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.

Evaluating the Efficacy of Type 2 Diabetes Polygenic Risk Scores in an Independent European Population.

Numerous type 2 diabetes (T2D) polygenic risk scores (PGSs) have been developed to predict individuals' predisposition to the disease. An independent assessment and verification of the best-performing PGS are warranted to allow for a rapid application of developed models. To date, only 3% of T2D PGSs have been evaluated. In this study, we assessed all (n = 102) presently published T2D PGSs in an independent cohort of 3718 individuals, which has not been included in the construction or fine-tuning of any T2D PGS so far. We further chose the best-performing PGS, assessed its performance across major population principal component analysis (PCA) clusters, and compared it with newly developed population-specific T2D PGS. Our findings revealed that 88% of the published PGSs were significantly associated with T2D; however, their performance was lower than what had been previously reported. We found a positive association of PGS improvement over the years (p-value = 8.01 × 10-4 with PGS002771 currently showing the best discriminatory power (area under the receiver operating characteristic (AUROC) = 0.669) and PGS003443 exhibiting the strongest association PGS003443 (odds ratio (OR) = 1.899). Further investigation revealed no difference in PGS performance across major population PCA clusters and when compared with newly developed population-specific PGS. Our findings revealed a positive trend in T2D PGS performance, consistently identifying high-T2D-risk individuals in an independent European population.

Gut Microbiome Composition and Dynamics in Hospitalized COVID-19 Patients and Patients with Post-Acute COVID-19 Syndrome.

The gut microbiome plays a pivotal role in the modulation of host responses during viral infections, and recent studies have underscored its significance in the context of coronavirus disease 2019 (COVID-19). We aimed to investigate the dynamics and compositional changes in the gut microbiome of COVID-19 patients, addressing both the acute phase and the recovery process, with a particular focus on the emergence of post-COVID-19 conditions. Involving 146 COVID-19 patients and 110 healthy controls, this study employed a shotgun metagenomics approach for cross-sectional and longitudinal analyses with one- and three-month follow-ups. We observed a decline in taxonomic diversity among hospitalized COVID-19 patients compared to healthy controls, while a subsequent increase in alpha diversity was shown during the recovery process. A notable contribution of Enterococcus faecium was identified in the acute phase of the infection, accompanied by an increasing abundance of butyrate-producing bacteria (e.g., Roseburia, Lachnospiraceae_unclassified) during the recovery period. We highlighted a protective role of the Prevotella genus in the long-term recovery process and suggested a potential significance of population-specificity in the early gut microbiome markers of post-acute COVID-19 syndrome. Our study represents distinctive gut microbiome signatures in COVID-19, with potential diagnostic and prognostic implications, pinpointing potential modulators of the disease progression.

Ryegrass mottle virus complete genome determination and development of infectious cDNA by combining two methods- 3' RACE and RNA-Seq.

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.

Variations in the Relative Abundance of Gut Bacteria Correlate with Lipid Profiles in Healthy Adults.

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.

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.

Whole-Genome Sequencing of 502 Individuals from Latvia: The First Step towards a Population-Specific Reference of Genetic Variation.

Despite rapid improvements in the accessibility of whole-genome sequencing (WGS), understanding the extent of human genetic variation is limited by the scarce availability of genome sequences from underrepresented populations. Developing the population-scale reference database of Latvian genetic variation may fill the gap in European genomes and improve human genomics research. In this study, we analysed a high-coverage WGS dataset comprising 502 individuals selected from the Genome Database of the Latvian Population. An assessment of variant type, location in the genome, function, medical relevance, and novelty was performed, and a population-specific imputation reference panel (IRP) was developed. We identified more than 18.2 million variants in total, of which 3.3% so far are not represented in gnomAD and dbSNP databases. Moreover, we observed a notable though distinct clustering of the Latvian cohort within the European subpopulations. Finally, our findings demonstrate the improved performance of imputation of variants using the Latvian population-specific reference panel in the Latvian population compared to established IRPs. In summary, our study provides the first WGS data for a regional reference genome that will serve as a resource for the development of precision medicine and complement the global genome dataset, improving the understanding of human genetic variation.

Abundance and prevalence of ESBL coding genes in patients undergoing first line eradication therapy for Helicobacter pylori.

