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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.

The gut microbiome is a versatile system regulating numerous aspects of host metabolism. Among other traits, variations in the composition of gut microbial communities are related to blood lipid patterns and hyperlipidaemia, yet inconsistent association patterns exist. This study aims to assess the relationships between the composition of the gut microbiome and variations in lipid profiles among healthy adults. This study used data and samples from 23 adult participants of a previously conducted dietary intervention study. Circulating lipid measurements and whole-metagenome sequences of the gut microbiome were derived from 180 blood and faecal samples collected from eight visits distributed across an 11-week study. Lipid-related variables explained approximately 4.5% of the variation in gut microbiome compositions, with higher effects observed for total cholesterol and high-density lipoproteins. Species from the genera Odoribacter, Anaerostipes, and Parabacteroides correlated with increased serum lipid levels, whereas probiotic species like Akkermansia muciniphila were more abundant among participants with healthier blood lipid profiles. An inverse correlation with serum cholesterol was also observed for Massilistercora timonensis, a player in regulating lipid turnover. The observed correlation patterns add to the growing evidence supporting the role of the gut microbiome as an essential regulator of host lipid metabolism.

Ryegrass mottle virus (RGMoV; genus: Sobemovirus) is a single-stranded positive RNA virus with a 30 nm viral particle size. It exhibits T = 3 symmetry with 180 coat protein (CP) subunits forming a viral structure. The RGMoV genome comprises five open reading frames that encode P1, Px, a membrane-anchored 3C-like serine protease, a viral genome-linked protein, P16, an RNA-dependent RNA polymerase, and CP. The RGMoV genome size varies, ranging from 4175 nt (MW411579.1) to 4253 nt (MW411579.1) in the deposited sequences. An earlier deposited RGMoV complete genome sequence of 4212 nt length (EF091714.1) was used to develop an infectious complementary DNA (icDNA) construct for in vitro gRNA transcription from the T7 promoter. However, viral infection was not induced when the transcribed gRNA was introduced into oat plants, indicating the potential absence of certain sequences in either the 5' or 3' untranslated regions (UTR) or both. The complete sequence of the 3' UTR was determined through 3' end RACE, while the 5' UTR was identified using high-throughput sequencing (HTS)-RNA-Seq to resolve the potential absences. Only the icDNA vector containing the newly identified UTR sequences proved infectious, resulting in typical viral infection symptoms and subsequent propagation of progeny viruses, exhibiting the ability to cause repeated infections in oat plants after at least one passage. The successful generation of icDNA highlighted the synergistic potential of utilizing both methods when a single approach failed. Furthermore, this study demonstrated the reliability of HTS as a method for determining the complete genome sequence of viral genomes.

Abstract Background Pituitary adenomas (PA) have an increased potential for relapse in one to 5 years after resection. In this study, we investigated the genetic differences in genomic DNA of primary and rapidly recurrent tumours in the same patient to explain the causality mechanisms of PA recurrence. Case presentation The patient was a 69-year-old female with non-functional pituitary macroadenoma with extension into the left cavernous sinus (Knosp grade 2) who underwent craniotomy and partial resection in August 2010. Two years later, the patient had prolonged tumour growth with an essential suprasellar extension (Knosp grade 2), and a second craniotomy with partial tumour resection was performed in September 2012. In both tumours, the KI-67 level was below 1.5%. Exome sequencing via semiconductor sequencing of patient germline DNA and somatic DNA from both tumours was performed. Tmap alignment and Platypus variant calling were performed followed by variant filtering and manual review with IGV software. We observed an increased load of missense variants in the recurrent PA tumour when compared to the original tumour. The number of detected variants increased from ten to 26 and potential clonal expansion of four variants was observed. Additionally, targeted SNP analysis revealed five rare missense SNPs with a potential impact on the function of the encoded proteins. Conclusions In this case study, an SNP located in HRAS is the most likely candidate inducing rapid PA progression. The relapsed PA tumour had a higher variation load and fast tumour recurrence in this patient could be caused by clonal expansion of the leftover tumour tissue.

