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The genome-scale metabolic model (GEM) has emerged as one of the leading modeling approaches for systems-level metabolic studies and has been widely explored for a broad range of organisms and applications. Owing to the development of genome sequencing technologies and available biochemical data, it is possible to reconstruct GEMs for model and non-model microorganisms as well as for multicellular organisms such as humans and animal models. GEMs will evolve in parallel with the availability of biological data, new mathematical modeling techniques and the development of automated GEM reconstruction tools. The use of high-quality, context-specific GEMs, a subset of the original GEM in which inactive reactions are removed while maintaining metabolic functions in the extracted model, for model organisms along with machine learning (ML) techniques could increase their applications and effectiveness in translational research in the near future. Here, we briefly review the current state of GEMs, discuss the potential contributions of ML approaches for more efficient and frequent application of these models in translational research, and explore the extension of GEMs to integrative cellular models.

Cancer and arachidonic acid (AA) have important linkages. For example, AA metabolites regulate several critical biological functions associated with carcinogenesis: angiogenesis, apoptosis, and cancer invasion. However, little is known about the comparative changes in metabolite expression of the arachidonic acid pathway (AAP) in carcinogenesis. In this study, we examined transcriptome data from 12 cancers, such as breast invasive carcinoma, colon adenocarcinoma, lung adenocarcinoma, and prostate adenocarcinoma. We also report here a reverse-engineering strategy wherein we estimated metabolic signatures associated with AAP by (1) making deductive inferences through transcriptome-level data extraction, (2) remodeling AA metabolism, and (3) performing a comparative analysis of cancer types to determine the similarities and differences between different cancer types with respect to AA metabolic alterations. We identified 77 AAP gene signatures differentially expressed in cancers and 37 AAP metabolites associated with them. Importantly, the metabolite 15(S)-HETE was identified in almost all cancers, while arachidonate, 5-HETE, PGF2α, 14,15-EET, 8,9-EET, 5,6-EET, and 20-HETE were discovered as other most regulated metabolites. This study shows that the 12 cancers studied herein, although in different branches of the AAP, have altered expression of AAP gene signatures. Going forward, AA related-cancer research generally, and the molecular signatures and their estimated metabolites reported herein specifically, hold broad promise for precision/personalized medicine in oncology as potential therapeutic and diagnostic targets.

Maturity-onset diabetes of the young (MODY) is a highly heterogeneous group of monogenic and nonautoimmune diseases. Misdiagnosis of MODY is a widespread problem and about 5% of patients with type 2 diabetes mellitus and nearly 10% with type 1 diabetes mellitus may actually have MODY. Using next-generation DNA sequencing (NGS) to facilitate accurate diagnosis of MODY, this study investigated mutations in 13 MODY genes ( HNF4A , GCK , HNF1A , PDX1 , HNF1B , NEUROD1 , KLF11 , CEL , PAX4 , INS , BLK , ABCC8 , and KCNJ11 ). In addition, we comprehensively investigated the clinical phenotypic effects of the genetic variations identified. Fifty-one adult patients with suspected MODY and 64 healthy controls participated in the study. We identified 7 novel and 10 known missense mutations localized in PDX1 , HNF1B , KLF11 , CEL , BLK , and ABCC8 genes in 29.4% of the patient sample. Importantly, we report several mutations that were classified as "deleterious" as well as those predicted as "benign." Notably, the ABCC8 p.R1103Q, ABCC8 p.V421I, CEL I336T, CEL p.N493H, BLK p.L503P, HNF1B p.S362P, and PDX1 p.E69A mutations were identified for the first time as causative variants for MODY. More aggressive clinical features were observed in three patients with double- and triple-heterozygosity of PDX1 - KLF11 (p.E69A/p.S182R), CEL - ABCC8 - KCNJ11 (p.I336, p.G157R/p.R1103Q/p.A157A), and HNF1B - KLF11 (p.S362P/p.P261L). Interestingly, the clinical effects of the BLK mutations appear to be exacerbated in the presence of obesity. In conclusion, NGS analyses of the adult patients with suspected MODY appear to be informative in a clinical context. These findings warrant further clinical diagnostic research and development in different world populations suffering from diabetes with genetic underpinnings.

Genomic medicine has made important strides over the past several decades, but as new insights and technologies emerge, the applications of genomics in medicine and planetary health continue to evolve and expand. An important grand challenge is harnessing and making sense of the genomic big data in ways that best serve public and planetary health. Because human health is inextricably intertwined with the health of planetary ecosystems and nonhuman animals, genomic medicine is in need of high throughput bioinformatics analyses to harness and integrate human and ecological multiomics big data. It is in this overarching context that artificial intelligence (AI), particularly machine learning and deep learning, offers enormous potentials to advance genomic medicine in a spirit of One Health. This expert review offers an analysis of the rapidly emerging role of AI in genomic medicine, including its current drivers, levers, opportunities, and challenges. The scope of AI applications in genomic medicine is broad, ranging from efficient and automated data analysis to drug repurposing and precision medicine, as with its challenges such as veracity of the big data that AI sorely depends on, social biases that the AI-driven algorithms can introduce, and how best to incorporate AI with human intelligence. The road ahead for AI in genomic medicine is complex and arduous and yet worthy of cautious optimism as we face future pandemics and ecological crises in the 21st century. Now is a good time to think about the role of AI in genomic medicine and planetary health.