The spread of extended-spectrum beta-lactamases (ESBLs) in nosocomial and community-acquired enterobacteria is an important challenge for clinicians due to the limited therapeutic options for infections that are caused by these organisms. Here, we developed a panel of ESBL coding genes, evaluated the abundance and prevalence of ESBL encoding genes in patients undergoing H. pylori eradication therapy, and summarized the effects of eradication therapy on functional profiles of the gut microbiome. To assess the repertoire of known beta lactamase (BL) genes, they were divided into clusters according to their evolutionary relation. Primers were designed for amplification of cluster marker regions, and the efficiency of this amplification panel was assessed in 120 fecal samples acquired from 60 patients undergoing H. pylori eradication therapy. In addition, fecal samples from an additional 30 patients were used to validate the detection efficiency of the developed ESBL panel. The presence for majority of targeted clusters was confirmed by NGS of amplification products. Metagenomic sequencing revealed that the abundance of ESBL genes within the pool of microorganisms was very low. The global relative abundances of the ESBL-coding gene clusters did not differ significantly among treatment states. However, at the level of each cluster, classical ESBL producers such as Klebsiella sp. for blaOXY (p = 0.0076), Acinetobacter sp. for blaADC (p = 0.02297) and others, differed significantly with a tendency to decrease compared to the pre- and post-eradication states. Only 13 clusters were common across all three datasets, suggesting a patient-specific distribution profile of ESBL-coding genes. The number of AMR genes detected in the post-eradication state was higher than that in the pre-eradication state, which could be attributed, at least in part, to the therapy. This study demonstrated that the ESBL screening panel was effective in targeting ESBL-coding gene clusters from bacterial DNA and that minor differences exist in the abundance and prevalence of ESBL-coding gene levels before and after eradication therapy.

Metatranscriptome analysis of blood in healthy individuals and irritable bowel syndrome patients.

Introduction. Although the presence of micro-organisms in the blood of healthy humans is a relatively new concept, there is a growing amount of evidence that blood might have its own microbiome.Gap Statement. Previous research has targeted the taxonomic composition of the blood microbiome using DNA-based sequencing methods, while little information is known about the presence of microbial transcripts obtained from the blood and their relation to conditions connected with increased gut permeability.Aim. To detect potentially alive and active micro-organisms and investigate differences in taxonomic composition between healthy people and patients with irritable bowel syndrome (IBS), we used the metatranscriptomics approach.Methodology. We collected blood samples from 23 IBS patients and 26 volunteers from the general population, and performed RNAseq on the isolated RNA. Reads corresponding to microbial genomes were identified with Kraken 2's standard plus protozoa and fungi database, and re-estimated at genus level with Bracken 2.7. We looked for trends in the taxonomic composition, making a comparison between the IBS and control groups, accounting for other different factors.Results. The dominant genera in the blood microbiome were found to be Cutibacterium, Bradyrhizobium, Escherichia, Pseudomonas, Micrococcus, Delftia, Mediterraneibacter, Staphylococcus, Stutzerimonas and Ralstonia. Some of these are typical environmental bacteria and could partially represent contamination. However, analysis of sequences from the negative controls suggested that some genera which are characteristic of the gut microbiome (Mediterraneibacter, Blautia, Collinsella, Klebsiella, Coprococcus, Dysosmobacter, Anaerostipes, Faecalibacterium, Dorea, Simiaoa, Bifidobacterium, Alistipes, Prevotella, Ruminococcus) are less likely to be a result of contamination. Differential analysis of microbes between groups showed that some taxa associated with the gut microbiome (Blautia, Faecalibacterium, Dorea, Bifidobacterium, Clostridium, Christensenella) are more prevalent in IBS patients compared to the general population. No significant correlations with any other factors were identified.Conclusion. Our findings support the existence of the blood microbiome and suggest the gut and possibly the oral microbiome as its origin, while the skin microbiome is a possible but less certain source. The blood microbiome is likely influenced by states of increased gut permeability such as IBS.

Spatial variation of the gut microbiome in response to long-term metformin treatment in high-fat diet-induced type 2 diabetes mouse model of both sexes.

Antidiabetic 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.

OverFlap PCR: A reliable approach for generating plasmid DNA libraries containing random sequences without a template bias.

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.

Amino Acid Metabolism is Significantly Altered at the Time of Admission in Hospital for Severe COVID-19 Patients: Findings from Longitudinal Targeted Metabolomics Analysis.

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.

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.

First Report on the Latvian SARS-CoV-2 Isolate Genetic Diversity.

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.

Baseline gut microbiome composition predicts metformin therapy short-term efficacy in newly diagnosed type 2 diabetes patients.

BACKGROUND: The 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. METHODS: In 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 <12.6 mmol/mol (1%). RESULTS: Metformin 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. CONCLUSIONS: Metformin 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.

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.

Case report: recurrent pituitary adenoma has increased load of somatic variants.

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.

Medication for Acromegaly Reduces Expression of MUC16, MACC1 and GRHL2 in Pituitary Neuroendocrine Tumour Tissue.

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 strongly affects transcriptome of peripheral blood cells in healthy individuals.

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.