AutophagySMDB: a curated database of small molecules that modulate protein targets regulating autophagy

Modulating autophagy to treat diseases: A revisited review on in silico methods

BACKGROUND

Autophagy refers to the conserved cellular catabolic process relevant to lysosome activity and plays a vital role in maintaining the dynamic equilibrium of intracellular matter by degrading harmful and abnormally accumulated cellular components. Accumulating evidence has recently revealed that dysregulation of autophagy by genetic and exogenous interventions may disrupt cellular homeostasis in human diseases. In silico approaches as powerful aids to experiments have also been extensively reported to play their critical roles in the storage, prediction, and analysis of massive amounts of experimental data. Thus, modulating autophagy to treat diseases by in silico methods would be anticipated.

AIM OF REVIEW

Here, we focus on summarizing the updated in silico approaches including databases, systems biology network approaches, omics-based analyses, mathematical models, and artificial intelligence (AI) methods that sought to modulate autophagy for potential therapeutic purposes, which will provide a new insight into more promising therapeutic strategies.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Autophagy-related databases are the data basis of the in silico method, storing a large amount of information about DNA, RNA, proteins, small molecules and diseases. The systems biology approach is a method to systematically study the interrelationships among biological processes including autophagy from a macroscopic perspective. Omics-based analyses are based on high-throughput data to analyze gene expression at different levels of biological processes involving autophagy. mathematical models are visualization methods to describe the dynamic process of autophagy, and its accuracy is related to the selection of parameters. AI methods use big data related to autophagy to predict autophagy targets, design targeted small molecules, and classify diverse human diseases for potential therapeutic applications.

AutophagyNet: high-resolution data source for the analysis of autophagy and its regulation

Macroautophagy/autophagy is a highly-conserved catabolic procss eliminating dysfunctional cellular components and invading pathogens. Autophagy malfunction contributes to disorders such as cancer, neurodegenerative and inflammatory diseases. Understanding autophagy regulation in health and disease has been the focus of the last decades. We previously provided an integrated database for autophagy research, the Autophagy Regulatory Network (ARN). For the last eight years, this resource has been used by thousands of users. Here, we present a new and upgraded resource, AutophagyNet. It builds on the previous database but contains major improvements to address user feedback and novel needs due to the advancement in omics data availability. AutophagyNet contains updated interaction curation and integration of over 280,000 experimentally verified interactions between core autophagy proteins and their protein, transcriptional and post-transcriptional regulators as well as their potential upstream pathway connections. AutophagyNet provides annotations for each core protein about their role: 1) in different types of autophagy (mitophagy, xenophagy, etc.); 2) in distinct stages of autophagy (initiation, expansion, termination, etc.); 3) with subcellular and tissue-specific localization. These annotations can be used to filter the dataset, providing customizable download options tailored to the user's needs. The resource is available in various file formats (e.g. CSV, BioPAX and PSI-MI), and data can be analyzed and visualized directly in Cytoscape. The multi-layered regulation of autophagy can be analyzed by combining AutophagyNet with tissue- or cell type-specific (multi-)omics datasets (e.g. transcriptomic or proteomic data). The resource is publicly accessible at http://autophagynet.org.Abbreviations: ARN: Autophagy Regulatory Network; ATG: autophagy related; BCR: B cell receptor pathway; BECN1: beclin 1; GABARAP: GABA type A receptor-associated protein; IIP: innate immune pathway; LIR: LC3-interacting region; lncRNA: long non-coding RNA; MAP1LC3B: microtubule associated protein 1 light chain 3 beta; miRNA: microRNA; NHR: nuclear hormone receptor; PTM: post-translational modification; RTK: receptor tyrosine kinase; TCR: T cell receptor; TLR: toll like receptor.

