex diseases and can greatly reduce the development of drug resistance. However, even with high-throughput screens, experimental methods are insufficient to explore novel drug combinations. In order to reduce the search space of drug combinations, there is an urgent need to develop more efficient computational methods to predict novel drug combinations. In recent decades, more and more machine learning (ML) algorithms have been applied to improve the predictive performance. The object of this study is to introduce and discuss the recent applications of ML methods and the widely used databases in drug combination prediction. In this study, we first describe the concept and controversy of synergism between drug combinations. Then, we investigate various publicly available data resources and tools for prediction tasks. Next, ML methods including classic ML and deep learning methods applied in drug combination prediction are introduced. Finally, we summarize the challenges to ML methods in prediction tasks and provide a discussion on future work.

Determination of the Protein-Protein Interactions within Acyl Carrier Protein (MmcB)-Dependent Modifications in the Biosynthesis of Mitomycin.

Mitomycin has a unique chemical structure and contains densely assembled functionalities with extraordinary antitumor activity. The previously proposed mitomycin C biosynthetic pathway has caused great attention to decipher the enzymatic mechanisms for assembling the pharmaceutically unprecedented chemical scaffold. Herein, we focused on the determination of acyl carrier protein (ACP)-dependent modification steps and identification of the protein-protein interactions between MmcB (ACP) with the partners in the early-stage biosynthesis of mitomycin C. Based on the initial genetic manipulation consisting of gene disruption and complementation experiments, genes mitE, mmcB, mitB, and mitF were identified as the essential functional genes in the mitomycin C biosynthesis, respectively. Further integration of biochemical analysis elucidated that MitE catalyzed CoA ligation of 3-amino-5-hydroxy-bezonic acid (AHBA), MmcB-tethered AHBA triggered the biosynthesis of mitomycin C, and both MitB and MitF were MmcB-dependent tailoring enzymes involved in the assembly of mitosane. Aiming at understanding the poorly characterized protein-protein interactions, the in vitro pull-down assay was carried out by monitoring MmcB individually with MitB and MitF. The observed results displayed the clear interactions between MmcB and MitB and MitF. The surface plasmon resonance (SPR) biosensor analysis further confirmed the protein-protein interactions of MmcB with MitB and MitF, respectively. Taken together, the current genetic and biochemical analysis will facilitate the investigations of the unusual enzymatic mechanisms for the structurally unique compound assembly and inspire attempts to modify the chemical scaffold of mitomycin family antibiotics.

Multi-dimensional data integration algorithm based on random walk with restart.

BACKGROUND: The accumulation of various multi-omics data and computational approaches for data integration can accelerate the development of precision medicine. However, the algorithm development for multi-omics data integration remains a pressing challenge. RESULTS: Here, we propose a multi-omics data integration algorithm based on random walk with restart (RWR) on multiplex network. We call the resulting methodology Random Walk with Restart for multi-dimensional data Fusion (RWRF). RWRF uses similarity network of samples as the basis for integration. It constructs the similarity network for each data type and then connects corresponding samples of multiple similarity networks to create a multiplex sample network. By applying RWR on the multiplex network, RWRF uses stationary probability distribution to fuse similarity networks. We applied RWRF to The Cancer Genome Atlas (TCGA) data to identify subtypes in different cancer data sets. Three types of data (mRNA expression, DNA methylation, and microRNA expression data) are integrated and network clustering is conducted. Experiment results show that RWRF performs better than single data type analysis and previous integrative methods. CONCLUSIONS: RWRF provides powerful support to users to decipher the cancer molecular subtypes, thus may benefit precision treatment of specific patients in clinical practice.

Nonomuraea nitratireducens sp. nov., a new actinobacterium isolated from Suaeda australis Moq. rhizosphere.

A novel actinomycete, designated WYY166T, was isolated from the rhizosphere of Suaeda australis Moq. collected in Dongfang, PR China. The taxonomic position of this strain was investigated using a polyphasic approach. Phylogenetic analysis based on its 16S rRNA gene referred strain WYY166T to the genus Nonomuraea, and it was most closely related to the type strains Nonomuraea candida HMC10T, Nonomuraea turkmeniaca DSM 43926T, Nonomuraea maritima NBRC 106687T and Nonomuraea polychroma DSM 43925T (98.35, 97.60, 97.36 and 97.30% sequence similarity, respectively). Genome sequencing revealed a genome size of 11.27 Mbp and a G+C content of 71.10 mol%. The genome average nucleotide identity (ANI) values and the digital DNA - DNA hybridization (dDDH) values between strain WYY166T and the other species of the genus were found to be low (ANI 81.63~85.23 %, dDDH 23.6~31.6 %), suggesting that it represented a new species. The physiological evaluation showed that it had remarkable nitrate reduction activity. The whole-cell hydrolysates contained meso-diaminopimelic acid and madurose. The N-acyl type of muramic acid was acetyl. The major menaquinones were MK-9 (H4) (86.9 %) and MK-9 (H2) (13.1 %). The predominant fatty acids were iso-C16 : 0 (53.2 %), 10-methyl C17 : 0 (10.7 %), C17 : 1 ω6c (8.3 %) and iso-C16 : 1 h (7.3 %). These physiological, biochemical and chemotaxonomic data suggested that strain WYY166T should be classified as representing a novel species of the genus Nonomuraea, for which the name Nonomuraea nitratireducens sp. nov. is proposed. The type strain is WYY166T (=MCCC 1K03779T=KCTC 49343T).

[Progress on anti-tumor molecular mechanisms of dihydroartemisinin].

Artemisinin is an anti-malarial drug with poor water solubility and oral absorption; so a variety of derivatives based on the parent nucleus have been developed. Compared with artemisinin, dihydroartemisinin (DHA) has a stronger anti-malaria activity, and has the advantages of high metabolic rate and better water solubility. Recent studies have discovered that DHA has a good inhibitory effect on tumor cells, which is closely related to the peroxide bridge in its molecular structure. Since tumor cells need more Fe3+ than normal cells, there are a large number of transferrin receptors on the tumor cell membrane. DHA can break the peroxide bridge in the presence of Fe2+, and the free radicals generated can play its lethal effect on tumor cells. In addition, DHA can promote endocytosis of transferrin receptor, and thus prevent cancer cells from taking Fe3+ from microenvironment. This article reviews the anti-tumor molecular mechanism of DHA, including accelerating oxidative damage, inducing apoptosis, inhibiting the growth, proliferation and invasion of tumor cells, reversing tumor multidrug resistance.

[Progress of antibacterial activity and antibacterial mechanism of isoquinoline alkaloids].

This paper reviewed the antibacterial activity and structure-activity relationship of isoquinoline alkaloids, such as protoberberine, protopine, benzophenanthridine, aporphine, double benzyl isoquinoline, etc. The antibacterial mechanism of alkaloids were illustrated from cell wall and membrane damage, membrane permeability changes, related enzymes and efflux pump inhibition, influence of bacterial DNA and related protein synthesis and so on. In addition, this paper summarized the structure-activity relationship of isoquinoline alkaloids. In order to improve the screening efficiency of drug targets, the complementary effect of biological information science and combinatorial chemistry should be developed abundantly in the development of natural product. This paper will provide a theoretical reference for the research and development of new antibacterial agent.