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

Exploring metformin monotherapy response in Type-2 diabetes: Computational insights through clinical, genomic, and proteomic markers using machine learning algorithms

BACKGROUND

In 2016, the UK had 4.5 million people with diabetes, predominantly Type-2 Diabetes Mellitus (T2DM). The NHS allocates £10 billion (9% of its budget) to manage diabetes. Metformin is the primary treatment for T2DM, but 35% of patients don't benefit from it, leading to complications. This study aims to delve into metformin's efficacy using clinical, genomic, and proteomic data to uncover new biomarkers and build a Machine Learning predictor for early metformin response detection.

METHODS

Here we report analysis from a T2DM dataset of individuals prescribed metformin monotherapy from the Diastrat cohort recruited at the Altnagelvin Area Hospital, Northern Ireland.

RESULTS

In the clinical data analysis, comparing responders (those achieving HbA1c ≤ 48 mmol/mol) to non-responders (with HbA1c > 48 mmol/mol), we identified that creatinine levels and bodyweight were more negatively correlated with response than non-response. In genomic analysis, we identified statistically significant (p-value <0.05) variants rs6551649 (LPHN3), rs6551654 (LPHN3), rs4495065 (LPHN3) and rs7940817 (TRPC6) which appear to differentiate the responders and non-responders. In proteomic analysis, we identified 15 statistically significant (p-value <0.05, q-value <0.05) proteomic markers that differentiate controls, responders, non-responders and treatment groups, out of which the most significant were HAOX1, CCL17 and PAI that had fold change ∼2. A machine learning model was build; the best model predicted non-responders with 83% classification accuracy.

CONCLUSION

Further testing in prospective validation cohorts is required to determine the clinical utility of the proposed model.

Differential methylation pattern in pubertal girls associated with biochemical premature adrenarche

Biochemical premature adrenarche is defined by elevated serum DHEAS [≥40 μg/dL] before age 8 y in girls. This condition is receiving more attention due to its association with obesity, hyperinsulinemia, dyslipidemia, and polycystic ovary syndrome. Nevertheless, the link between early androgen excess and these risk factors remains unknown. Epigenetic modifications, and specifically DNA methylation, have been associated with the initiation and progression of numerous disorders, including obesity and insulin resistance. The aim of this study was to determine if prepubertal androgen exposure is associated with a different methylation profile in pubertal girls. Eighty-six healthy girls were studied. At age 7 y, anthropometric measurements were begun and DHEAS levels were determined. Girls were classified into Low DHEAS (LD) [<42 μg/dL] and High DHEAS (HD) [≥42 μg/dL] groups. At Tanner stages 2 and 4 a DNA methylation microarray was performed to identify differentially methylated CpG positions (DMPs) between HD and LD groups. We observed a differential methylation pattern between pubertal girls with and without biochemical PA. Moreover, a set of DNA methylation markers, selected by the LASSO method, successfully distinguished between HD and LD girls regardless of Tanner stage. Additionally, a subset of these markers were significantly associated with glucose-related measures such as insulin level, HOMA-IR, and glycaemia. This pilot study provides evidence consistent with the hypothesis that high DHEAS concentration, or its hormonally active metabolites, may induce a unique blood methylation signature in pubertal girls, and that this methylation pattern is associated with altered glucose metabolism.

Precision Medicine in Diabetes

Tailoring treatment or management to groups of individuals based on specific clinical, molecular, and genomic features is the concept of precision medicine. Diabetes is highly heterogenous with respect to clinical manifestations, disease progression, development of complications, and drug response. The current practice for drug treatment is largely based on evidence from clinical trials that report average effects. However, around half of patients with type 2 diabetes do not achieve glycaemic targets despite having a high level of adherence and there are substantial differences in the incidence of adverse outcomes. Therefore, there is a need to identify predictive markers that can inform differential drug responses at the point of prescribing. Recent advances in molecular genetics and increased availability of real-world and randomised trial data have started to increase our understanding of disease heterogeneity and its impact on potential treatments for specific groups. Leveraging information from simple clinical features (age, sex, BMI, ethnicity, and co-prescribed medications) and genomic markers has a potential to identify sub-groups who are likely to benefit from a given drug with minimal adverse effects. In this chapter, we will discuss the state of current evidence in the discovery of clinical and genetic markers that have the potential to optimise drug treatment in type 2 diabetes.