Esophageal squamous cell carcinoma (ESCC) is among the most dangerous cancers with high mortality and lack of robust diagnostics and personalized/precision therapeutics. To achieve a systems-level understanding of tumorigenesis, unraveling of variations in the protein interactome and determination of key proteins exhibiting significant alterations in their interaction patterns during tumorigenesis are crucial. To this end, we have described differential protein-protein interactions and differentially interacting proteins (DIPs) in ESCC by utilizing the human protein interactome and transcriptome. Furthermore, DIP-centered modules were analyzed according to their potential in elucidation of disease mechanisms and improvement of efficient diagnostic, prognostic, and treatment strategies. Seven modules were presented as potential diagnostic, and 16 modules were presented as potential prognostic biomarker candidates. Importantly, our findings also suggest that 30 out of the 53 repurposed drugs were noncancer drugs, which could be used in the treatment of ESCC. Interestingly, 25 of these, proposed as novel drug candidates here, have not been previously associated in a context of esophageal cancer. In this context, risperidone and clozapine were validated for their growth inhibitory potential in three ESCC lines. Our findings offer a high potential for the development of innovative diagnostic, prognostic, and therapeutic strategies for further experimental studies in line with predictive diagnostics, targeted prevention, and personalization of medical services in ESCC specifically, and personalized cancer care broadly.

Diabetes is a common disorder with a heterogeneous clinical presentation and an enormous burden on health care worldwide. About 1-6% of patients with diabetes suffer from maturity-onset diabetes of the young (MODY), the most common form of monogenic diabetes with autosomal dominant inheritance. MODY is genetically and clinically heterogeneous and caused by genetic variations in pancreatic β-cell development and insulin secretion. We report here new findings from targeted next-generation sequencing (NGS) of 13 MODY-related genes. A sample of 22 unrelated pediatric patients with MODY and 13 unrelated healthy controls were recruited from a Turkish population. Targeted NGS was performed with Miseq 4000 (Illumina) to identify genetic variations in 13 MODY-related genes: HNF4A , GCK , HNF1A , PDX1 , HNF1B , NEUROD1 , KLF11 , CEL , PAX4 , INS , BLK , ABCC8 , and KCNJ11 . The NGS data were analyzed adhering to the Genome Analysis ToolKit (GATK) best practices pipeline, and variant filtering and annotation were performed. In the patient sample, we identified 43 MODY-specific genetic variations that were not present in the control group, including 11 missense mutations and 4 synonymous mutations. Importantly, and to the best of our knowledge, the missense mutations NEUROD1 p.D202E, KFL11 p.R461Q, BLK p.G248R, and KCNJ11 p.S385F were first associated with MODY in the present study. These findings contribute to the worldwide knowledge base on MODY and molecular correlates of clinical heterogeneity in monogenic childhood diabetes. Further comparative population genetics and functional genomics studies are called for, with an eye to discovery of novel diagnostics and personalized medicine in MODY. Because MODY is often misdiagnosed as type 1 or type 2 diabetes mellitus, advances in MODY diagnostics with NGS stand to benefit diabetes overall clinical care as well.

Cancer stem-like cells (CSCs) possess the ability to self-renew and differentiate, and they are among the major factors driving tumorigenesis, metastasis, and resistance to chemotherapy. Therefore, it is critical to understand the molecular substrates of CSC biology so as to discover novel molecular biosignatures that distinguish CSCs and tumor cells. Here, we report new findings and insights by employing four transcriptome datasets associated with CSCs, with CSC and tumor samples from breast, lung, oral, and ovarian tissues. The CSC samples were analyzed to identify differentially expressed genes between CSC and tumor phenotypes. Through comparative profiling of expression levels in different cancer types, we identified 17 "seed genes" that showed a mutual differential expression pattern. We showed that these seed genes were strongly associated with cancer-associated signaling pathways and biological processes, the immune system, and the key cancer hallmarks. Further, the seed genes presented significant changes in their expression profiles in different cancer types and diverse mutation rates, and they also demonstrated high potential as diagnostic and prognostic biomarkers in various cancers. We report a number of seed genes that represent significant potential as "systems biomarkers" for understanding the pathobiology of tumorigenesis. Seed genes offer a new innovation avenue for potential applications toward cancer precision medicine in a broad range of cancers in oncology in the future.

Cancer as the leading cause of death worldwide has many issues that still need to be addressed. Since the alterations on the glycan compositions or/and structures (i.e., glycosylation, sialylation, and fucosylation) are common features of tumorigenesis, glycomics becomes an emerging field examining the structure and function of glycans. In the past, cancer studies heavily relied on genomics and transcriptomics with relatively little exploration of the glycan alterations and glycoprotein biomarkers among individuals and populations. Since glycosylation of proteins increases their structural complexity by several orders of magnitude, glycome studies resulted in highly dynamic biomarkers that can be evaluated for cancer diagnosis, prognosis, and therapy. Glycome not only integrates our genetic background with past and present environmental factors but also offers a promise of more efficient patient stratification compared with genetic variations. Therefore, studying glycans holds great potential for better diagnostic markers as well as developing more efficient treatment strategies in human cancers. While recent developments in glycomics and associated technologies now offer new possibilities to achieve a high-throughput profiling of glycan diversity, we aim to give an overview of the current status of glycan research and the potential applications of the glycans in the scope of the personalized medicine strategies for cancer.

