Most accurate mutations in SARS-CoV-2 genomes identified in Uzbek patients show novel amino acid changes.

Purpose: The rapid changes in the coronavirus genomes created new strains after the first variation was found in Wuhan in 2019. SARS-CoV-2 genotypes should periodically undergo whole genome sequencing to control it because it has been extremely helpful in combating the virus. Many diagnoses, treatments, and vaccinations have been developed against it based on genome sequencing. With its practical implications, this study aimed to determine changes in the delta variant of SARS-CoV-2 widespread in Uzbekistan during the pandemic by genome sequencing, thereby providing crucial insights for developing effective control strategies that can be directly applied in the field. Design: We meticulously generated 17 high-quality whole-genome sequence data from 48 SARS-CoV-2 genotypes of COVID-19 patients who tested positive by PCR in Tashkent, Uzbekistan. Our rigorous approach, which includes stringent quality control measures and multiple rounds of verification, ensures the accuracy and reliability of our findings. Methods: Our study employed a unique combination of genome sequencing and bioinformatics web tools to analyze amino acid (AA) changes in the virus genomes. This approach allowed us to understand the genetic changes in the delta variant of SARS-CoV-2 widespread in Uzbekistan during the pandemic. Results: Our study revealed significant nucleotide polymorphisms, including non-synonymous (missense) and synonymous mutations in the coding regions of the sequenced sample genomes. These findings, categorized by phylogenetic analysis into the G clade (or GK sub-clade), contribute to our understanding of the delta variant of SARS-CoV-2 widespread in Uzbekistan during the pandemic. A total of 134 mutations were identified, consisting of 65 shared and 69 unique mutations. These nucleotide changes, including one frameshift mutation, one conservative and disruptive insertion-deletion, four upstream region mutations, four downstream region mutations, 39 synonymous mutations, and 84 missense mutations, are crucial in the ongoing battle against the virus. Conclusion: The comprehensive whole-genome sequencing data presented in this study aids in tracing the origins and sources of circulating SARS-CoV-2 variants and analyzing emerging variations within Uzbekistan and globally. The genome sequencing of SARS-CoV-2 from samples collected in Uzbekistan in late 2021, during the peak of the pandemic's second wave nationwide, is detailed here. Following acquiring these sequences, research efforts have focused on developing DNA and plant-based edible vaccines utilizing prevalent SARS-CoV-2 strains in Uzbekistan, which are currently undergoing clinical trials.

Rel Family Transcription Factor NFAT5 Upregulates COX2 via HIF-1α Activity in Ishikawa and HEC1a Cells.

Nuclear factor of activated T cells 5 (NFAT5) and cyclooxygenase 2 (COX2; PTGS2) both participate in diverse pathologies including cancer progression. However, the biological role of the NFAT5-COX2 signaling pathway in human endometrial cancer has remained elusive. The present study explored whether NFAT5 is expressed in endometrial tumors and if NFAT5 participates in cancer progression. To gain insights into the underlying mechanisms, NFAT5 protein abundance in endometrial cancer tissue was visualized by immunohistochemistry and endometrial cancer cells (Ishikawa and HEC1a) were transfected with NFAT5 or with an empty plasmid. As a result, NFAT5 expression is more abundant in high-grade than in low-grade endometrial cancer tissue. RNA sequencing analysis of NFAT5 overexpression in Ishikawa cells upregulated 37 genes and downregulated 20 genes. Genes affected included cyclooxygenase 2 and hypoxia inducible factor 1α (HIF1A). NFAT5 transfection and/or treatment with HIF-1α stabilizer exerted a strong stimulating effect on HIF-1α promoter activity as well as COX2 expression level and prostaglandin E2 receptor (PGE2) levels. Our findings suggest that activation of NFAT5-HIF-1α-COX2 axis could promote endometrial cancer progression.

Glioblastoma-instructed microglia transition to heterogeneous phenotypic states with phagocytic and dendritic cell-like features in patient tumors and patient-derived orthotopic xenografts.