Over the decades, practical biotechnology researchers have aimed to improve naturally occurring proteins and create novel ones. It is widely recognized that coupling protein sequence randomization with various effect screening methodologies is one of the most powerful techniques for quickly, efficiently, and purposefully acquiring these desired improvements. Over the years, considerable advancements have been made in this field. However, developing PCR-based or template-guided methodologies has been hampered by resultant template sequence biases. Here, we present a novel whole plasmid amplification-based approach, which we named OverFlap PCR, for randomizing virtually any region of plasmid DNA without introducing a template sequence bias.

ABSTRACT The heterogeneity in severity and outcome of COVID-19 cases points out the urgent need for early molecular characterization of patients followed by risk-stratified care. The main objective of this study was to evaluate the fluctuations of serum metabolomic profiles of COVID-19 patients with severe illness during the different disease stages in a longitudinal manner. We demonstrate a distinct metabolomic signature in serum samples of 32 hospitalized patients at the acute phase compared to the recovery period, suggesting the tryptophan (tryptophan, kynurenine, and 3-hydroxy-DL-kynurenine) and arginine (citrulline and ornithine) metabolism as contributing pathways in the immune response to SARS-CoV-2 with a potential link to the clinical severity of the disease. In addition, we suggest that glutamine deprivation may further result in inhibited M2 macrophage polarization as a complementary process, and highlight the contribution of phenylalanine and tyrosine in the molecular mechanisms underlying the severe course of the infection. In conclusion, our results provide several functional metabolic markers for disease progression and severe outcome with potential clinical application. IMPORTANCE Although the host defense mechanisms against SARS-CoV-2 infection are still poorly described, they are of central importance in shaping the course of the disease and the possible outcome. Metabolomic profiling may complement the lacking knowledge of the molecular mechanisms underlying clinical manifestations and pathogenesis of COVID-19. Moreover, early identification of metabolomics-based biomarker signatures is proved to serve as an effective approach for the prediction of disease outcome. Here we provide the list of metabolites describing the severe, acute phase of the infection and bring the evidence of crucial metabolic pathways linked to aggressive immune responses. Finally, we suggest metabolomic phenotyping as a promising method for developing personalized care strategies in COVID-19 patients.

Remaining a major healthcare concern with nearly 29 million confirmed cases worldwide at the time of writing, novel severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has caused more than 920 thousand deaths since its outbreak in China, December 2019. First case of a person testing positive for SARS-CoV-2 infection within the territory of the Republic of Latvia was registered on 2nd of March 2020, 9 days prior to the pandemic declaration by WHO. Since then, more than 277,000 tests were carried out confirming a total of 1,464 cases of coronavirus disease 2019 (COVID-19) in the country as of 12th of September 2020. Rapidly reacting to the spread of the infection, an ongoing sequencing campaign was started mid-March in collaboration with the local testing laboratories, with an ultimate goal in sequencing as much local viral isolates as possible, resulting in first full-length SARS-CoV-2 isolate genome sequences from the Baltics region being made publicly available in early April. With 133 viral isolates representing ~9.1% of the total COVID-19 cases during the “first coronavirus wave” in the country (early March, 2020—mid-September, 2020) being completely sequenced as of today, here, we provide a first report on the genetic diversity of Latvian SARS-CoV-2 isolates.

Acromegaly is a disease mainly caused by pituitary neuroendocrine tumor (PitNET) overproducing growth hormone. First-line medication for this condition is the use of somatostatin analogs (SSAs), that decrease tumor mass and induce antiproliferative effects on PitNET cells. Dopamine agonists (DAs) can also be used if SSA treatment is not effective. This study aimed to determine differences in transcriptome signatures induced by SSA/DA therapy in PitNET tissue. We selected tumor tissue from twelve patients with somatotropinomas, with half of the patients receiving SSA/DA treatment before surgery and the other half treatment naive. Transcriptome sequencing was then carried out to identify differentially expressed genes (DEGs) and their protein–protein interactions, using pathway analyses. We found 34 upregulated and six downregulated DEGs in patients with SSA/DA treatment. Three tumor development promoting factors MUC16, MACC1, and GRHL2, were significantly downregulated in therapy administered PitNET tissue; this finding was supported by functional studies in GH3 cells. Protein–protein interactions and pathway analyses revealed extracellular matrix involvement in the antiproliferative effects of this type of the drug treatment, with pronounced alterations in collagen regulation. Here, we have demonstrated that somatotropinomas can be distinguished based on their transcriptional profiles following SSA/DA therapy, and SSA/DA treatment does indeed cause changes in gene expression. Treatment with SSA/DA significantly downregulated several factors involved in tumorigenesis, including MUC16, MACC1, and GRHL2. Genes that were upregulated, however, did not have a direct influence on antiproliferative function in the PitNET cells. These findings suggested that SSA/DA treatment acted in a tumor suppressive manner and furthermore, collagen related interactions and pathways were enriched, implicating extracellular matrix involvement in this anti-tumor effect of drug treatment.

