Histone acetyltransferase 1 (HAT1) acetylates hypoxia-inducible factor 2 alpha (HIF2A) to execute hypoxia response.

Hypoxic response to low oxygen levels is characteristic of most solid cancers. Hypoxia-inducible factors (HIFs) regulate cellular metabolism, survival, proliferation, and cancer stem cell growth during hypoxia. The genome-wide analysis identified HAT1, a type B histone acetyltransferase, as an upregulated and essential gene in glioblastoma (GBM). GSEA analysis of differentially regulated genes in HAT1 silenced cells identified significant depletion of "hypoxia" gene sets. Hypoxia conditions induced HIF2A, not HIF1A protein levels in glioma cells in a HAT1-dependent manner. HAT1 and HIF2A interacted with each other and occupied the promoter of VEGFA, a bonafide HIF1A/HIF2A target. Acetylation of K512 and K596 residues by HAT1 is essential for HIF2A stabilization under normoxia and hypoxia as HIF2A carrying acetylation mimic mutations at either of these residues (H512Q or K596Q) showed stable expression in HAT1 silenced cells under normoxia and hypoxia conditions. Finally, we demonstrate that the HAT1-HIF2A axis is essential for hypoxia-promoted cancer stem cell maintenance and reprogramming. Thus, our study identifies that the HAT1-dependent acetylation of HIF2A is vital to executing the hypoxia-induced cell survival and cancer stem cell growth, therefore proposing the HAT1-HIF2A axis as a potential therapeutic target.

Optical Microscope Based Universal Parameter for Identifying Layer Number in Two-Dimensional Materials.

Optical contrast is the most common preliminary method to identify layer number of two-dimensional (2D) materials, but it is seldom used as a confirmatory technique. We explain the reason for variation of optical contrast between imaging systems, motivating system-independent measurement of optical contrast as a critical need. We describe a universal method to quantify the layer number using the RGB (red-green-blue) and RAW optical images. For RGB images, the slope of 2D flake (MoS2, WSe2, graphene) intensity vs substrate intensity is extracted from optical images with varying lamp power. The intensity slope identifies layer number and is system independent. For RAW images, intensity slopes and intensity ratios are completely system and intensity independent. Intensity slope (for RGB) and intensity ratio (for RAW) are thus universal parameters for identifying layer number. The RAW format is not present in all imaging systems, but it can confirm layer number using a single optical image, making it a rapid and system-independent universal method. A Fresnel-reflectance-based optical model provides an excellent match with experiments. Furthermore, we have created a MATLAB-based graphical user interface that can identify layer number rapidly. This technique is expected to accelerate the preparation of heterostructures and to fulfill a prolonged need for universal optical contrast method.

Differentiated glioma cell-derived fibromodulin activates integrin-dependent Notch signaling in endothelial cells to promote tumor angiogenesis and growth.

Cancer stem cells (CSCs) alone can initiate and maintain tumors, but the function of non-cancer stem cells (non-CSCs) that form the tumor bulk remains poorly understood. Proteomic analysis showed a higher abundance of the extracellular matrix small leucine-rich proteoglycan fibromodulin (FMOD) in the conditioned medium of differentiated glioma cells (DGCs), the equivalent of glioma non-CSCs, compared to that of glioma stem-like cells (GSCs). DGCs silenced for FMOD fail to cooperate with co-implanted GSCs to promote tumor growth. FMOD downregulation neither affects GSC growth and differentiation nor DGC growth and reprogramming in vitro. DGC-secreted FMOD promotes angiogenesis by activating integrin-dependent Notch signaling in endothelial cells. Furthermore, conditional silencing of FMOD in newly generated DGCs in vivo inhibits the growth of GSC-initiated tumors due to poorly developed vasculature and increases mouse survival. Collectively, these findings demonstrate that DGC-secreted FMOD promotes glioma tumor angiogenesis and growth through paracrine signaling in endothelial cells and identifies a DGC-produced protein as a potential therapeutic target in glioma.

Machine Learning Approaches to Classify Primary and Metastatic Cancers Using Tissue of Origin-Based DNA Methylation Profiles.

Metastatic cancers account for up to 90% of cancer-related deaths. The clear differentiation of metastatic cancers from primary cancers is crucial for cancer type identification and developing targeted treatment for each cancer type. DNA methylation patterns are suggested to be an intriguing target for cancer prediction and are also considered to be an important mediator for the transition to metastatic cancer. In the present study, we used 24 cancer types and 9303 methylome samples downloaded from publicly available data repositories, including The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). We constructed machine learning classifiers to discriminate metastatic, primary, and non-cancerous methylome samples. We applied support vector machines (SVM), Naive Bayes (NB), extreme gradient boosting (XGBoost), and random forest (RF) machine learning models to classify the cancer types based on their tissue of origin. RF outperformed the other classifiers, with an average accuracy of 99%. Moreover, we applied local interpretable model-agnostic explanations (LIME) to explain important methylation biomarkers to classify cancer types.

Genome Sequence of Classical Swine Fever Virus NIVEDI-165, Subtype 1.1, a Field Virus Strain Isolated from the Southern Part of India.

The whole-genome sequence of an Indian field isolate of classical swine fever virus, NIVEDI-165, was found to be subtype 1.1, and it showed 89 to 99% amino acid identity and 84 to 99% nucleotide identity with four and five Indian classical swine fever virus (CSFV) isolates, respectively. To the best of our knowledge, this is the first report on a full-genome sequence of CSFV from South India.

Comparative sequence and structural analysis of Indian orf viruses based on major envelope immuno-dominant protein (F1L), an homologue of pox viral p35/H3 protein.

Orf virus (ORFV), a member of the genus Parapoxvirus in the family Poxviridae, is the cause of orf, a highly contagious zoonotic viral disease that affects mainly sheep and goats. In the present study, the sequence and phylogenetic analysis of Indian ORFV isolates (n = 15) from natural outbreaks in sheep and goats belonging to different geographical regions were analysed on the basis of F1L gene along with homology modelling of F1L protein. Multiple sequence alignments revealed highly conserved C-terminus and heterogeneity of N-terminus region of F1L among all orf viruses studied. Further, a comparative sequence alignment indicated conservation of various motifs such as glycosaminoglycan (GAG), Asp/Glu-any residue-Asp (D/ExD) and a Cx3C chemokine like motif among all poxviruses and unique motifs (proline rich region [PRR] and Lys-Gly-Asp [KGD]), in parapoxviruses including ORFV isolates irrespective of geography and host species. Phylogenetically, two major clusters were noticed which included Indian orf isolates along with foreign isolates. Structurally, ORFV F1L resembled the topology as exhibited by its homologue vaccinia virus H3 protein with mixed ß/α folds and ligand binding specificity in docking models. We noted that despite differences in host cell specificity and pathogencity, poxvirus proteins especially ORFV F1L protein and its homologues presumed to share similarities as they are highly conserved irrespective of species and countries of origin. Further, the study also indicated the possibilities of differentiation of ORFV strains based on N-terminal heterogeneity despite highly conserved C-terminal region with conserved motifs.