Metformin strongly affects transcriptome of peripheral blood cells in healthy individuals

Effects of metformin on transcriptomic and metabolomic profiles in breast cancer survivors enrolled in the randomized placebo-controlled MetBreCS trial

Metformin reduces the incidence of breast cancer in patients with obesity and type 2 diabetes. However, our knowledge of the effects of metformin on breast cancer recurrence is limited. Within the randomized double-blind placebo-controlled phase II trial MetBreCS, we examined changes in breast tissue from breast cancer survivors with BMI > 25 kg/m2 after treatment with metformin. To identify metformin-regulated signaling pathways, we integrated the transcriptomic, metabolomic and steroid hormone profiles using bivariate and functional analyses. We identified MS4A1, HBA2, MT-RNR1, MT-RNR2, EGFL6 and FDCSP expression to be differentially expressed in breast tissues from metformin-treated postmenopausal women. The integration of transcriptomic and metabolomic profiles revealed down-regulation of immune response genes associated with reduced levels of arginine and citrulline in the metformin-treated group. The integration of transcriptomic and steroid hormone profiles showed an enrichment of steroid hormone biosynthesis and metabolism pathways with highly negatively correlated CYP11A1 and CYP1B1 expression in breast tissue from postmenopausal metformin-treated women. Our results indicate that postmenopausal breast cancer survivors treated with metformin have specific changes in breast tissue gene expression that may prevent the development of new tumors.Trial registration: MetBreCs trial is registered at European Union Clinical Trials Register (EudraCT Protocol # 2015-001001-14) on 07/10/2015.

Harnessing Pharmacomultiomics for Precision Medicine in Diabetes: A Comprehensive Review

Type 2 diabetes (T2D) is the fastest-growing non-communicable disease worldwide, accounting for around 90% of all diabetes cases and imposing a significant health burden globally. Due to its phenotypic heterogeneity and composite genetic underpinnings, T2D requires a precision medicine approach personalized to individual molecular profiles, thereby shifting away from the traditional "one-size-fits-all" medical methods. This review advocates for a thorough pharmacomultiomics approach to enhance precision medicine for T2D. It emphasizes personalized treatment strategies that enhance treatment efficacy while minimizing adverse effects by integrating data from genomics, proteomics, metabolomics, transcriptomics, microbiomics, and epigenomics. We summarize key findings on candidate genes impacting diabetic medication responses and explore the potential of pharmacometabolomics in predicting drug efficacy. The role of pharmacoproteomics in prognosis and discovering new therapeutic targets is discussed, along with transcriptomics' contribution to understanding T2D pathophysiology. Additionally, pharmacomicrobiomics is explored to understand gut microbiota interactions with antidiabetic drugs. Emerging evidence on utilizing epigenomic profiles in improving drug efficacy and personalized treatment is also reviewed, illustrating their implications in personalized medicine. In this paper, we discuss the integration of these layers of omics data, examining recently developed paradigms that leverage complex data to deepen our understanding of diabetes. Such integrative approaches advance precision medicine strategies to tackle the disease by better understanding its complex biology.

Innovative aspects and applications of single cell technology for different diseases

Recent developments in single-cell technologies have provided valuable insights from cancer genomics to complex microbial communities. Single-cell technologies including the RNA-seq, next-generation sequencing (NGS), epigenomics, genomics, and transcriptomics can be used to uncover the single cell nature and molecular characterization of individual cells. These technologies also reveal the cellular transition states, evolutionary relationships between genes, the complex structure of single-cell populations, cell-to-cell interaction leading to biological discoveries and more reliable than traditional bulk technologies. These technologies are becoming the first choice for the early detection of inflammatory biomarkers affecting the proliferation and progression of tumor cells in the tumor microenvironment and improving the clinical efficacy of patients undergoing immunotherapy. These technologies also hold a central position in the detection of checkpoint inhibitors and thus determining the signaling pathways evoked by tumor invasion. This review addressed the emerging approaches of single cell-based technologies in cancer immunotherapies and different human diseases at cellular and molecular levels and the emerging role of sequencing technologies leading to drug discovery. Advancements in these technologies paved for discovering novel diagnostic markers for better understanding the pathological and biochemical mechanisms also for controlling the rate of different diseases.

