Abstract The predominant view of pluripotency regulation proposes a stable ground state with coordinated expression of key transcription factors (TFs) that prohibit differentiation. Another perspective suggests a more complexly regulated state involving competition between multiple lineage-specifying TFs that define pluripotency. These contrasting views were developed from extensive analyses of TFs in pluripotent cells in vitro. An experimentally validated, genome-wide repertoire of the regulatory interactions that control pluripotency within the in vivo cellular contexts is yet to be developed. To address this limitation, we assembled a TF interactome of adult human male germ cell tumors (GCTs) using the Algorithm for the Accurate Reconstruction of Cellular Pathways (ARACNe) to analyze gene expression profiles of 141 tumors comprising pluripotent and differentiated subsets. The network (GCTNet) comprised 1,305 TFs, and its ingenuity pathway analysis identified pluripotency and embryonal development as the top functional pathways. We experimentally validated GCTNet by functional (silencing) and biochemical (ChIP-seq) analysis of the core pluripotency regulatory TFs POU5F1, NANOG, and SOX2 in relation to their targets predicted by ARACNe. To define the extent of the in vivo pluripotency network in this system, we ranked all TFs in the GCTNet according to sharing of ARACNe-predicted targets with those of POU5F1 and NANOG using an odds-ratio analysis method. To validate this network, we silenced the top 10 TFs in the network in H9 embryonic stem cells. Silencing of each led to downregulation of pluripotency and induction of lineage; 7 of the 10 TFs were identified as pluripotency regulators for the first time. Stem Cells 2015;33:367–377
SUMMARY The determination of long non-coding RNA (lncRNA) function is a major challenge in RNA biology with applications to basic, translational, and medical research [1–7]. Our efforts to improve the accuracy of lncRNA-target inference identified lncRNAs that coordinately regulate both the transcriptional and post-transcriptional processing of their targets. Namely, these lncRNAs may regulate the transcription of their target and chaperone the resulting message until its translation, leading to tightly coupled lncRNA and target abundance. Our analysis suggested that hundreds of cancer genes are coordinately and tightly regulated by lncRNAs and that this unexplored regulatory paradigm may propagate the effects of non-coding alterations to effectively dysregulate gene expression programs. As a proof-of-principle we studied the regulation of DICER1 [8, 9]—a cancer gene that controls microRNA biogenesis—by the lncRNA ZFAS1 , showing that ZFAS1 activates DICER1 transcription and blocks its post-transcriptional repression to phenomimic and regulate DICER1 and its target microRNAs.
Influenza viruses are enveloped, negative sense single-stranded RNA viruses covered in a dense layer of glycoproteins. Hemagglutinin (HA) accounts for 80-90% of influenza glycoprotein and plays a role in host cell binding and membrane fusion. While previous studies have characterized structures of receptor-free and receptor-bound HA in vitro, the effect of receptor binding on HA organization and structure on virions remains unknown. Here, we used cryo-electron tomography (cryoET) to visualize influenza virions bound to a sialic acid receptor mimic. Overall, receptor binding did not result in significant changes in viral morphology; however, we observed rearrangements of HA trimer organization and orientation. Compared to the even inter-glycoprotein spacing of unliganded HA trimers, receptor binding promotes HA trimer clustering and formation of a triplet of trimers. Subtomogram averaging and refinement yielded 8-10 Å reconstructions that allowed us to visualize specific contacts between HAs from neighboring trimers and identify molecular features that mediate clustering. Taken together, we present new structural evidence that receptor binding triggers clustering of HA trimers, revealing an additional layer of HA dynamics and plasticity.
Heritable and idiopathic pulmonary arterial hypertension (PAH) are phenotypically identical and associated with mutations in several genes related to transforming growth factor (TGF) beta signaling, including bone morphogenetic protein receptor type 2, activin receptor-like kinase 1, endoglin, and mothers against decapentaplegic 9. Approximately 25% of heritable cases lack identifiable mutations in any of these genes.
<p>Supplemental Table 1: Tumor Mutational burden according to individual KRAS mutation. Supplemental Table 2: Multivariate Analysis of duration of platinum/pemetrexed chemotherapy in patients with KRAS-mutant NSCLC Supplemental Table 3: Multivariate Analysis of duration of immunotherapy in patients with KRAS-mutant NSCLC Supplemental Table 4: Multivariate Analysis of Overall Survival from start of Immunotherapy in patients with KRAS-mutant NSCLC Supplemental Figure 1: Duration of platinum/pemetrexed +/- bevacizumab therapy in subset of patients receiving first line therapy for Stage IV disease by KEAP1/NFE2L2 mutations. Supplemental Figure 2: Duration of immune checkpoint inhibitor for treatment of Stage IV disease by different co-occurring mutations (A, STK11 B, KEAP1 or NFE2L2, C, TP53)</p>
This thesis explores (i) the feasibility of using communication theory models to understand the protein synthesis process from gene to protein, (ii) to find the genetic error control mechanism using error correcting coding theory and (iii) detecting diseases related genetic errors using statistical learning methods on biological databases i.e., EST(Expressed Sequence Tag) and SNP(Single Nucleotide Polymorphism). Several statistical tests are proposed and tested over various biological data. These include the CUSUM (Cumulative Sum) detection for abrupt changes in a stochastic process, SVD(Singular Value Decomposition) for dimensionality reduction and HMM-SVM(Hidden Markov Model-Support Vector Machine). We propose new disease diagnosis systems based on Gene Variation Analysis. The system consist of Pre-Processing, Similarity Search and clustering by EST analysis and disease analysis by SNP classification. Pre-processing reduces the overall noise (vector contamination, low complexity region, repeats) in EST data to improve the efficacy of subsequent analysis. EST clustering and assembly using CAP3 sequence assembly is used to collect overlapping ESTs from the same transcript to reduce redundancy. The assembled EST called Consensus EST sequences are merged based on clone-identification data to obtain the best putative gene representation. Detailed test results on several biological databases are used to draw key conclusions about the proposed mathematical analyses.
