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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.

[Display omitted] • Autophagy is a conserved evolutionarily pathway to maintain cellular homeostasis for human health. • Databases containing autophagy have been constructed to store information on genes, proteins, compounds, and diseases. • The systems biology approaches involve integrating multiple omics data to predict potential molecular interactions and linking autophagy with other biological processes. • The integration of the omics methods and artificial intelligence can build models and extract features. • Mathematical modeling combined with deep neural networks is used to describe the complex dynamic process of autophagy. 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. 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. 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. [ABSTRACT FROM AUTHOR]

Pesticides kill neurons, but the mechanism leading to selective dopaminergic loss in Parkinson's disease (PD) is unknown. Understanding the pesticide's effect on dopaminergic neurons (DA) can help to screen and treat PD. The critical uptake of pesticides by the membrane receptors at DA is hypothesized to activate a signaling cascade and accelerate degeneration. Using MPTP as a reference, we demonstrate the mechanisms of eleven crucial pesticides through molecular docking, protein networks, regulatory pathways, and prioritization of key pesticide-regulating proteins. Participants were recruited and grouped into control and PD based on clinical characteristics as well as pesticide traces in their blood plasma. Then, qPCR was used to measure pesticide-associated gene expression in peripheral blood mononuclear cells between groups. As a result of molecular docking, all eleven pesticides and the MPTP showed high binding efficiency against 274 membrane receptor proteins of DA. Further, the protein interaction networks showed activation of multiple signaling cascades through these receptors. Subsequent analysis revealed 31 biological pathways shared by all 11pesticides and MPTP that were overrepresented by 46 crucial proteins. Among these, CTNNB1, NDUFS6, and CAV1 were prioritized to show a significant change in gene expression in pesticide-exposed PD which guides toward therapy. [ABSTRACT FROM AUTHOR]

Abstract Background Renal Cell Carcinoma (RCC) is difficult to treat with 5-year survival rate of 10% in metastatic patients. Main reasons of therapy failure are lack of validated biomarkers and scarce knowledge of the biological processes occurring during RCC progression. Thus, the investigation of mechanisms regulating RCC progression is fundamental to improve RCC therapy. Methods In order to identify molecular markers and gene processes involved in the steps of RCC progression, we generated several cell lines of higher aggressiveness by serially passaging mouse renal cancer RENCA cells in mice and, concomitantly, performed functional genomics analysis of the cells. Multiple cell lines depicting the major steps of tumor progression (including primary tumor growth, survival in the blood circulation and metastatic spread) were generated and analyzed by large-scale transcriptome, genome and methylome analyses. Furthermore, we performed clinical correlations of our datasets. Finally we conducted a computational analysis for predicting the time to relapse based on our molecular data. Results Through in vivo passaging, RENCA cells showed increased aggressiveness by reducing mice survival, enhancing primary tumor growth and lung metastases formation. In addition, transcriptome and methylome analyses showed distinct clustering of the cell lines without genomic variation. Distinct signatures of tumor aggressiveness were revealed and validated in different patient cohorts. In particular, we identified SAA2 and CFB as soluble prognostic and predictive biomarkers of the therapeutic response. Machine learning and mathematical modeling confirmed the importance of CFB and SAA2 together, which had the highest impact on distant metastasis-free survival. From these data sets, a computational model predicting tumor progression and relapse was developed and validated. These results are of great translational significance. Conclusion A combination of experimental and mathematical modeling was able to generate meaningful data for the prediction of the clinical evolution of RCC.