Metformin beyond an anti-diabetic agent: A comprehensive and mechanistic review on its effects against natural and chemical toxins

In addition to the anti-diabetic effect of metformin, a growing number of studies have shown that metformin has some exciting properties, such as anti-oxidative capabilities, anticancer, genomic stability, anti-inflammation, and anti-fibrosis, which have potent, that can treat other disorders other than diabetes mellitus. We aimed to describe and review the protective and antidotal efficacy of metformin against biologicals, chemicals, natural, medications, pesticides, and radiation-induced toxicities. A comprehensive search has been performed from Scopus, Web of Science, PubMed, and Google Scholar databases from inception to March 8, 2023. All in vitro, in vivo, and clinical studies were considered. Many studies suggest that metformin affects diseases other than diabetes. It is a radioprotective and chemoprotective drug that also affects viral and bacterial diseases. It can be used against inflammation-related and apoptosis-related abnormalities and against toxins to lower their effects. Besides lowering blood sugar, metformin can attenuate the effects of toxins on body weight, inflammation, apoptosis, necrosis, caspase-3 activation, cell viability and survival rate, reactive oxygen species (ROS), NF-κB, TNF-α, many interleukins, lipid profile, and many enzymes activity such as catalase and superoxide dismutase. It also can reduce the histopathological damages induced by many toxins on the kidneys, liver, and colon. However, clinical trials and human studies are needed before using metformin as a therapeutic agent against other diseases.

Systems biology of autophagy in leishmanial infection and its diverse role in precision medicine

Autophagy is a contentious issue in leishmaniasis and is emerging as a promising therapeutic regimen. Published research on the impact of autophagic regulation on Leishmania survival is inconclusive, despite numerous pieces of evidence that Leishmania spp. triggers autophagy in a variety of cell types. The mechanistic approach is poorly understood in the Leishmania parasite as autophagy is significant in both Leishmania and the host. Herein, this review discusses the autophagy proteins that are being investigated as potential therapeutic targets, the connection between autophagy and lipid metabolism, and microRNAs that regulate autophagy and lipid metabolism. It also highlights the use of systems biology to develop novel autophagy-dependent therapeutics for leishmaniasis by utilizing artificial intelligence (AI), machine learning (ML), mathematical modeling, network analysis, and other computational methods. Additionally, we have shown many databases for autophagy and metabolism in Leishmania parasites that suggest potential therapeutic targets for intricate signaling in the autophagy system. In a nutshell, the detailed understanding of the dynamics of autophagy in conjunction with lipids and miRNAs unfolds larger dimensions for future research.

New Visions on Natural Products and Cancer Therapy: Autophagy and Related Regulatory Pathways

Macroautophagy (autophagy) has been a highly conserved process throughout evolution and allows cells to degrade aggregated/misfolded proteins, dysfunctional or superfluous organelles and damaged macromolecules, in order to recycle them for biosynthetic and/or energetic purposes to preserve cellular homeostasis and health. Changes in autophagy are indeed correlated with several pathological disorders such as neurodegenerative and cardiovascular diseases, infections, cancer and inflammatory diseases. Conversely, autophagy controls both apoptosis and the unfolded protein response (UPR) in the cells. Therefore, any changes in the autophagy pathway will affect both the UPR and apoptosis. Recent evidence has shown that several natural products can modulate (induce or inhibit) the autophagy pathway. Natural products may target different regulatory components of the autophagy pathway, including specific kinases or phosphatases. In this review, we evaluated ~100 natural compounds and plant species and their impact on different types of cancers via the autophagy pathway. We also discuss the impact of these compounds on the UPR and apoptosis via the autophagy pathway. A multitude of preclinical findings have shown the function of botanicals in regulating cell autophagy and its potential impact on cancer therapy; however, the number of related clinical trials to date remains low. In this regard, further pre-clinical and clinical studies are warranted to better clarify the utility of natural compounds and their modulatory effects on autophagy, as fine-tuning of autophagy could be translated into therapeutic applications for several cancers.

