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.

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.

Background and Objectives: the upper respiratory tract harbors the highest bacterial density in the whole respiratory system. Adenoids, which are located in the nasopharynx, are a major site of bacterial colonies in the upper airways. Our goal was to use culture-independent molecular techniques to identify the breadth of bacterial diversity in the adenoid vegetations of children suffering from chronic rhinosinusitis and obstructive sleep apnea. Materials and methods: in total, 21 adenoid samples were investigated using amplification and sequencing of the V3-V4 hypervariable region of the bacterial 16S rRNA gene. Results: among the most common bacterial species found were Veillonella atypica, Fusobactrium nucelatum, Shaalia odontolytica, and Moraxella catarrhalis. Veillonella atypica and Fusbacteriumnucelatum dominated the microbiome in all 21 samples, attributing to more than 60% of all detected genetic material. Conclusions: since both Veillonella atypica and Fusobacterium nucleatum are, predominantly, oral cavity and dental microorganisms, our findings may suggest oral microbiome migration deeper into the oropharynx and nasopharynx where these bacteria colonize adenoid vegetations.

Pituitary neuroendocrine tumours (PitNETs) are neoplasms of the pituitary that overproduce hormones or cause unspecific symptoms due to mass effect. Growth hormone overproducing GH-producing PitNETs cause acromegaly leading to connective tissue, metabolic or oncologic disorders. The medical treatment of acromegaly is somatostatin analogues (SSA) in specific cases combined with dopamine agonists (DA), but almost half of patients display partial or full SSA resistance and potential causes of this are unknown. In this study we investigated transcriptomic landscape of GH-producing PitNETs on several levels and functional models—tumour tissue of patients with and without SSA preoperative treatment, tumour derived pituispheres and GH3 cell line incubated with SSA to study effect of medication on gene expression. MGI sequencing platform was used to sequence total RNA from PitNET tissue, pituispheres, mesenchymal stromal stem-like cells (MSC), and GH3 cell cultures, and data were analysed with Salmon—DeSeq2 pipeline. We observed that the GH-producing PitNETs have distinct changes in growth hormone related pathways related to its functional status alongside inner cell signalling, ion transport, cell adhesion and extracellular matrix characteristic patterns. In pituispheres model, treatment regimens (octreotide and cabergoline) affect specific cell proliferation (MKI67) and core functionality pathways (RYR2, COL8A2, HLA-G, ARFGAP1, TGFBR2). In GH3 cells we observed that medication did not have transcriptomic effects similar to preoperative treatment in PitNET tissue or pituisphere model. This study highlights the importance of correct model system selection for cell transcriptomic profiling and data interpretation that could be achieved in future by incorporating NGS methods and detailed cell omics profiling in PitNET model research.

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