Novel crop improvement approaches, including those that facilitate for the exploitation of crop wild relatives and underutilized species harboring the much-needed natural allelic variation are indispensable if we are to develop climate-smart crops with enhanced abiotic and biotic stress tolerance, higher nutritive value, and superior traits of agronomic importance. Top among these approaches are the "omics" technologies, including genomics, transcriptomics, proteomics, metabolomics, phenomics, and their integration, whose deployment has been vital in revealing several key genes, proteins and metabolic pathways underlying numerous traits of agronomic importance, and aiding marker-assisted breeding in major crop species. Here, citing several relevant examples, we appraise our understanding on the recent developments in omics technologies and how they are driving our quest to breed climate resilient crops. Large-scale genome resequencing, pan-genomes and genome-wide association studies are aiding the identification and analysis of species-level genome variations, whilst RNA-sequencing driven transcriptomics has provided unprecedented opportunities for conducting crop abiotic and biotic stress response studies. Meanwhile, single cell transcriptomics is slowly becoming an indispensable tool for decoding cell-specific stress responses, although several technical and experimental design challenges still need to be resolved. Additionally, the refinement of the conventional techniques and advent of modern, high-resolution proteomics technologies necessitated a gradual shift from the general descriptive studies of plant protein abundances to large scale analysis of protein-metabolite interactions. Especially, metabolomics is currently receiving special attention, owing to the role metabolites play as metabolic intermediates and close links to the phenotypic expression. Further, high throughput phenomics applications are driving the targeting of new research domains such as root system architecture analysis, and exploration of plant root-associated microbes for improved crop health and climate resilience. Overall, coupling these multi-omics technologies to modern plant breeding and genetic engineering methods ensures an all-encompassing approach to developing nutritionally-rich and climate-smart crops whose productivity can sustainably and sufficiently meet the current and future food, nutrition and energy demands. [ABSTRACT FROM AUTHOR]

Recent studies have convincingly linked gut microbiota to traits relevant to atherosclerosis, such as insulin resistance, dyslipidemia and inflammation, and have revealed novel disease pathways involving microbe-derived metabolites. These results have important implications for understanding how environmental and genetic factors act together to influence cardiovascular disease (CVD) risk. Thus, dietary constituents are not only absorbed and metabolized by the host but they also perturb the gut microbiota, which in turn influence host metabolism and inflammation. It also appears that host genetics helps to shape the gut microbiota community. Here, we discuss challenges in understanding these interactions and the role they play in CVD.

Novel crop improvement approaches, including those that facilitate for the exploitation of crop wild relatives and underutilized species harboring the much-needed natural allelic variation are indispensable if we are to develop climate-smart crops with enhanced abiotic and biotic stress tolerance, higher nutritive value, and superior traits of agronomic importance. Top among these approaches are the “omics” technologies, including genomics, transcriptomics, proteomics, metabolomics, phenomics, and their integration, whose deployment has been vital in revealing several key genes, proteins and metabolic pathways underlying numerous traits of agronomic importance, and aiding marker-assisted breeding in major crop species. Here, citing several relevant examples, we appraise our understanding on the recent developments in omics technologies and how they are driving our quest to breed climate resilient crops. Large-scale genome resequencing, pan-genomes and genome-wide association studies are aiding the identification and analysis of species-level genome variations, whilst RNA-sequencing driven transcriptomics has provided unprecedented opportunities for conducting crop abiotic and biotic stress response studies. Meanwhile, single cell transcriptomics is slowly becoming an indispensable tool for decoding cell-specific stress responses, although several technical and experimental design challenges still need to be resolved. Additionally, the refinement of the conventional techniques and advent of modern, high-resolution proteomics technologies necessitated a gradual shift from the general descriptive studies of plant protein abundances to large scale analysis of protein-metabolite interactions. Especially, metabolomics is currently receiving special attention, owing to the role metabolites play as metabolic intermediates and close links to the phenotypic expression. Further, high throughput phenomics applications are driving the targeting of new research domains such as root system architecture analysis, and exploration of plant root-associated microbes for improved crop health and climate resilience. Overall, coupling these multi-omics technologies to modern plant breeding and genetic engineering methods ensures an all-encompassing approach to developing nutritionally-rich and climate-smart crops whose productivity can sustainably and sufficiently meet the current and future food, nutrition and energy demands.

