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Results for “Binding Sites/genetics”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Crosslinking transcription factors to their recognition sequences with PtII complexes

We have prepared phosphorothioate-containing cyclic oligodeoxynucleotides that fold into 'dumbbells' containing CRE and TRE sequences, the binding sequences for the CREB and JUN proteins, respectively. Six phosphorothioate residues were introduced into each of the recognition sequences. K2PtCl4 crosslinks CRE to CREB and TRE to JUN. The extent of crosslinking is about eight times greater than that observed with standard oligodeoxynucleotides and amounts to 30-50% of the efficiency of non-covalent association as estimated by gel-shift assays. Crosslinking is reversed by incubation with NaCN. The crosslinking reaction is specific--a dumbbell oligonucleotide with six phosphorothioate groups introduced into the Sp1 recognition sequence could not be crosslinked efficiently to CREB or JUN proteins with K2PtCl4. The binding of TRE to CREB is not strong enough for effective detection by gel-shift assays, but the TRE-CREB complex is crosslinked efficiently by K2PtCl4 and can then readily be detected.

NASA Discipline Exobiology↗
NASA NTRS2002

Distinct requirements for TrkB and TrkC signaling in target innervation by sensory neurons

Signaling by brain-derived neurotrophic factor (BDNF) via the TrkB receptor, or by neurotrophin-3 (NT3) through the TrkC receptor support distinct populations of sensory neurons. The intracellular signaling pathways activated by Trk (tyrosine kinase) receptors, which in vivo promote neuronal survival and target innervation, are not well understood. Using mice with TrkB or TrkC receptors lacking the docking site for Shc adaptors (trkB(shc/shc) and trkC(shc/shc) mice), we show that TrkB and TrkC promote survival of sensory neurons mainly through Shc site-independent pathways, suggesting that these receptors use similar pathways to prevent apoptosis. In contrast, the regulation of target innervation appears different: in trkB(shc/shc) mice neurons lose target innervation, whereas in trkC(shc/shc) mice the surviving TrkC-dependent neurons maintain target innervation and function. Biochemical analysis indicates that phosphorylation at the Shc site positively regulates autophosphorylation of TrkB, but not of TrkC. Our findings show that although TrkB and TrkC signals mediating survival are largely similar, TrkB and TrkC signals required for maintenance of target innervation in vivo are regulated by distinct mechanisms.

Non-NASA Center↗
NASA NTRS2001

Rapid evolution of cis-regulatory sequences via local point mutations

Although the evolution of protein-coding sequences within genomes is well understood, the same cannot be said of the cis-regulatory regions that control transcription. Yet, changes in gene expression are likely to constitute an important component of phenotypic evolution. We simulated the evolution of new transcription factor binding sites via local point mutations. The results indicate that new binding sites appear and become fixed within populations on microevolutionary timescales under an assumption of neutral evolution. Even combinations of two new binding sites evolve very quickly. We predict that local point mutations continually generate considerable genetic variation that is capable of altering gene expression.

Non-NASA Center↗
NASA NTRS2004

cWINNOWER algorithm for finding fuzzy dna motifs

The cWINNOWER algorithm detects fuzzy motifs in DNA sequences rich in protein-binding signals. A signal is defined as any short nucleotide pattern having up to d mutations differing from a motif of length l. The algorithm finds such motifs if a clique consisting of a sufficiently large number of mutated copies of the motif (i.e., the signals) is present in the DNA sequence. The cWINNOWER algorithm substantially improves the sensitivity of the winnower method of Pevzner and Sze by imposing a consensus constraint, enabling it to detect much weaker signals. We studied the minimum detectable clique size qc as a function of sequence length N for random sequences. We found that qc increases linearly with N for a fast version of the algorithm based on counting three-member sub-cliques. Imposing consensus constraints reduces qc by a factor of three in this case, which makes the algorithm dramatically more sensitive. Our most sensitive algorithm, which counts four-member sub-cliques, needs a minimum of only 13 signals to detect motifs in a sequence of length N = 12,000 for (l, d) = (15, 4). Copyright Imperial College Press.

Evaluation Studies↗