An in silico comparative transcriptome analysis identifying hub lncRNAs and mRNAs in brain metastatic small cell lung cancer (SCLC)

Small cell lung cancer (SCLC) is a particularly lethal subtype of lung cancer. Metastatic lung tumours lead to most deaths from lung cancer. Predicting and preventing tumour metastasis is crucially essential for patient survivability. Hence, in the current study, we focused on a comprehensive analysis of lung cancer patients' differentially expressed genes (DEGs) on brain metastasis cell lines. DEGs are analysed through KEGG and GO databases for the most critical biological processes and pathways for enriched DEGs. Additionally, we performed protein-protein interaction (PPI), GeneMANIA, and Kaplan-Meier survival analyses on our DEGs. This article focused on mRNA and lncRNA DEGs for LC patients with brain metastasis and underlying molecular mechanisms. The expression data was gathered from the Gene Expression Omnibus database (GSE161968). We demonstrate that 30 distinct genes are up-expressed in brain metastatic SCLC patients, and 31 genes are down-expressed. All our analyses show that these genes are involved in metastatic SCLC. PPI analysis revealed two hub genes (CAT and APP). The results of this article present three lncRNAs, Including XLOC_l2_000941, LOC100507481, and XLOC_l2_007062, also notable mRNAs, have a close relation with brain metastasis in lung cancer and may have a role in the epithelial-mesenchymal transition (EMT) in tumour cells.

The Interplay of Four Main Pathways Recomposes Immune Landscape in Primary and Metastatic Gastroenteropancreatic Neuroendocrine Tumors

Background

The four major pathways in gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) including chromatin remodeling, DNA damage repair, activation of mTOR signaling, and telomere maintenance were mediated by some critical molecules and constituted critical processes of regulation in cancer-causing processes. However, the interplay and potential role of these pathway-related molecules in the tumor microenvironment of the primary and metastatic site remained unknown.

Methods

We systematically evaluated the mRNA expression of 34 molecules associated with the four pathways in 227 GEP−NEN samples from 5 datasets. We assigned the samples into two expression patterns of pathway-related molecules by an unsupervised clustering method. Subsequently, we explored the specific cell-related molecules, especially immune and stromal cells using the WGCNA method, based on differentially expressed genes (DEGs) responsible for the different patterns of pathway-related molecules, which provided a new method to qualify the pathway-related subtypes of individual tumors, then the PC_Score and PI_Score scoring systems were also constructed using obtained specific cell-related molecules. Furthermore, we performed the association of pathway-related subtypes with characteristics of immune landscape in primary and metastatic GEP-NENs.

Results

We demonstrated that the specific pathway-related molecules (SMARCA4, MLH1, TSC1, ATRX, and ATR) were associated with cytolytic activity. Then we identified the two distinct patterns of pathway-related molecules, which were characteristic with a significantly distinct immune landscape. Using WGCNA, we also identified the fibroblast-related molecules, including ASPN, COL10A1, COL3A1, EDNRA, MYL9, PRELP, RAB31, SPARC, and THBS2, and immune-related molecules including CASP1, CCL5, CTSS, CYBRD1, PMP22, and TFEC. Based on these specific markers, we identified four distinct pathway-related subtypes, characterized by immune and fibrotic enriched (I/FE), immune enriched (IE), fibrotic enriched (FE), and immune and fibrotic desert (I/FD), of which I/FE was characteristic with the highest PC_Score and PI_Score whereas I/FD presents the opposite trend. I/FE was positively correlated with immune landscape of T-cell activation and immunosuppression. Furthermore, the I/FE marked GEP-NENs with increased immune activation scores (T-cell costimulation, MHC I presentation, and APC costimulation). Importantly, the four distinct pathway-related subtypes were not conserved in different tumor sites, because I/FE was lacking in the liver metastatic site even though IE, FE, and I/FD also could be observed in the metastatic site.