AMTDB: A comprehensive database of autophagic modulators for anti-tumor drug discovery

Autophagy, originally described as a mechanism for intracellular waste disposal and recovery, has been becoming a crucial biological process closely related to many types of human tumors, including breast cancer, osteosarcoma, glioma, etc., suggesting that intervention of autophagy is a promising therapeutic strategy for cancer drug development. Therefore, a high-quality database is crucial for unraveling the complicated relationship between autophagy and human cancers, elucidating the crosstalk between the key autophagic pathways, and autophagic modulators with their remarkable antitumor activities. To achieve this goal, a comprehensive database of autophagic modulators (AMTDB) was developed. AMTDB focuses on 153 cancer types, 1,153 autophagic regulators, 860 targets, and 2,046 mechanisms/signaling pathways. In addition, a variety of classification methods, advanced retrieval, and target prediction functions are provided exclusively to cater to the different demands of users. Collectively, AMTDB is expected to serve as a powerful online resource to provide a new clue for the discovery of more candidate cancer drugs.

Modeling Structure-Activity Relationship of AMPK Activation

The adenosine monophosphate activated protein kinase (AMPK) is critical in the regulation of important cellular functions such as lipid, glucose, and protein metabolism; mitochondrial biogenesis and autophagy; and cellular growth. In many diseases-such as metabolic syndrome, obesity, diabetes, and also cancer-activation of AMPK is beneficial. Therefore, there is growing interest in AMPK activators that act either by direct action on the enzyme itself or by indirect activation of upstream regulators. Many natural compounds have been described that activate AMPK indirectly. These compounds are usually contained in mixtures with a variety of structurally different other compounds, which in turn can also alter the activity of AMPK via one or more pathways. For these compounds, experiments are complicated, since the required pure substances are often not yet isolated and/or therefore not sufficiently available. Therefore, our goal was to develop a screening tool that could handle the profound heterogeneity in activation pathways of the AMPK. Since machine learning algorithms can model complex (unknown) relationships and patterns, some of these methods (random forest, support vector machines, stochastic gradient boosting, logistic regression, and deep neural network) were applied and validated using a database, comprising of 904 activating and 799 neutral or inhibiting compounds identified by extensive PubMed literature search and PubChem Bioassay database. All models showed unexpectedly high classification accuracy in training, but more importantly in predicting the unseen test data. These models are therefore suitable tools for rapid in silico screening of established substances or multicomponent mixtures and can be used to identify compounds of interest for further testing.

Host factors subverted by Mycobacterium tuberculosis: Potential targets for host directed therapy

INTRODUCTION

Despite new approaches in the diagnosis and treatment of tuberculosis (TB), it continues to be a major health burden. Several immunotherapies that potentiate the immune response have come up as adjuncts to drug therapies against drug resistant TB strains; however, there needs to be an urgent appraisal of host specific drug targets for improving their clinical management and to curtail disease progression. Presently, various host directed therapies (HDTs) exist (repurposed drugs, nutraceuticals, monoclonal antibodies and immunomodulatory agents), but these mostly address molecules that combat disease progression.

AREAS COVERED

The current review discusses major Mycobacterium tuberculosis (M. tuberculosis) survival paradigms inside the host and presents a plethora of host targets subverted by M. tuberculosis which can be further explored for future HDTs. The host factors unique to M. tuberculosis infection (in humans) have also been identified through an in-silico interaction mapping.

EXPERT OPINION

HDTs could become the next-generation adjunct therapies in order to counter antimicrobial resistance and virulence, as well as to reduce the duration of existing TB treatments. However, current scientific efforts are largely directed toward combatants rather than host molecules co-opted by M. tuberculosis for its survival. This might drive the immune system to a hyper-inflammatory condition; therefore, we emphasize that host factors subverted by M. tuberculosis, and their subsequent neutralization, must be considered for development of better HDTs.