Gut microbiome encoded purine and amino acid pathways present prospective biomarkers for predicting metformin therapy efficacy in newly diagnosed T2D patients

Metformin is widely used for treating type 2 diabetes mellitus (T2D). However, the efficacy of metformin monotherapy is highly variable within the human population. Understanding the potential indirect or synergistic effects of metformin on gut microbiota composition and encoded functions could potentially offer new insights into predicting treatment efficacy and designing more personalized treatments in the future. We combined targeted metabolomics and metagenomic profiling of gut microbiomes in newly diagnosed T2D patients before and after metformin therapy to identify potential pre-treatment biomarkers and functional signatures for metformin efficacy and induced changes in metformin therapy responders. Our sequencing data were largely corroborated by our metabolic profiling and identified that pre-treatment enrichment of gut microbial functions encoding purine degradation and glutamate biosynthesis was associated with good therapy response. Furthermore, we identified changes in glutamine-associated amino acid (arginine, ornithine, putrescine) metabolism that characterize differences in metformin efficacy before and after the therapy. Moreover, metformin Responders' microbiota displayed a shifted balance between bacterial lipidA synthesis and degradation as well as alterations in glutamate-dependent metabolism of N-acetyl-galactosamine and its derivatives (e.g. CMP-pseudaminate) which suggest potential modulation of bacterial cell walls and human gut barrier, thus mediating changes in microbiome composition. Together, our data suggest that glutamine and associated amino acid metabolism as well as purine degradation products may potentially condition metformin activity via its multiple effects on microbiome functional composition and therefore serve as important biomarkers for predicting metformin efficacy.

Metformin mitigates radiation toxicity exerting antioxidant and genoprotective properties

The antidiabetic drug metformin (MF) exhibits redox-modulating effects in various pathologies associated with oxidative stress and mitigates ionizing radiation-induced toxicity, but the underlying mechanisms remain to be elucidated. Thus, we studied some radiomitigatory effects of MF and explored the possible mechanisms behind them. Highly sensitive luminescence methods and non-competitive enzyme-linked immunosorbent assay (ELISA) were used in in vitro studies, and in vivo the damage to bone marrow cells and its repair were assessed by the micronucleus test. In a solution, MF at concentrations exceeding 0.1 µM effectively intercepts •OH upon X-ray-irradiation, but does not react directly with H2O2. MF accelerates the decomposition of H2O2 catalyzed by copper ions. MF does not affect the radiation-induced formation of H2O2 in the solution of bovine gamma-globulin (BGG), but has a modulating effect on the generation of H2O2 in the solution of bovine serum albumin (BSA). MF at 0.05-1 mM decreases the radiation-induced formation of 8-oxoguanine in a DNA solution depending on the concentration of MF with a maximum at 0.25 mM. MF at doses of 3 mg/kg body weight (bw) and 30 mg/kg bw administered to mice after irradiation, but not before irradiation, reduces the frequency of micronucleus formation in polychromatophilic erythrocytes of mouse red bone marrow. Our work has shown that the radiomitigatory properties of MF are mediated by antioxidant mechanisms of action, possibly including its ability to chelate polyvalent metal ions.

Metformin targets intestinal immune system signaling pathways in a high-fat diet-induced mouse model of obesity and insulin resistance

Introduction

Research findings of the past decade have highlighted the gut as the main site of action of the oral antihyperglycemic agent metformin despite its pharmacological role in the liver. Extensive evidence supports metformin's modulatory effect on the composition and function of gut microbiota, nevertheless, the underlying mechanisms of the host responses remain elusive. Our study aimed to evaluate metformin-induced alterations in the intestinal transcriptome profiles at different metabolic states.

Methods

The high-fat diet-induced mouse model of obesity and insulin resistance of both sexes was developed in a randomized block experiment and bulk RNA-Seq of the ileum tissue was the method of choice for comparative transcriptional profiling after metformin intervention for ten weeks.

Results

We found a prominent transcriptional effect of the diet itself with comparatively fewer genes responding to metformin intervention. The overrepresentation of immune-related genes was observed, including pronounced metformin-induced upregulation of immunoglobulin heavy-chain variable region coding Ighv1-7 gene in both high-fat diet and control diet-fed animals. Moreover, we provide evidence of the downregulation NF-kappa B signaling pathway in the small intestine of both obese and insulin-resistant animals as well as control animals after metformin treatment. Finally, our data pinpoint the gut microbiota as a crucial component in the metformin-mediated downregulation of NF-kappa B signaling evidenced by a positive correlation between the Rel and Rela gene expression levels and abundances of Parabacteroides distasonis, Bacteroides spp., and Lactobacillus spp. in the gut microbiota of the same animals.