Conclusions

This study was the first to perform a comprehensive analysis of the four major pathways in GEP-NENs. We demonstrated the potential function of these pathway-related molecules in immune landscapes. Our findings indicated that the primary and metastatic GEP-NENs had distinct antitumor phenotypes. This work highlighted the interplay and potential clinical utility of these pathway-related molecules in GEP-NENs.

Recent Multiomics Approaches in Endometrial Cancer

Endometrial cancer is the most common gynecological cancers in developed countries. Many of the mechanisms involved in its initiation and progression remain unclear. Analysis providing comprehensive data on the genome, transcriptome, proteome, and epigenome could help in selecting molecular markers and targets in endometrial cancer. Multiomics approaches can reveal disturbances in multiple biological systems, giving a broader picture of the problem. However, they provide a large amount of data that require processing and further integration prior to analysis. There are several repositories of multiomics datasets, including endometrial cancer data, as well as portals allowing multiomics data analysis and visualization, including Oncomine, UALCAN, LinkedOmics, and miRDB. Multiomics approaches have also been applied in endometrial cancer research in order to identify novel molecular markers and therapeutic targets. This review describes in detail the latest findings on multiomics approaches in endometrial cancer.

Integration of transcriptomics and system pharmacology to reveal the therapeutic mechanism underlying Qingfei Xiaoyan Wan to treat allergic asthma

ETHNOPHARMACOLOGICAL RELEVANCE

Asthma is a chronic inflammatory disease, characterized by airway inflammation, hyperresponsiveness, and bronchial smooth muscle contraction. Qingfei Xiaoyan Wan (QFXYW), a traditional Chinese formula, has been shown to exert anti-asthma effects and immune response in multiple diseases.

AIM OF THIS STUDY

In this study, we evaluated the therapeutic mechanism of QFXYW in the suppression of allergic asthma by integrating of transcriptomics and system pharmacology.

MATERIALS AND METHODS

BALB/c mice were sensitized with ovalbumin (OVA) to establish the allergic asthma model, and its success was confirmed with behavioral observations. Lung histopathological analysis, inflammatory pathology scores, transcription factors were used to evaluate the effects of QFXYW on allergic asthma. The therapeutic mechanism of QFXYW in treating allergic asthma through integrated transcriptomics and system pharmacology was then determined: hub genes were screened out by topological analysis and functional enrichment analysis were performed to identify key signaling pathway. Subsequently, quantitative RP-PCR and protein array were performed to detect the mRNA of hub genes and to predict the key pathway in OVA-induced allergic asthma, respectively.

RESULTS

Our results demonstrated that QFXYW could significantly attenuate inflammatory cell infiltration, mucus secretion, and epithelial damage. The transcriptomics analysis found the six hub genes with the highest values- CXCL10, CXCL2, CXCL1, IL-6, CCL-5, and CCL-4 were screened out. Functional enrichment analysis showed that the differentially expressed genes (DEGs) were mainly enriched in the inflammatory response and cytokine signaling pathway. Moreover, the quantitative RT-PCR verification experiment found the CXCL2 and CXCL1 were significantly suppressed after treatment with QFXYW. The results of protein array showed that QFXYW inhibited the multi-cytokines of OVA-induced allergic asthma via cytokine signaling pathway.

CONCLUSIONS

QFXYW may have mediated OVA-induced allergic asthma mainly through the hub genes CXCL2, CXCL1, and the cytokine signaling pathway. This finding will offer a novel strategy to explore effective and safe mechanism of Traditional Chinese Medicine (TCM) formula to treat allergic asthma.