BACKGROUND: A major contributing factor to glioblastoma (GBM) development and progression is its ability to evade the immune system by creating an immune-suppressive environment, where GBM-associated myeloid cells, including resident microglia and peripheral monocyte-derived macrophages, play critical pro-tumoral roles. However, it is unclear whether recruited myeloid cells are phenotypically and functionally identical in GBM patients and whether this heterogeneity is recapitulated in patient-derived orthotopic xenografts (PDOXs). A thorough understanding of the GBM ecosystem and its recapitulation in preclinical models is currently missing, leading to inaccurate results and failures of clinical trials. METHODS: Here, we report systematic characterization of the tumor microenvironment (TME) in GBM PDOXs and patient tumors at the single-cell and spatial levels. We applied single-cell RNA sequencing, spatial transcriptomics, multicolor flow cytometry, immunohistochemistry, and functional studies to examine the heterogeneous TME instructed by GBM cells. GBM PDOXs representing different tumor phenotypes were compared to glioma mouse GL261 syngeneic model and patient tumors. RESULTS: We show that GBM tumor cells reciprocally interact with host cells to create a GBM patient-specific TME in PDOXs. We detected the most prominent transcriptomic adaptations in myeloid cells, with brain-resident microglia representing the main population in the cellular tumor, while peripheral-derived myeloid cells infiltrated the brain at sites of blood-brain barrier disruption. More specifically, we show that GBM-educated microglia undergo transition to diverse phenotypic states across distinct GBM landscapes and tumor niches. GBM-educated microglia subsets display phagocytic and dendritic cell-like gene expression programs. Additionally, we found novel microglial states expressing cell cycle programs, astrocytic or endothelial markers. Lastly, we show that temozolomide treatment leads to transcriptomic plasticity and altered crosstalk between GBM tumor cells and adjacent TME components. CONCLUSIONS: Our data provide novel insights into the phenotypic adaptation of the heterogeneous TME instructed by GBM tumors. We show the key role of microglial phenotypic states in supporting GBM tumor growth and response to treatment. Our data place PDOXs as relevant models to assess the functionality of the TME and changes in the GBM ecosystem upon treatment.

Glioblastoma-instructed microglia transition to heterogeneous phenotypic states with phagocytic and dendritic cell-like features in patient tumors and patient-derived orthotopic xenografts.

Background: A major contributing factor to glioblastoma (GBM) development and progression is its ability to evade the immune system by creating an immune-suppressive environment, where GBM-associated myeloid cells, including resident microglia and peripheral monocyte-derived macrophages, play critical pro-tumoral roles. However, it is unclear whether recruited myeloid cells are phenotypically and functionally identical in GBM patients and whether this heterogeneity is recapitulated in patient-derived orthotopic xenografts (PDOXs). A thorough understanding of the GBM ecosystem and its recapitulation in preclinical models is currently missing, leading to inaccurate results and failures of clinical trials. Methods: Here, we report systematic characterization of the tumor microenvironment (TME) in GBM PDOXs and patient tumors at the single-cell and spatial levels. We applied single-cell RNA-sequencing, spatial transcriptomics, multicolor flow cytometry, immunohistochemistry and functional studies to examine the heterogeneous TME instructed by GBM cells. GBM PDOXs representing different tumor phenotypes were compared to glioma mouse GL261 syngeneic model and patient tumors. Results: We show that GBM tumor cells reciprocally interact with host cells to create a GBM patient-specific TME in PDOXs. We detected the most prominent transcriptomic adaptations in myeloid cells, with brain-resident microglia representing the main population in the cellular tumor, while peripheral-derived myeloid cells infiltrated the brain at sites of blood-brain barrier disruption. More specifically, we show that GBM-educated microglia undergo transition to diverse phenotypic states across distinct GBM landscapes and tumor niches. GBM-educated microglia subsets display phagocytic and dendritic cell-like gene expression programs. Additionally, we found novel microglial states expressing cell cycle programs, astrocytic or endothelial markers. Lastly, we show that temozolomide treatment leads to transcriptomic plasticity and altered crosstalk between GBM tumor cells and adjacent TME components. Conclusions: Our data provide novel insights into the phenotypic adaptation of the heterogeneous TME instructed by GBM tumors. We show the key role of microglial phenotypic states in supporting GBM tumor growth and response to treatment. Our data place PDOXs as relevant models to assess the functionality of the TME and changes in the GBM ecosystem upon treatment.

The 3D genomic landscape of differential response to EGFR/HER2 inhibition in endocrine-resistant breast cancer cells.