Small molecule probes for targeting autophagy

Autophagy is implicated in a wide range of (patho)physiological processes including maintenance of cellular homeostasis, neurodegenerative disorders, aging and cancer. As such, small molecule autophagy modulators are in great demand, both for their ability to act as tools to better understand this essential process and as potential therapeutics. Despite substantial advances in the field, major challenges remain in the development and comprehensive characterization of probes that are specific to autophagy. In this Review, we discuss recent developments in autophagy-modulating small molecules, including the specific challenges faced in the development of activators and inhibitors, and recommend guidelines for their use. Finally, we discuss the potential to hijack the process for targeted protein degradation, an area of great importance in chemical biology and drug discovery.

Metformin alleviates cisplatin-induced ototoxicity by autophagy induction possibly via the AMPK/FOXO3a pathway

Cisplatin is an antitumor drug that is widely used for the treatment of various solid tumors. Unfortunately, patients are often troubled by serious side effects, especially hearing loss. Up to now, there have been no clear and effective measures to prevent cisplatin-induced ototoxicity in clinical use. We explored the role of autophagy and the efficacy of metformin in cisplatin-induced ototoxicity in cells, zebrafish, and mice. Furthermore, the underlying molecular mechanism of how metformin affects cisplatin-induced ototoxicity was examined. In in vitro experiments, autophagy levels in HEI-OC1 cells were assessed using fluorescence and Western blot analyses. In in vivo experiments, whether metformin had a protective effect against cisplatin ototoxicity was validated in zebrafish and C57BL/6 mice. The results showed that cisplatin induced autophagy activation in HEI-OC1 cells. Metformin exerted antagonistic effects against cisplatin ototoxicity in HEI-OC1 cells, zebrafish, and mice. Notably, metformin activated autophagy and increased the expression levels of the adenosine monophosphate-activated protein kinase (AMPK) and the transcription factor Forkhead box protein O3 (FOXO3a), whereas cells with AMPK silencing displayed otherwise. Our findings indicate that metformin alleviates cisplatin-induced ototoxicity possibly through AMPK/FOXO3a-mediated autophagy machinery. This study underpins further researches on the prevention and treatment of cisplatin ototoxicity.NEW & NOTEWORTHY Cisplatin is an antitumor drug that is widely used for the treatment of various solid tumors. Up to now, there have been no clear and effective measures to prevent cisplatin-induced ototoxicity in clinical use. We investigated the protective effect of metformin on cisplatin ototoxicity in vitro and in vivo. Our findings indicate that metformin alleviates cisplatin-induced ototoxicity possibly through AMPK/FOXO3a-mediated autophagy machinery. This study underpins further researches on the prevention and treatment of cisplatin ototoxicity.

Modulatory Effects of Autophagy on APP Processing as a Potential Treatment Target for Alzheimer's Disease

Alzheimer’s disease (AD) is characterized by the formation of intracellular aggregate composed of heavily phosphorylated tau protein and extracellular deposit of amyloid-β (Aβ) plaques derived from proteolysis cleavage of amyloid precursor protein (APP). Autophagy refers to the lysosomal-mediated degradation of cytoplasmic constituents, which plays a critical role in maintaining cellular homeostasis. Importantly, recent studies reported that dysregulation of autophagy is associated in the pathogenesis of AD, and therefore, autophagy modulation has gained attention as a promising approach to treat AD pathogenesis. In AD, both the maturation of autolysosomes and its retrograde transports have been obstructed, which causes the accumulation of autophagic vacuoles and eventually leads to degenerating and dystrophic neurites function. However, the mechanism of autophagy modulation in APP processing and its pathogenesis have not yet been fully elucidated in AD. In the early stage of AD, APP processing and Aβ accumulation-mediated autophagy facilitate the removal of toxic protein aggregates via mTOR-dependent and -independent pathways. In addition, a number of autophagy-related genes (Atg) and APP are thought to influence the development of AD, providing a bidirectional link between autophagy and AD pathology. In this review, we summarized the current observations related to autophagy regulation and APP processing in AD, focusing on their modulation associated with the AD progression. Moreover, we emphasizes the application of small molecules and natural compounds to modulate autophagy for the removal and clearance of APP and Aβ deposits in the pathological condition of AD.