Urodele amphibians such as the axolotl ( Ambystoma mexicanum ) display a large capacity for tissue regeneration and remarkable resistance to cancer. As a model organism, axolotl thus offers a unique opportunity for cancer research and anticancer drug discovery, not to mention the discerning mechanisms that underpin controlled cellular growth and regeneration versus cancer. To the best of our knowledge, little is known on comparative gene expression changes during regeneration events such as wound healing in axolotl and humans. Using publicly available transcriptomics data and bioinformatics analyses, we examined the differential gene expression signatures in skin wound samples from axolotl and humans after skin biopsy punch injury, in comparison with intact (uninjured) control skin samples. We identified 95 genes exhibiting a reversal expression pattern between humans and axolotl during the wound healing/regeneration period. These genes were significantly associated with collagen biosynthesis, extracellular matrix organization, PI3K-Akt signaling pathway, immune system response, and apoptotic process. Furthermore, this new gene set exhibited high prognostic performance in discriminating the survival risk in skin-related cancers, including melanoma (hazard ratio [HR] = 8.14, p  < 10 -30 ), oral cancer (HR >100, p  < 10 -12 ), and head and neck carcinoma (HR = 5.29, p  < 10 -30 ). Moreover, considering these gene signatures, we repositioned 11 small molecules as potential anticancer drug candidates indicating reversal effects on upregulated human genes and downregulated axolotl genes or mimicking downregulated human genes and upregulated axolotl genes. We anticipate that this study offers new insights on gene signatures bridging regeneration mechanisms with tumorigenesis and cancer drug repositioning.

The head and neck squamous cell carcinoma (HNSCC) is one of the most common cancers in the world, but robust biomarkers and diagnostics are still not available. This study provides in-depth insights from systems biology analyses to identify molecular biomarker signatures to inform systematic drug targeting in HNSCC. Gene expression profiles from tumors and normal tissues of 22 patients with histological confirmation of nonmetastatic HNSCC were subjected to integrative analyses with genome-scale biomolecular networks (i.e., protein-protein interaction and transcriptional and post-transcriptional regulatory networks). We aimed to discover molecular signatures at RNA and protein levels, which could serve as potential drug targets for therapeutic innovation in the future. Eleven proteins, 5 transcription factors, and 20 microRNAs (miRNAs) came into prominence as potential drug targets. The differential expression profiles of these reporter biomolecules were cross-validated by independent RNA-Seq and miRNA-Seq datasets, and risk discrimination performance of the reporter biomolecules, BLNK, CCL2, E4F1, FOSL1, ISG15, MMP9, MYCN, MYH11, miR-1252, miR-29b, miR-29c, miR-3610, miR-431, and miR-523, was also evaluated. Using the transcriptome guided drug repositioning tool, geneXpharma, several candidate drugs were repurposed, including antineoplastic agents (e.g., gemcitabine and irinotecan), antidiabetics (e.g., rosiglitazone), dermatological agents (e.g., clocortolone and acitretin), and antipsychotics (e.g., risperidone), and binding affinities of the drugs to their potential targets were assessed using molecular docking analyses. The molecular signatures and repurposed drugs presented in this study warrant further attention for experimental studies since they offer significant potential as biomarkers and candidate therapeutics for precision medicine approaches to clinical management of HNSCC.

Drug repositioning is an innovative approach to identify new therapeutic indications for existing drugs. Drug repositioning offers the promise of reducing drug development timeframes and costs, and because it involves drugs that are already in the clinic, it might remedy some of the drug safety challenges traditionally associated with drug candidates that are not yet available in the clinic. The gene-by-drug interactions are an important dimension of optimal drug repositioning and development strategies. While gene-by-drug interactions have been curated and presented in various databases, novel bioinformatics tools and approaches are timely, and required with a specific focus to support drug positioning. We report, in this study, the design of a public web-accessible transcriptomic-/gene expression-guided pharmaceuticals search tool, geneXpharma ( www.genexpharma.org ). GeneXpharma is a public platform with user-centric interface that provides statistically evaluated gene expressions and their drug interactions for 48 diseases under seven different disease categories. GeneXpharma is designed and organized to generate hypotheses on druggable genome within the disease-gene-drug triad and thus, help repositioning of drugs against diseases. The search system accommodates various entry points using drugs, genes, or diseases, which then enable researchers to extract drug repurposing candidates and readily export for further evaluation. Future developments aim to improve the geneXpharma algorithm, enrich its content, and enhance the website interface through addition of network visualizations and graphical display items. Bioinformatics search tools can help enable the convergence of drug repositioning and gene-by-drug interactions so as to further optimize drug development efforts in the future.