BACKGROUND: Recent studies suggested that crosstalk between ERα and EGFR/HER2 pathways plays a critical role in mediating endocrine therapy resistance. Several inhibitors targeting EGFR/HER2 signaling, including FDA-approved lapatinib and gefitinib as well as a novel dual tyrosine kinase inhibitor (TKI) sapitinib, showed greater therapeutic efficacies. However, how 3D chromatin landscape responds to the inhibition of EGFR/HER2 pathway remains to be elucidated. METHODS: In this study, we conducted in situ Hi-C and RNA-seq in two ERα+ breast cancer cell systems, 1) parental MCF7 cells and its associated tamoxifen-resistant MCF7TR cells; and 2) parental T47D cells and its associated tamoxifen-resistant T47DTR cells, before and after the treatment of sapitinib. RESULTS: We identified differential responses in topologically associated domains (TADs), looping genes and expressed genes. Interestingly, we found that many differential TADs and looping genes are reversible after sapitinib treatment, indicating that EGFR/HER2 signaling may play a role in reshaping and rewiring the high order genome organization. We further examined and recapitulated the reversible looping genes in 3D spheroids of breast cancer cells, demonstrating that 3D cell culture spheroid of breast cancer cells could be a potential preclinical breast cancer model for studying 3D chromatin regulation. CONCLUSIONS: Our study has provided significant insights into our understanding of 3D genomic landscape changes in response to EGFR/HER2 Inhibition in endocrine-resistant breast cancer cells. Our data provides a rich resource for further evaluating chromatin structural responses to EGFR/HER2 targeted therapies in endocrine-resistant breast cancer cells. Our analyses suggest that these alterations of chromatin structures and transcriptional programs may provide new avenues for intervention or designing of patient selection for targeted endocrine treatment.

Reciprocal fine-tuning of progesterone and prolactin-regulated gene expression in breast cancer cells.

Progesterone and prolactin are two key hormones involved in development and remodeling of the mammary gland. As such, both hormones have been linked to breast cancer. Despite the overlap between biological processes ascribed to these two hormones, little is known about how co-expression of both hormones affects their individual actions. Progesterone and prolactin exert many of their effects on the mammary gland through activation of gene expression, either directly (progesterone, binding to the progesterone receptor [PR]) or indirectly (multiple transcription factors being activated downstream of prolactin, most notably STAT5). Using RNA-seq in T47D breast cancer cells, we characterized the gene expression programs regulated by progestin and prolactin, either alone or in combination. We found significant crosstalk and fine-tuning between the transcriptional programs executed by each hormone independently and in combination. We divided and characterized the transcriptional programs into four broad categories. All crosstalk/fine-tuning shown to be modulated by progesterone was dependent upon the expression of PR. Moreover, PR was recruited to enhancer regions of all regulated genes. Interestingly, despite the canonical role for STAT5 in transducing prolactin-signaling in the normal and lactating mammary gland, very few of the prolactin-regulated transcriptional programs fine-tuned by progesterone in this breast cancer cell line model system were in fact dependent upon STAT5. Cumulatively, these data suggest that the interplay of progesterone and prolactin in breast cancer impacts gene expression in a more complex and nuanced manner than previously thought, and likely through different transcriptional regulators than those observed in the normal mammary gland. Studying gene regulation when both hormones are present is most clinically relevant, particularly in the context of breast cancer.

Mapping circulating serum miRNAs to their immune-related target mRNAs.

PURPOSE: Evidence suggests that circulating serum microRNAs (miRNAs) might preferentially target immune-related mRNAs. If this were the case, we hypothesized that immune-related mRNAs would have more predicted serum miRNA binding sites than other mRNAs and, reciprocally, that serum miRNAs would have more immune-related mRNA targets than non-serum miRNAs. MATERIALS AND METHODS: We developed a consensus target predictor using the random forest framework and calculated the number of predicted miRNA-mRNA interactions in various subsets of miRNAs (serum, non-serum) and mRNAs (immune related, nonimmune related). RESULTS: Immune-related mRNAs were predicted to be targeted by serum miRNA more than other mRNAs. Moreover, serum miRNAs were predicted to target many more immune-related mRNA targets than non-serum miRNAs; however, these two biases in immune-related mRNAs and serum miRNAs appear to be completely independent. CONCLUSION: Immune-related mRNAs have more miRNA binding sites in general, not just for serum miRNAs; likewise, serum miRNAs target many more mRNAs than non-serum miRNAs overall, regardless of whether they are immune related or not. Nevertheless, these two independent phenomena result in a significantly larger number of predicted serum miRNA-immune mRNA interactions than would be expected by chance.