Tracing the footsteps of autophagy in computational biology

Autophagy plays a crucial role in maintaining cellular homeostasis through the degradation of unwanted materials like damaged mitochondria and misfolded proteins. However, the contribution of autophagy toward a healthy cell environment is not only limited to the cleaning process. It also assists in protein synthesis when the system lacks the amino acids’ inflow from the extracellular environment due to diet consumptions. Reduction in the autophagy process is associated with diseases like cancer, diabetes, non-alcoholic steatohepatitis, etc., while uncontrolled autophagy may facilitate cell death. We need a better understanding of the autophagy processes and their regulatory mechanisms at various levels (molecules, cells, tissues). This demands a thorough understanding of the system with the help of mathematical and computational tools. The present review illuminates how systems biology approaches are being used for the study of the autophagy process. A comprehensive insight is provided on the application of computational methods involving mathematical modeling and network analysis in the autophagy process. Various mathematical models based on the system of differential equations for studying autophagy are covered here. We have also highlighted the significance of network analysis and machine learning in capturing the core regulatory machinery governing the autophagy process. We explored the available autophagic databases and related resources along with their attributes that are useful in investigating autophagy through computational methods. We conclude the article addressing the potential future perspective in this area, which might provide a more in-depth insight into the dynamics of autophagy.

Mechanisms of Action of Autophagy Modulators Dissected by Quantitative Systems Pharmacology Analysis

Autophagy plays an essential role in cell survival/death and functioning. Modulation of autophagy has been recognized as a promising therapeutic strategy against diseases/disorders associated with uncontrolled growth or accumulation of biomolecular aggregates, organelles, or cells including those caused by cancer, aging, neurodegeneration, and liver diseases such as α1-antitrypsin deficiency. Numerous pharmacological agents that enhance or suppress autophagy have been discovered. However, their molecular mechanisms of action are far from clear. Here, we collected a set of 225 autophagy modulators and carried out a comprehensive quantitative systems pharmacology (QSP) analysis of their targets using both existing databases and predictions made by our machine learning algorithm. Autophagy modulators include several highly promiscuous drugs (e.g., artenimol and olanzapine acting as activators, fostamatinib as an inhibitor, or melatonin as a dual-modulator) as well as selected drugs that uniquely target specific proteins (~30% of modulators). They are mediated by three layers of regulation: (i) pathways involving core autophagy-related (ATG) proteins such as mTOR, AKT, and AMPK; (ii) upstream signaling events that regulate the activity of ATG pathways such as calcium-, cAMP-, and MAPK-signaling pathways; and (iii) transcription factors regulating the expression of ATG proteins such as TFEB, TFE3, HIF-1, FoxO, and NF-κB. Our results suggest that PKA serves as a linker, bridging various signal transduction events and autophagy. These new insights contribute to a better assessment of the mechanism of action of autophagy modulators as well as their side effects, development of novel polypharmacological strategies, and identification of drug repurposing opportunities.

Deciphering the Rules of in Silico Autophagy Methods for Expediting Medicinal Research

Autophagy is a highly evolutionarily conserved cellular catabolic process responsible for degradation of damaged organelles and long-lived proteins. It is not surprising that scientific interest in the connection of autophagy and diseases has been growing. Over the past 2 decades, many new experimental approaches have been emerging in the field of targeting autophagy for medicinal research. Interestingly, in addition to experimental methods, a number of databases, Web servers, mathematics models, omics approaches, and systems biology network approaches related to autophagy have become available online, which may be collectively considered to be "in silico autophagy methods" for expediting current medicinal research. Thus, we have summarized a series of relevant in silico autophagy approaches for promoting the most appropriate usage of these resources for potential therapeutic purposes.