Olsenkey5563

Despite both originating from endosymbiotic bacteria, one does not typically expect mitochondrial DNA (mtDNA) to show strong sequence identity to plastid DNA (ptDNA). Nevertheless, a recent analysis of Haematococcus lacustris revealed exactly that. A common repeat element has proliferated throughout the mtDNA and ptDNA of this chlamydomonadalean green alga, resulting in the unprecedented situation whereby these two distinct organelle genomes are largely made up of nearly identical sequences. In this short update to the work on H. lacustris, I highlight another chlamydomonadalean species (Stephanosphaera pluvialis) for which matching repeats have spread throughout its organelle genomes (but to a lesser degree than in H. lacustris). What's more, the organelle repeats from S. pluvialis are similar to those from H. lacustris, suggesting that they have a shared origin, and perhaps existed in the mtDNA and ptDNA of the most recent common ancestor of these two species. However, my examination of organelle genomes from other close relatives of H. lacustris and S. pluvialis did not uncover further compelling examples of common organelle repeat elements, meaning that the evolutionary history of these repeats might be more complicated than initially thought.Fungal pathogens are a global threat to human health. For example, fungi from the genus Aspergillus cause a spectrum of diseases collectively known as aspergillosis. Most of the >200,000 life-threatening aspergillosis infections per year worldwide are caused by Aspergillus fumigatus. Recently, molecular typing techniques have revealed that aspergillosis can also be caused by organisms that are phenotypically similar to A. fumigatus but genetically distinct, such as Aspergillus lentulus and Aspergillus fumigatiaffinis. Importantly, some of these so-called cryptic species are thought to exhibit different virulence and drug susceptibility profiles than A. fumigatus, however, our understanding of their biology and pathogenic potential has been stymied by the lack of genome sequences and phenotypic profiling of multiple clinical strains. To fill this gap, we phenotypically characterized the virulence and drug susceptibility of 15 clinical strains of A. fumigatus, A. lentulus, and A. fumigatiaffinis from Spain and sequenced their genomes. We found heterogeneity in drug susceptibility across species and strains. We further found heterogeneity in virulence within each species but no significant differences in the virulence profiles between the three species. Genes known to influence drug susceptibility (cyp51A and fks1) vary in paralog number and sequence among these species and strains and correlate with differences in drug susceptibility. Similarly, genes known to be important for virulence in A. fumigatus showed variability in number of paralogs across strains and across species. Characterization of the genomic similarities and differences of clinical strains of A. lentulus, A. fumigatiaffinis, and A. APG2449 fumigatus that vary in disease-relevant traits will advance our understanding of the variance in pathogenicity between Aspergillus species and strains that are collectively responsible for the vast majority of aspergillosis infections in humans.Extrathyroidal extension (ETE) affects papillary thyroid cancer (PTC) prognosis. The objective of this study was to identify biomarkers for ETE and explore the mechanisms controlling its development in PTC. We performed a comprehensive bioinformatics analysis using several datasets. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) on 58 paired PTC samples from The Cancer Genome Atlas (TCGA) were used to detect ETE-related mRNA and long noncoding (lnc) RNA modules and construct an lncRNA/mRNA network. An independent TCGA dataset containing 438 samples was utilized to validate and characterize the WGCNA results. Functional annotation was used to identify the biological functions and related pathways of ETE modules. Two independent RNA sequencing datasets were combined to crossvalidate relationships between lncRNAs and mRNAs by Pearson correlation analysis. Transcription factors (TFs) for affected genes were predicted using the binding motif data from Ensembl Biomart to ccted with hub genes showed significant survival differences between low- and high-risk groups (p = 0.00025) and performed good prediction for PTC prognosis(AUC = 0.794; C-index = 0.895). The identification of 33 biomarkers and TF/lncRNA/mRNA regulatory network would provide new insights into the molecular mechanisms of ETE besides the prognosis model may have important clinical implications in the improvement of PTC risk stratification, therapeutic decision-making, and prognosis prediction.Mammals contain over 200 different cell types, yet nearly all have the same genomic DNA sequence. It is a key question in biology how the genetic instructions in DNA are selectively interpreted by cells to specify various transcriptional programs and therefore cellular identity. The structural and functional organization of chromatin governs the transcriptional state of individual genes. To understand how genomic loci adopt different levels of gene expression, it is critical to characterize all local chromatin factors as well as long-range interactions in the 3D nuclear compartment. Much of our current knowledge regarding protein interactions in a chromatin context is based on affinity purification of chromatin components coupled to mass spectrometry (AP-MS). AP-MS has been invaluable to map strong protein-protein interactions in the nucleus. However, the interaction is detected after cell lysis and biochemical enrichment, allowing for loss or gain of false positive or negative interaction partners. Recently, proximity-dependent labeling methods have emerged as powerful tools for studying chromatin in its native context. These methods take advantage of engineered enzymes that are fused to a chromatin factor of interest and can directly label all factors in proximity. Subsequent pull-down assays followed by mass spectrometry or sequencing approaches provide a comprehensive snapshot of the proximal chromatin interactome. By combining this method with dCas9, this approach can also be extended to study chromatin at specific genomic loci. Here, we review and compare current proximity-labeling approaches available for studying chromatin, with a particular focus on new emerging technologies that can provide important insights into the transcriptional and chromatin interaction networks essential for cellular identity.