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.

BackgroundThe study was conducted to investigate the effects of metformin treatment on the human gut microbiome's taxonomic and functional profile in the Latvian population, and to evaluate the correlation of these changes with therapeutic efficacy and tolerance.MethodsIn this longitudinal observational study, stool samples for shotgun metagenomic sequencing-based analysis were collected in two cohorts. The first cohort included 35 healthy nondiabetic individuals (metformin dose 2x850mg/day) at three time-points during metformin administration. The second cohort was composed of 50 newly-diagnosed type 2 diabetes patients (metformin dose-determined by an endocrinologist) at two concordant times. Patients were defined as Responders if their HbA1c levels during three months of metformin therapy had decreased by ≥12.6 mmol/mol (1%), while in Non-responders HbA1c were decreased by ResultsMetformin reduced the alpha diversity of microbiota in healthy controls (p = 0.02) but not in T2D patients. At the species level, reduction in the abundance of Clostridium bartlettii and Barnesiella intestinihominis, as well as an increase in the abundance of Parabacteroides distasonis and Oscillibacter unclassified overlapped between both study groups. A large number of group-specific changes in taxonomic and functional profiles was observed. We identified an increased abundance of Prevotella copri (FDR = 0.01) in the Non-Responders subgroup, and enrichment of Enterococcus faecium, Lactococcus lactis, Odoribacter, and Dialister at baseline in the Responders group. Various taxonomic units were associated with the observed incidence of side effects in both cohorts.ConclusionsMetformin effects are different in T2D patients and healthy individuals. Therapy induced changes in the composition of gut microbiome revealed possible mediators of observed short-term therapeutic effects. The baseline composition of the gut microbiome may influence metformin therapy efficacy and tolerance in T2D patients and could be used as a powerful prediction tool.

Abstract Background Metformin is a widely prescribed antihyperglycemic agent that has been also associated with multiple therapeutic effects in various diseases, including several types of malignancies. There is growing evidence regarding the contribution of the epigenetic mechanisms in reaching metformin’s therapeutic goals; however, the effect of metformin on human cells in vivo is not comprehensively studied. The aim of our study was to examine metformin-induced alterations of DNA methylation profiles in white blood cells of healthy volunteers, employing a longitudinal study design. Results Twelve healthy metformin-naïve individuals where enrolled in the study. Genome-wide DNA methylation pattern was estimated at baseline, 10 h and 7 days after the start of metformin administration. The whole-genome DNA methylation analysis in total revealed 125 differentially methylated CpGs, of which 11 CpGs and their associated genes with the most consistent changes in the DNA methylation profile were selected: POFUT2, CAMKK1, EML3, KIAA1614, UPF1, MUC4, LOC727982, SIX3, ADAM8, SNORD12B, VPS8, and several differentially methylated regions as novel potential epigenetic targets of metformin. The main functions of the majority of top-ranked differentially methylated loci and their representative cell signaling pathways were linked to the well-known metformin therapy targets: regulatory processes of energy homeostasis, inflammatory responses, tumorigenesis, and neurodegenerative diseases. Conclusions Here we demonstrate for the first time the immediate effect of short-term metformin administration at therapeutic doses on epigenetic regulation in human white blood cells. These findings suggest the DNA methylation process as one of the mechanisms involved in the action of metformin, thereby revealing novel targets and directions of the molecular mechanisms underlying the various beneficial effects of metformin. Trial registration EU Clinical Trials Register, 2016-001092-74. Registered 23 March 2017, https://www.clinicaltrialsregister.eu/ctr-search/trial/2016-001092-74/LV.