Background: Renal Cell Carcinoma (RCC) is difficult to treat with 5-year survival rate of 10% in metastatic patients. Main reasons of therapy failure are lack of validated biomarkers and scarce knowledge of the biological processes occurring during RCC progression. Thus, the investigation of mechanisms regulating RCC progression is fundamental to improve RCC therapy. Methods: In order to identify molecular markers and gene processes involved in the steps of RCC progression, we generated several cell lines of higher aggressiveness by serially passaging mouse renal cancer RENCA cells in mice and, concomitantly, performed functional genomics analysis of the cells. Multiple cell lines depicting the major steps of tumor progression (including primary tumor growth, survival in the blood circulation and metastatic spread) were generated and analyzed by large-scale transcriptome, genome and methylome analyses. Furthermore, we performed clinical correlations of our datasets. Finally we conducted a computational analysis for predicting the time to relapse based on our molecular data. Results: Through in vivo passaging, RENCA cells showed increased aggressiveness by reducing mice survival, enhancing primary tumor growth and lung metastases formation. In addition, transcriptome and methylome analyses showed distinct clustering of the cell lines without genomic variation. Distinct signatures of tumor aggressiveness were revealed and validated in different patient cohorts. In particular, we identified SAA2 and CFB as soluble prognostic and predictive biomarkers of the therapeutic response. Machine learning and mathematical modeling confirmed the importance of CFB and SAA2 together, which had the highest impact on distant metastasis-free survival. From these data sets, a computational model predicting tumor progression and relapse was developed and validated. These results are of great translational significance. Conclusion: A combination of experimental and mathematical modeling was able to generate meaningful data for the prediction of the clinical evolution of RCC. [ABSTRACT FROM AUTHOR]

Type 1 diabetes (T1D) occurs as a consequence of an autoimmune attack against pancreatic β- cells. Due to a lack of a clear understanding of the T1D pathogenesis, the identification of effective therapies for T1D is the active area in the research. The study purpose was to prioritize potential drugs and targets in T1D via systems biology approach. Gene expression data of peripheral blood mononuclear cells (PBMCs) and pancreatic β-cells in T1D were analyzed and differential expressed genes were integrated with protein-protein interactions (PPI) data. Multiple topological centrality parameters of extracted query-query PPI (QQPPI) networks were calculated and the interaction of more central proteins with drugs was investigated. Molecular docking was performed to further predict the interactions between drugs and the binding sites of targets. Central proteins were identified by the analysis of PBMC (MYC, ERBB2, PSMA1, ABL1 and HSP90AA1) and pancreatic β-cells (HSP90AB1, ESR1, RELA, RAC1, NFKB1, NFKB2, IKBKE, ARRB2 and SRC) QQPPI networks. Thirteen drugs which targeted eight central proteins were identified by further analysis of drug-target interactions. Some drugs which investigated for diabetes treatment in the experimental models of T1D were prioritized by literature verification, including melatonin, resveratrol, lapatinib, geldanamycin, eugenol and fostaminib. Finally, according on molecular docking analysis, lapatinib-ERBB2 and eugenol- ESR1 exhibited highest and lowest binding energy, respectively. This study presented promising results for the prioritization of potential drug-targets which might facilitate T1D targeted therapy and its drug discovery process more effectively. [ABSTRACT FROM AUTHOR]

Cardiomyopathy (CMP) is a group of myocardial diseases that progressively impair cardiac function. The mechanisms underlying CMP development are poorly understood, but lifestyle factors are clearly implicated as risk factors. This study aimed to identify molecular biomarkers involved in inflammatory CMP development and progression using a systems biology approach. The authors analysed microarray gene expression datasets from CMP and tissues affected by risk factors including smoking, ageing factors, high body fat, clinical depression status, insulin resistance, high dietary red meat intake, chronic alcohol consumption, obesity, high‐calorie diet and high‐fat diet. The authors identified differentially expressed genes (DEGs) from each dataset and compared those from CMP and risk factor datasets to identify common DEGs. Gene set enrichment analyses identified metabolic and signalling pathways, including MAPK, RAS signalling and cardiomyopathy pathways. Protein–protein interaction (PPI) network analysis identified protein subnetworks and ten hub proteins (CDK2, ATM, CDT1, NCOR2, HIST1H4A, HIST1H4B, HIST1H4C, HIST1H4D, HIST1H4E and HIST1H4L). Five transcription factors (FOXC1, GATA2, FOXL1, YY1, CREB1) and five miRNAs were also identified in CMP. Thus the authors' approach reveals candidate biomarkers that may enhance understanding of mechanisms underlying CMP and their link to risk factors. Such biomarkers may also be useful to develop new therapeutics for CMP. [ABSTRACT FROM AUTHOR]