Discussion

Our study supports the immunomodulatory effect of metformin in the ileum of obese and insulin-resistant C57BL/6N mice contributed by intestinal immunoglobulin responses, with a prominent emphasis on the downregulation of NF-kappa B signaling pathway, associated with alterations in the composition of the gut microbiome.

Metformin induces tolerogenicity of dendritic cells by promoting metabolic reprogramming

Dendritic cells (DCs) can mediate immune responses or immune tolerance depending on their immunophenotype and functional status. Remodeling of DCs' immune functions can develop proper therapeutic regimens for different immune-mediated diseases. In the immunopathology of autoimmune diseases (ADs), activated DCs notably promote effector T-cell polarization and exacerbate the disease. Recent evidence indicates that metformin can attenuate the clinical symptoms of ADs due to its anti-inflammatory properties. Whether and how the therapeutic effects of metformin on ADs are associated with DCs remain unknown. In this study, metformin was added to a culture system of LPS-induced DC maturation. The results revealed that metformin shifted DC into a tolerant phenotype, resulting in reduced surface expression of MHC-II, costimulatory molecules and CCR7, decreased levels of proinflammatory cytokines (TNF-α and IFN-γ), increased level of IL-10, upregulated immunomodulatory molecules (ICOSL and PD-L) and an enhanced capacity to promote regulatory T-cell (Treg) differentiation. Further results demonstrated that the anti-inflammatory effects of metformin in vivo were closely related to remodeling the immunophenotype of DCs. Mechanistically, metformin could mediate the metabolic reprogramming of DCs through FoxO3a signaling pathways, including disturbing the balance of fatty acid synthesis (FAS) and fatty acid oxidation (FAO), increasing glycolysis but inhibiting the tricarboxylic acid cycle (TAC) and pentose phosphate pathway (PPP), which resulted in the accumulation of fatty acids (FAs) and lactic acid, as well as low anabolism in DCs. Our findings indicated that metformin could induce tolerance in DCs by reprogramming their metabolic patterns and play anti-inflammatory roles in vitro and in vivo.

Composition of the maternal gastrointestinal microbiome as a predictor of neonatal birth weight

BACKGROUND

The biological mechanism by which the maternal gastrointestinal microbiota contributes to fetal growth and neonatal birth weight is currently unknown. The purpose of this study was to explore how the composition of the maternal microbiome in varying pre-gravid body mass index (BMI) groups are associated with neonatal birth weight adjusted for gestational age.

METHODS

Retrospective, cross-sectional metagenomic analysis of bio-banked fecal swab biospecimens (n = 102) self-collected by participants in the late second trimester of pregnancy.

RESULTS

Through high-dimensional regression analysis using principal components (PC) of the microbiome, we found that the best performing multivariate model explained 22.9% of the variation in neonatal weight adjusted for gestational age. Pre-gravid BMI (p = 0.05), PC3 (p = 0.03), and the interaction of the maternal microbiome with maternal blood glucose on the glucose challenge test (p = 0.01) were significant predictors of neonatal birth weight after adjusting for potential confounders including maternal antibiotic use during gestation and total gestational weight gain.

CONCLUSIONS

Our results indicate a significant association between the maternal gastrointestinal microbiome in the late second trimester and neonatal birth weight adjusted for gestational age. Moderated by blood glucose at the time of the universal glucose screening, the gastrointestinal microbiome may have a role in the regulation of fetal growth.

IMPACT

Maternal blood glucose in the late second trimester significantly moderates the relationship between the maternal gastrointestinal microbiome and neonatal size adjusted for gestational age. Our findings provide preliminary evidence for fetal programming of neonatal birth weight through the maternal gastrointestinal microbiome during pregnancy.

Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models

The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug-omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug-drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities.