Metformin is a commonly used antihyperglycaemic agent for the treatment of type 2 diabetes mellitus. Nevertheless, the exact mechanisms of action, underlying the various therapeutic effects of metformin, remain elusive. The goal of this study was to evaluate the alterations in longitudinal whole-blood transcriptome profiles of healthy individuals after a one-week metformin intervention in order to identify the novel molecular targets and further prompt the discovery of predictive biomarkers of metformin response. Next generation sequencing-based transcriptome analysis revealed metformin-induced differential expression of genes involved in intestinal immune network for IgA production and cytokine-cytokine receptor interaction pathways. Significantly elevated faecal sIgA levels during administration of metformin, and its correlation with the expression of genes associated with immune response (CXCR4, HLA-DQA1, MAP3K14, TNFRSF21, CCL4, ACVR1B, PF4, EPOR, CXCL8) supports a novel hypothesis of strong association between metformin and intestinal immune system, and for the first time provide evidence for altered RNA expression as a contributing mechanism of metformin's action. In addition to universal effects, 4 clusters of functionally related genes with a subject-specific differential expression were distinguished, including genes relevant to insulin production (HNF1B, HNF1A, HNF4A, GCK, INS, NEUROD1, PAX4, PDX1, ABCC8, KCNJ11) and cholesterol homeostasis (APOB, LDLR, PCSK9). This inter-individual variation of the metformin effect on the transcriptional regulation goes in line with well-known variability of the therapeutic response to the drug.

BACKGROUND:Metformin is a widely used first-line drug for treatment of type 2 diabetes. Despite its advantages, metformin has variable therapeutic effects, contraindications, and side effects. Here, for the very first time, we investigate the short-term effect of metformin on the composition of healthy human gut microbiota. METHODS:We used an exploratory longitudinal study design in which the first sample from an individual was the control for further samples. Eighteen healthy individuals were treated with metformin (2 × 850 mg) for 7 days. Stool samples were collected at three time points: prior to administration, 24 hours and 7 days after metformin administration. Taxonomic composition of the gut microbiome was analyzed by massive parallel sequencing of 16S rRNA gene (V3 region). RESULTS:There was a significant reduction of inner diversity of gut microbiota observed already 24 hours after metformin administration. We observed an association between the severity of gastrointestinal side effects and the increase in relative abundance of common gut opportunistic pathogen Escherichia-Shigella spp. One week long treatment with metformin was associated with a significant decrease in the families Peptostreptococcaceae and Clostridiaceae_1 and four genera within these families. CONCLUSIONS:Our results are in line with previous findings on the capability of metformin to influence gut microbiota. However, for the first time we provide evidence that metformin has an immediate effect on the gut microbiome in humans. It is likely that this effect results from the increase in abundance of opportunistic pathogens and further triggers the occurrence of side effects associated with the observed dysbiosis. An additional randomized controlled trial would be required in order to reach definitive conclusions, as this is an exploratory study without a placebo control arm. Our findings may be further used to create approaches that improve the tolerability of metformin.

ABSTRACTAntidiabetic drug metformin alters the gut microbiome composition in the context of type 2 diabetes and other diseases; however, its effects have been mainly studied using fecal samples, which offer limited information about the intestinal site-specific effects of this drug. Our study aimed to characterize the spatial variation of the gut microbiome in response to metformin treatment by using a high-fat diet-induced type 2 diabetes mouse model of both sexes. Four intestinal parts, each at the luminal and mucosal layer level, were analyzed in this study by performing 16S rRNA sequencing covering six variable regions (V1-V6) of the gene and thus allowing to obtain in-depth information about the microbiome composition. We identified significant differences in gut microbiome diversity in each of the intestinal parts regarding the alpha and beta diversities. Metformin treatment altered the abundance of different genera in all studied intestinal sites, with the most pronounced effect in the small intestine, where Lactococcus increased remarkably. The abundance of Lactobacillus was substantially lower in male mice compared to female mice in all locations, in addition to an enrichment of opportunistic pathogens. Diet type and intestinal layer had significant effects on microbiome composition at each of the sites studied. We observed a different effect of metformin treatment on the analyzed subsets, indicating the multiple dimensions of metformin’s effect on the gut microbiome.

IntroductionResearch 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. MethodsThe 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. ResultsWe 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. DiscussionOur 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.

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.