Commentary: Novel strategies and new tools to curtail the health effects of pesticides

BACKGROUND

Flaws in the science supporting pesticide risk assessment and regulation stand in the way of progress in mitigating the human health impacts of pesticides. Critical problems include the scope of regulatory testing protocols, the near-total focus on pure active ingredients rather than formulated products, lack of publicly accessible information on co-formulants, excessive reliance on industry-supported studies coupled with reticence to incorporate published results in the risk assessment process, and failure to take advantage of new scientific opportunities and advances, e.g. biomonitoring and "omics" technologies.

RECOMMENDED ACTIONS

Problems in pesticide risk assessment are identified and linked to study design, data, and methodological shortcomings. Steps and strategies are presented that have potential to deepen scientific knowledge of pesticide toxicity, exposures, and risks. We propose four solutions: (1) End near-sole reliance in regulatory decision-making on industry-supported studies by supporting and relying more heavily on independent science, especially for core toxicology studies. The cost of conducting core toxicology studies at labs not affiliated with or funded directly by pesticide registrants should be covered via fees paid by manufacturers to public agencies. (2) Regulators should place more weight on mechanistic data and low-dose studies within the range of contemporary exposures. (3) Regulators, public health agencies, and funders should increase the share of exposure-assessment resources that produce direct measures of concentrations in bodily fluids and tissues. Human biomonitoring is vital in order to quickly identify rising exposures among vulnerable populations including applicators, pregnant women, and children. (4) Scientific tools across disciplines can accelerate progress in risk assessments if integrated more effectively. New genetic and metabolomic markers of adverse health impacts and heritable epigenetic impacts are emerging and should be included more routinely in risk assessment to effectively prevent disease.

CONCLUSIONS

Preventing adverse public health outcomes triggered or made worse by exposure to pesticides will require changes in policy and risk assessment procedures, more science free of industry influence, and innovative strategies that blend traditional methods with new tools and mechanistic insights.

Multidimensional mechanisms of metformin in cancer treatment

Metformin has been in clinical use for more than half a century, yet its molecular mechanism of action is not entirely understood. Metformin has been shown to have antiproliferative and synergistic effects on various types of cancers. The anticancer effects of metformin are potentially applicable to both diabetic and nondiabetic patients. Areas of ongoing investigation focus on metformin's ability to activate adenosine monophosphate kinase (AMPK), in addition to its effect on Myc mRNA, monocarboxylate transporter 1 (MCT1), hypoxia-inducible factor 1 (HIF1), mammalian target of rapamycin (mTOR), and human epidermal growth factor receptor 2 (HER2). Additional anticancer effects are exhibited by acting on liver kinase B1 (LKB1), CREB-regulated transcription coactivator 2 (CRTC2), nitric oxide, and reactive oxygen species. Further investigation will be focused on elucidating metformin's metal-binding properties and how they may be harnessed for their anticancer effect. The acquired knowledge about metformin properties has expanded the number of targets for drug discovery such as microRNA, hexokinase, adenylate cyclase, transcription factors, various cyclins, and copper. In order to design anticancer drugs that mimic metformin's mechanism of action, binding assay studies must be conducted to fully understand and utilize the AMPK-dependent and independent mechanisms. Metformin's complex mechanisms that can potentially make this drug a multifaceted therapy targeting tumorigenesis in addition to information from ongoing clinical trials implicate that metformin can be a potential chemotherapeutic drug or adjuvant that could prove to be vital to future strategies against several types of cancer.