Exercise training reduces circulating cytokines in male patients with coronary artery disease and type 2 diabetes: A pilot study

Low-grade inflammation is central to coronary artery disease (CAD) and type 2 diabetes (T2D) and is reduced by exercise training. The objective of this study was to compare the anti-inflammatory potential of moderate-to-vigorous intensity continuous training (MICT) and high-intensity interval training (HIIT) in patients with CAD with or without T2D. The design and setting of this study is based on a secondary analysis of registered randomized clinical trial NCT02765568. Male patients with CAD were randomly assigned to either MICT or HIIT, with subgroups divided according to T2D status (non-T2D-HIIT n = 14 and non-T2D-MICT n = 13; T2D-HIIT n = 6 and T2D-MICT n = 5). The intervention was a 12-week cardiovascular rehabilitation program consisting of either MICT or HIIT (twice weekly sessions) and circulating cytokines measured pre- and post-training as inflammatory markers. The co-occurrence of CAD and T2D was associated with increased plasma IL-8 (p = 0.0331). There was an interaction between T2D and the effect of the training interventions on plasma FGF21 (p = 0.0368) and IL-6 (p = 0.0385), which were further reduced in the T2D groups. An interaction between T2D, training modalities, and the effect of time (p = 0.0415) was detected for SPARC, with HIIT increasing circulating concentrations in the control group, while lowering them in the T2D group, and the inverse occurring with MICT. The interventions also reduced plasma FGF21 (p = 0.0030), IL-6 (p = 0.0101), IL-8 (p = 0.0087), IL-10 (p < 0.0001), and IL-18 (p = 0.0009) irrespective of training modality or T2D status. HIIT and MICT resulted in similar reductions in circulating cytokines known to be increased in the context of low-grade inflammation in CAD patients, an effect more pronounced in patients with T2D for FGF21 and IL-6.

Implications of genetic variations, differential gene expression, and allele-specific expression on metformin response in drug-naïve type 2 diabetes

PURPOSE

Metformin is widely used to treat type 2 diabetes mellitus (T2DM) individuals. Clinically, inter-individual variability of metformin response is of significant concern and is under interrogation. In this study, a targeted exome and whole transcriptome analysis were performed to identify predictive biomarkers of metformin response in drug-naïve T2DM individuals.

METHODS

The study followed a prospective study design. Drug-naïve T2DM individuals (n = 192) and controls (n = 223) were enrolled. T2DM individuals were administered with metformin monotherapy and defined as responders and non-responders based on their glycated haemoglobin change over three months. 146 T2DM individuals were used for the final analysis and remaining samples were lost during the follow-up. Target exome sequencing and RNA-seq was performed to analyze genetic and transcriptome profile. The selected SNPs were validated by genotyping and allele specific gene expression using the TaqMan assay. The gene prioritization, enrichment analysis, drug-gene interactions, disease-gene association, and correlation analysis were performed using various tools and databases.

RESULTS

rs1050152 and rs272893 in SLC22A4 were associated with improved response to metformin. The copy number loss was observed in PPARGC1A in the non-responders. The expression analysis highlighted potential differentially expressed targets for predicting metformin response (n = 35) and T2DM (n = 14). The expression of GDF15, TWISTNB, and RPL36A genes showed a maximum correlation with the change in HbA1c levels. The disease-gene association analysis highlighted MAGI2 rs113805659 to be linked with T2DM.

CONCLUSION

The results provide evidence for the genetic variations, perturbed transcriptome, allele-specific gene expression, and pathways associated with metformin drug response in T2DM.

Predictive genomic tools in disease stratification and targeted prevention: a recent update in personalized therapy advancements

In the current era of medical revolution, genomic testing has guided the healthcare fraternity to develop predictive, preventive, and personalized medicine. Predictive screening involves sequencing a whole genome to comprehensively deliver patient care via enhanced diagnostic sensitivity and specific therapeutic targeting. The best example is the application of whole-exome sequencing when identifying aberrant fetuses with healthy karyotypes and chromosomal microarray analysis in complicated pregnancies. To fit into today's clinical practice needs, experimental system biology like genomic technologies, and system biology viz., the use of artificial intelligence and machine learning is required to be attuned to the development of preventive and personalized medicine. As diagnostic techniques are advancing, the selection of medical intervention can gradually be influenced by a person's genetic composition or the cellular profiling of the affected tissue. Clinical genetic practitioners can learn a lot about several conditions from their distinct facial traits. Current research indicates that in terms of diagnosing syndromes, facial analysis techniques are on par with those of qualified therapists. Employing deep learning and computer vision techniques, the face image assessment software DeepGestalt measures resemblances to numerous of disorders. Biomarkers are essential for diagnostic, prognostic, and selection systems for developing personalized medicine viz. DNA from chromosome 21 is counted in prenatal blood as part of the Down's syndrome biomarker screening. This review is based on a detailed analysis of the scientific literature via a vigilant approach to highlight the applicability of predictive diagnostics for the development of preventive, targeted, personalized medicine for clinical application in the framework of predictive, preventive, and personalized medicine (PPPM/3 PM). Additionally, targeted prevention has also been elaborated in terms of gene-environment interactions and next-generation DNA sequencing. The application of 3 PM has been highlighted by an in-depth analysis of cancer and cardiovascular diseases. The real-time challenges of genome sequencing and personalized medicine have also been discussed.