Chronic kidney disease has severe impacts on the patient and represents a major burden to the health care systems worldwide. Despite an increased knowledge of pathophysiological processes involved in kidney diseases, the progress in defining novel treatment strategies has been limited. One reason is the descriptive disease categorization used in nephrology based on clinical findings or histopathological categories irrespective of potential different molecular disease mechanisms. To accelerate progress toward a targeted treatment, a definition of human disease extending from phenotypic disease classification to mechanism-based disease definitions is needed. In recent years, we have witnessed a major transition in biomedical research from a single gene research to an information rich and collaborative science. Tissue-based analysis in renal disease allows to link structure to molecular function. In our review, we introduce the concept of precision medicine in nephrology, describe several large cohort studies established for molecular analysis of kidney diseases, and highlight examples of renal biopsy-driven target identification by integrative systems biology approaches. Furthermore, we give an outlook on how the new disease definitions can be used for patient stratification in clinical trial design. Finally, we introduce the concept of an informational commons of renal precision medicine for joint analyses of large-scale data sets in renal failure. [ABSTRACT FROM AUTHOR]

A major objective of 'omics' technologies is to understand genetic causality of complex traits of human diseases. High‐throughput omics technologies and their application to medicine open up remarkable opportunities for realising optimised medical treatment for individuals. Because many major breakthrough and discoveries in this field have been driven by the development of new omics technologies, in this review, the authors aim to provide an in‐depth description of their underlying principles as a foundation of developing another new omics technology, and to introduce their emerging applications for personalised medicine. The systems biology approach is then introduced as a future direction towards actionable personalised medicine. [ABSTRACT FROM AUTHOR]

Endophytism is the phenomenon of mutualistic association of a plant with a microorganism wherein the microbe lives within the tissues of the plant without causing any symptoms of disease. In addition to being a treasured biological resource, endophytes play diverse indispensable functions in nature for plant growth, development, stress tolerance, and adaptation. Our understanding of endophytism and its ecological aspects are overtly limited, and we have only recently started to appreciate its essence. Endophytes may impact plant biology through the production of diverse chemical entities including, but not limited to, plant growth hormones and by modulating the gene expression of defense and other secondary metabolic pathways of the host. Studies have shown differential recruitment of endophytes in endophytic populations of plants growing in the same locations, indicating host specificity and that endophytes evolve in a coordinated fashion with the host plants. Endophytic technology can be employed for the efficient production of agricultural and economically important plants and plant products. The rational application of endophytes to manipulate the microbiota, intimately associated with plants, can help in enhancement of production of agricultural produce, increased production of key metabolites in medicinal and aromatic plants, as well as adaption to new bio-geographic regions through tolerance to various biotic and abiotic conditions. However, the potential of endophytic biology can be judiciously harnessed only when we obtain insight into the molecular mechanism of this unique mutualistic relationship. In this paper, we present a discussion on endophytes, endophytism, their significance, and diverse functions in nature as unraveled by the latest research to understand this universal natural phenomenon. [ABSTRACT FROM AUTHOR]

The hormesis phenomena or J-shaped dose response have been accepted as a common phenomenon regardless of the involved biological model, endpoint measured and chemical class/physical stressor. This paper first introduced a mathematical dose response model based on systems biology approach. It links molecular-level cell cycle checkpoint control information to clonal growth cancer model to predict the possible shapes of the dose response curves of Ionizing Radiation (IR) induced tumor transformation frequency. J-shaped dose response curves have been captured with consideration of cell cycle checkpoint control mechanisms. The simulation results indicate the shape of the dose response curve relates to the behavior of the saddle-node points of the model in the bifurcation diagram. A simplified version of the model in previous work of the authors was used mathematically to analyze behaviors relating to the saddle-node points for the J-shaped dose response curve. It indicates that low-linear energy transfer (LET) is more likely to have a J-shaped dose response curve. This result emphasizes the significance of systems biology approach, which encourages collaboration of multidiscipline of biologists, toxicologists and mathematicians, to illustrate complex cancer-related events, and confirm the biphasic dose-response at low doses. [ABSTRACT FROM AUTHOR]

