Project description:Mammals display wide range of variation in their lifespan. Investigating the molecular networks that distinguish long- from short-lived species has proven useful to identify determinants of longevity. Here, we compared the liver of long-lived naked mole-rats (NMRs) and the phylogenetically closely related, shorter-lived, guinea pigs using an integrated omic approach. We found that NMRs livers display a unique expression pattern of mitochondrial proteins that result in distinct metabolic features of their mitochondria. For instance, we observed a generally reduced respiration rate associated with lower protein levels of respiratory chain components, particularly complex I, and increased capacity to utilize fatty acids. Interestingly, we show that the same molecular networks are affected during aging in both NMR and humans, supporting a direct link to the extraordinary longevity of both species. Finally, we identified a novel longevity pathway and validated it experimentally in the nematode C. elegans.
Project description:The naked mole-rat (NMR), Heterocephalus glaber, is a mouse-sized subterranean rodent native to East Africa. Research on NMRs is intensifying in an effort to gain leverage from their unusual physiology, long-life span and cancer resistance for the development of new theraputics. Few studies have attempted to explain the reasons behind the NMR’s cancer resistance, but most prominently Tian et al. reported that NMR cells produce high-molecular weight hyaluronan as a potential cause for the NMR’s cancer resistance. Tian et al. have shown that NMR cells are resistant to transformation by SV40 Large T Antigen (SV40LT) and oncogenic HRAS (HRASG12V), a combination of oncogenes sufficient to transform mouse and rat fibroblasts. We have developed a number of lentiviral vectors to deliver both these oncogenes and generated 106 different cell lines from five different tissues and eleven different NMRs, and report here that contrary to Tian et al.’s observation, NMR cells are susceptible to oncogenic transformation by SV40LT and HRASG12V. Our data thus point to a non-cell autonomous mechanism underlying the remarkable cancer resistance of NMRs. Identifying these non-cell autonomous mechanisms could have significant implications on our understanding of human cancer development.
Project description:The detachment of epithelial cells, but not cancer cells, causes anoikis due to reduced energy production. Invasive tumor cells generate three splice variants of the metastasis gene osteopontin. The cancer-specific form osteopontin-c supports anchorage-independence through inducing oxidoreductases and upregulating intermediates/enzymes in the hexose monophosphate shunt, glutathione cycle, glycolysis, glycerol phosphate shuttle, and mitochondrial respiratory chain. Osteopontin-c signaling upregulates glutathione (consistent with the induction of the enzyme GPX-4), glutamine and glutamate (which can feed into the tricarboxylic acid cycle). Consecutively, the cellular ATP levels are elevated. The elevated creatine may be synthesized from serine via glycine and also supports the energy metabolism by increasing the formation of ATP. Metabolic probing with N-acetyl-L-cysteine, L-glutamate, or glycerol identified differentially regulated pathway components, with mitochondrial activity being redox dependent and the creatine pathway depending on glutamine. The effects are consistent with a stimulation of the energy metabolism that supports anti-anoikis. Our findings imply a synergism in cancer cells between osteopontin-a, which increases the cellular glucose levels, and osteopontin-c, which utilizes this glucose to generate energy. mRNA profiles of MCF-7 cells transfected with osteopontin-a, osteopontin-c and vector control were generated by RNA-Seq, in triplicate, by Illumina HiSeq.
Project description:Motility in the Archaea domain is facilitated by a unique motility structure termed the archaellum. N-glycosylation of the major structural proteins (archaellins) is important for their subsequent incorporation into the archaellum filament. Here, we report the structure of the archaellin glycan from Methanothermococcus thermolithotrophicus, a methanogen which grows optimally at 65°C. Four archaellin genes (flaB1-4) have previously been identified. In gel digestion and LC-MS analysis revealed the identity of the upper band as FlaB1 and the lower band as FlaB3. Examination of the protein sequences for the four archaellins indicated multiple possible N-linked glycosylation sites in each. We observed using mass spectrometry that Mtc. thermolithotrophicus archaellins is posttranslationally modified at multiple sites with an N-linked branched oligosaccharide composed of 7 sugars (1414 Da). NMR analysis of the purified glycan determined the structure to be α-D-glycero-D-manno-Hep3OMe6OMe-(1-3)-[α-GalNAcA3OMe-(1-2)-]-β-Man-(1-4)-[-GalA3OMe4OAc6CMe-(1-4)--GalA-(1-2)-]-α-GalAN-(1-3)-β-GalNAc-Asn. A detailed investigation by HILIC-MS discovered the presence of several, less abundant glycan variants, related to but distinct from the main heptameric glycan. In addition, we confirmed that the S-layer protein is modified with the same heptameric glycan suggesting a common N-glycosylation pathway.
Project description:Transcriptional enhancers act as docking stations for combinations of transcription factors and thereby regulate spatiotemporal activation of their target genes. A single enhancer, of a few hundred base pairs in length, can autonomously and independently of its location and orientation drive cell-type specific expression of a gene or transgene. It has been a long-standing goal in the field to decode the regulatory logic of an enhancer and to understand the details of how spatiotemporal gene expression is encoded in an enhancer sequence. Recently, deep learning models have yielded unprecedented insight into the enhancer code, and well-trained models are reaching a level of understanding that may be close to complete. As a consequence, we hypothesized that deep learning models can be used to guide the directed design of synthetic, cell type specific enhancers, and that this process would allow for a detailed tracing of all enhancer features at nucleotide-level resolution. Here we implemented and compared three different design strategies, each built on a deep learning model: (1) directed sequence evolution; (2) directed iterative motif implanting; and (3) generative design. We evaluated the function of fully synthetic enhancers to specifically target Kenyon cells or glial cells in the fruit fly brain using transgenic animals. We then exploited this concept further by creating “dual-code” enhancers that target two cell types, and minimal enhancers smaller than 50 base pairs that are fully functional. By examining the trajectories followed during state space searches towards functional enhancers, we could accurately define the enhancer code as the optimal strength, combination, and relative distance of TF activator motifs, and the absence of TF repressor motifs. Finally, we applied the same three strategies to successfully design human enhancers, finding highly similar design principles as in Drosophila. In conclusion, enhancer design guided by deep learning leads to better understanding of how enhancers work and shows that their code can be exploited to manipulate cell states.
Project description:Analysis of whole mouse muscle and inguinal lymph node gene expression signature induced after 24h by in-vivo intramuscularly administration of R848, SMIP-7.7, SMIP-7.8 and 4%DMSO controls. Analysis of whole mouse muscle and inguinal lymph node gene expression signature induced after 6h by in-vivo intramuscularly administration of R848 and SMIP-7.2 in 1% DMSO, and SMIP-7.10 and SMIP-7.10+alum in Buffer.