The promising therapeutic effects of metformin on metabolic reprogramming of cancer-associated fibroblasts in solid tumors

Tumor-infiltrated lymphocytes are exposed to many toxic metabolites and molecules in the tumor microenvironment (TME) that suppress their anti-tumor activity. Toxic metabolites, such as lactate and ketone bodies, are produced mainly by catabolic cancer-associated fibroblasts (CAFs) to feed anabolic cancer cells. These catabolic and anabolic cells make a metabolic compartment through which high-energy metabolites like lactate can be transferred via the monocarboxylate transporter channel 4. Moreover, a decrease in molecules, including caveolin-1, has been reported to cause deep metabolic changes in normal fibroblasts toward myofibroblast differentiation. In this context, metformin is a promising drug in cancer therapy due to its effect on oncogenic signal transduction pathways, leading to the inhibition of tumor proliferation and downregulation of key oncometabolites like lactate and succinate. The cross-feeding and metabolic coupling of CAFs and tumor cells are also affected by metformin. Therefore, the importance of metabolic reprogramming of stromal cells and also the pivotal effects of metformin on TME and oncometabolites signaling pathways have been reviewed in this study.

Targeting aging mechanisms: pharmacological perspectives

Geroprotectors slow down aging and promote healthy longevity in model animals. Although hundreds of compounds have been shown to extend the life of laboratory model organisms, clinical studies on potential geroprotectors are exceedingly rare, especially in healthy elders. This review aims to classify potential geroprotectors based on the mechanisms by which they influence aging. These pharmacological interventions can be classified into the following groups: those that prevent oxidation; proteostasis regulators; suppressors of genomic instability; epigenetic drugs; those that preserve mitochondrial function; inhibitors of aging-associated signaling pathways; hormetins; senolytics/senostatics; anti-inflammatory drugs; antifibrotic agents; neurotrophic factors; factors preventing the impairment of barrier function; immunomodulators; and prebiotics, metabiotics, and enterosorbents.

Interaction between IgA and gut microbiota and its role in controlling metabolic syndrome

Immunoglobulin A (IgA) is the most abundant immunoglobulin isotype secreted into the mucosal tissues, mainly intestinal mucus. Humans can produce several grams of IgA every day, accounting for three quarters of the body's total immunoglobulin content. IgA, together with mucus and antimicrobial peptides, forms the first line of defence for intestinal epithelial cells, protecting them from a significant number of intestinal antigens. IgA also plays a principal role in controlling the gut microbiota (GM), and disruption in IgA can result in dysbiosis, such as the enrichment of Proteobacteria, which are generally bound by IgA. Proteobacteria overexpansion is also usually seen in obesity and colitis. Consistent with this, IgA dysfunction frequently results in metabolic syndrome (MetS), including conditions such as obesity, adiposity, insulin resistance, and inflammation. In contrast, enhanced IgA function can improve, and even prevent, MetS. Interactions among IgA, GM, and metabolism provide a promising avenue to combat MetS.

Still Living Better through Chemistry: An Update on Caloric Restriction and Caloric Restriction Mimetics as Tools to Promote Health and Lifespan

Caloric restriction (CR), the reduction of caloric intake without inducing malnutrition, is the most reproducible method of extending health and lifespan across numerous organisms, including humans. However, with nearly one-third of the world’s population overweight, it is obvious that caloric restriction approaches are difficult for individuals to achieve. Therefore, identifying compounds that mimic CR is desirable to promote longer, healthier lifespans without the rigors of restricting diet. Many compounds, such as rapamycin (and its derivatives), metformin, or other naturally occurring products in our diets (nutraceuticals), induce CR-like states in laboratory models. An alternative to CR is the removal of specific elements (such as individual amino acids) from the diet. Despite our increasing knowledge of the multitude of CR approaches and CR mimetics, the extent to which these strategies overlap mechanistically remains unclear. Here we provide an update of CR and CR mimetic research, summarizing mechanisms by which these strategies influence genome function required to treat age-related pathologies and identify the molecular fountain of youth.