Background: Drug resistance in breast cancer is an unsolved problem in treating patients. It has been recently discussed that lysosomes contribute to the invasion and angiogenesis of cancer cells. There is evidence that lysosomes can also cause multi-drug resistance. We analyzed this emerging concept in breast cancer through computational and systems biology approaches., Objectives: We aimed to identify the key lysosome-related genes associated with drug-resistant breast cancer., Methods: All genes contributing to the structure and function of lysosomes were inquired through the Human Lysosome Gene Database. The prioritized top 51 genes from the provided lists of Endeavour, ToppGene, and GPSy as prioritization tools were selected. All lysosomal genes and 12 breast cancer-related genes aligned to identify the most similar genes to breast cancer-related genes. Different centralities were applied to score each human protein to calculate the most central lysosomal genes in the human protein-protein interaction (PPI) network. Common genes were extracted from the results of the mentioned methods as a selected gene set. For Gene Ontology enrichment, the selected gene set was analyzed by WebGestalt, DAVID, and KOBAS. The PPI network was constructed via the STRING database. The PPI network was analyzed utilizing Cytoscape for topology network interaction and CytoHubba to extract hub genes., Results: Based on biological studies, literature reviews, and comparing all mentioned analyzing methods, six genes were introduced as essential in breast cancer. This computational approach to all lysosome-related genes suggested that candidate genes include PRF1, TLR9, CLTC, GJA1, AP3B1, and RPTOR. The analyses of these six genes suggest that they may have a crucial role in breast cancer development, which has rarely been evaluated. These genes have a potential therapeutic implication for new drug discovery for chemo-resistant breast cancer., Conclusions: The present work focused on all the functional and structural lysosome-related genes associated with breast cancer. It revealed the top six lysosome hub genes that might serve as therapeutic targets in drug-resistant breast cancer. Since these genes play a pivotal role in the structure and function of lysosomes, targeting them can effectively overcome drug resistance., Competing Interests: Conflict of Interests: The authors declare no conflict of interest., (Copyright © 2022, Author(s).)

The aim of this study was to develop a systems approach to study tumor tissue. The importance of concurrent extraction of RNA, DNA, and protein is evident when genetic aberrations and the differences in the proteome and transcriptome have to be correlated. The need is magnified, as the tissue available for study is miniscule, is shared amongst investigators, and needs to support the holistic approach. Trizol is a monophasic solution of phenol and guanidine isothiocyanate and can be used to isolate the three biomolecules simultaneously. Trizol solution was used for RNA extraction in an ongoing study about expression of molecular markers in non-small cell lung carcinoma (NSCLC) and breast tumor tissue. After isolation of RNA, the remaining Trizol fraction was stored at -80°C for over 6 months. We have shown the extraction of protein from 17 tumor and adjacent, normal tissue samples and PBMC obtained from four blood samples. The isolation and solubilization of the protein fraction were done according to the product information using isopropanol for precipitation and guanidine hydrochloride and SDS for washing and solubilization, respectively, modifying the time of solubilization. The protein was estimated by the bicinchoninic acid (BCA) method and analyzed on polyacrylamide gels. Staining showed a wide repertoire, and Western blotting confirmed extraction of cytokeratins (CK) and DNA repair proteins. Whereas tissue samples in which the RNA was degraded could be assessed by the presence of the protein salvaging the marker analysis, it was seen that nuclear proteins cannot be retrieved and are probably lost with the DNA fraction. [ABSTRACT FROM AUTHOR]

One approach to understanding gut microbiome-host interactions, described in this review, is to examine how natural variation in a model organism, where environmental factors can be controlled, affects the microbiome and, in turn, how the microbiome is associated with physiological or clinical traits. A variation of this approach, termed "systems genetics" is to characterize both the microbiome and the host using various high throughput technologies, such as metabolomics or gene expression of the microbiome and the host. By relating variation in the microbiome and host functions to such "molecular phenotypes", hypotheses can be generated and then experimentally tested. To model human gut microbiome-host interactions in this way, the mouse is particularly useful given the extensive body of genetic resources and experimental tools that are available., Competing Interests: Conflicts of interest The authors declare that they have no conflicts of interest.