Acute Effects on the Human Peripheral Blood Transcriptome of Decompression Sickness Secondary to Scuba Diving

Decompression sickness (DCS) develops due to inert gas bubble formation in bodily tissues and in the circulation, leading to a wide range of potentially serious clinical manifestations. Its pathophysiology remains incompletely understood. In this study, we aim to explore changes in the human leukocyte transcriptome in divers with DCS compared to closely matched unaffected controls after uneventful diving. Cases (n = 7) were divers developing the typical cutis marmorata rash after diving with a confirmed clinical diagnosis of DCS. Controls (n = 6) were healthy divers who surfaced from a ≥25 msw dive without decompression violation or evidence of DCS. Blood was sampled at two separate time points-within 8 h of dive completion and 40-44 h later. Transcriptome analysis by RNA-Sequencing followed by bioinformatic analysis was carried out to identify differentially expressed genes and relate their function to biological pathways. In DCS cases, we identified enrichment of transcripts involved in acute inflammation, activation of innate immunity and free radical scavenging pathways, with specific upregulation of transcripts related to neutrophil function and degranulation. DCS-induced transcriptomic events were reversed at the second time point following exposure to hyperbaric oxygen. The observed changes are consistent with findings from animal models of DCS and highlight a continuum between the responses elicited by uneventful diving and diving complicated by DCS. This study sheds light on the inflammatory pathophysiology of DCS and the associated immune response. Such data may potentially be valuable in the search for novel treatments targeting this disease.

Metformin Strongly Affects Gut Microbiome Composition in High-Fat Diet-Induced Type 2 Diabetes Mouse Model of Both Sexes

Effects of metformin, the first-line drug for type 2 diabetes therapy, on gut microbiome composition in type 2 diabetes have been described in various studies both in human subjects and animals. However, the details of the molecular mechanisms of metformin action have not been fully understood. Moreover, there is a significant lack of information on how metformin affects gut microbiome composition in female mouse models, depending on sex and metabolic status in well controlled experimental setting. Our study aimed to examine metformin-induced alterations in gut microbiome diversity, composition, and functional implications of high-fat diet-induced type 2 diabetes mouse model, using, for the first time in mice study, the shotgun metagenomic sequencing that allows estimation of microorganisms at species level. We also employed a randomized block, factorial study design, and including 24 experimental units allocated to 8 treatment groups to systematically evaluate the effect of sex and metabolic status on metformin interaction with microbiome. We used DNA obtained from fecal samples representing gut microbiome before and after ten weeks-long metformin treatment. We identified 100 metformin-related differentially abundant species in high-fat diet-fed mice before and after the treatment, with most of the species relative abundances increased. In contrast, no significant changes were observed in control diet-fed mice. Functional analysis targeted to carbohydrate, lipid, and amino acid metabolism pathways revealed 14 significantly altered hierarchies. We also observed sex-specific differences in response to metformin treatment. Males experienced more pronounced changes in metabolic markers, while in females the extent of changes in gut microbiome representatives was more marked, indicated by 53 differentially abundant species with more remarkable Log fold changes compared to the combined-sex analysis. The same pattern manifested regarding the functional analysis, where we discovered 5 significantly affected hierarchies in female groups but not in males. Our results suggest that both sexes of animals should be included in future studies focusing on metformin effects on the gut microbiome.

Shared Blood Transcriptomic Signatures between Alzheimer's Disease and Diabetes Mellitus

Alzheimer’s disease (AD) and diabetes mellitus (DM) are known to have a shared molecular mechanism. We aimed to identify shared blood transcriptomic signatures between AD and DM. Blood expression datasets for each disease were combined and a co-expression network was used to construct modules consisting of genes with similar expression patterns. For each module, a gene regulatory network based on gene expression and protein-protein interactions was established to identify hub genes. We selected one module, where COPS4, PSMA6, GTF2B, GTF2F2, and SSB were identified as dysregulated transcription factors that were common between AD and DM. These five genes were also differentially co-expressed in disease-related tissues, such as the brain in AD and the pancreas in DM. Our study identified gene modules that were dysregulated in both AD and DM blood samples, which may contribute to reveal common pathophysiology between two diseases.