Delivery room skin-to-skin contact in preterm infants affects long-term expression of stress response genes

Premature birth is a traumatic event that puts mother and child at risk for subsequent psychopathology. Skin-to-skin contact in the form of intermittent kangaroo mother care has been shown to positively affect the infant's stress response and cognitive development, but underlying mechanisms remain unclear. Moreover, first skin-to-skin contact is usually delayed for days after birth. In the delivery room skin-to-skin study (DR-SSC), a prospective randomized controlled trial conducted from 2/2012 to 7/2015, we set out to assess the effect of delivery room skin-to-skin contact on the infant's mRNA expression of six key molecules involved in stress response and neurobehavioral development at hospital discharge. 88 firstborn, singleton preterm infants (born at 25-32 weeks of gestational age) were included. In the delivery room after initial stabilization, infants were randomized to either 60 min of skin-to-skin or 5 min of visual contact with their mother. In this explorative add-on study on the original DR-SSC study, we determined the expression of six important stress response genes (CRHR1 and CRHR2, AVP, NR3C1, HTR2A, and SLC6A4) in peripheral white blood cells of infants during routine blood sampling upon hospital discharge (corrected gestational age of 40 weeks). Infants were followed up to six months corrected age. Relative mRNA expression of the corticotropin releasing hormone receptor 2 (CRH R2), the glucocorticoid receptor gene (NR3C1), and the serotonin transporter gene (SLC6A4) was significantly reduced in the delivery room SSC infants. Additionally, gene expression of CRH R2 showed a correlation with HPA axis reactivity and parameters of mother-child interaction at six months corrected age. Our results highlight the importance of delivery room mother-child skin-to-skin contact and underline the urgent need for in-depth studies on the underlying molecular mechanisms.

Whole-blood transcriptome profiling reveals signatures of metformin and its therapeutic response

Metformin, a biguanide agent, is the first-line treatment for type 2 diabetes mellitus due to its glucose-lowering effect. Despite its wide application in the treatment of multiple health conditions, the glycemic response to metformin is highly variable, emphasizing the need for reliable biomarkers. We chose the RNA-Seq-based comparative transcriptomics approach to evaluate the systemic effect of metformin and highlight potential predictive biomarkers of metformin response in drug-naïve volunteers with type 2 diabetes in vivo. The longitudinal blood-derived transcriptome analysis revealed metformin-induced differential expression of novel and previously described genes involved in cholesterol homeostasis (SLC46A1 and LRP1), cancer development (CYP1B1, STAB1, CCR2, TMEM176B), and immune responses (CD14, CD163) after administration of metformin for three months. We demonstrate for the first time a transcriptome-based molecular discrimination between metformin responders (delta HbA1c ≥ 1% or 12.6 mmol/mol) and non-responders (delta HbA1c < 1% or 12.6 mmol/mol), that is determined by expression levels of 56 genes, explaining 13.9% of the variance in the therapeutic efficacy of the drug. Moreover, we found a significant upregulation of IRS2 gene (log2FC 0.89) in responders compared to non-responders before the use of metformin. Finally, we provide evidence for the mitochondrial respiratory complex I as one of the factors related to the high variability of the therapeutic response to metformin in patients with type 2 diabetes mellitus.

High-Throughput Transcriptome Profiling in Drug and Biomarker Discovery

The development of new drugs is multidisciplinary and systematic work. High-throughput techniques based on “-omics” have driven the discovery of biomarkers in diseases and therapeutic targets of drugs. A transcriptome is the complete set of all RNAs transcribed by certain tissues or cells at a specific stage of development or physiological condition. Transcriptome research can demonstrate gene functions and structures from the whole level and reveal the molecular mechanism of specific biological processes in diseases. Currently, gene expression microarray and high-throughput RNA-sequencing have been widely used in biological, medical, clinical, and drug research. The former has been applied in drug screening and biomarker detection of drugs due to its high throughput, fast detection speed, simple analysis, and relatively low price. With the further development of detection technology and the improvement of analytical methods, the detection flux of RNA-seq is much higher but the price is lower, hence it has powerful advantages in detecting biomarkers and drug discovery. Compared with the traditional RNA-seq, scRNA-seq has higher accuracy and efficiency, especially the single-cell level of gene expression pattern analysis can provide more information for drug and biomarker discovery. Therefore, (sc)RNA-seq has broader application prospects, especially in the field of drug discovery. In this overview, we will review the application of these technologies in drug, especially in natural drug and biomarker discovery and development. Emerging applications of scRNA-seq and the third generation RNA-sequencing tools are also discussed.