Genomes hold within them the record of the evolution of life on Earth. But genome fusions and horizontal gene transfer (HGT) seem to have obscured sufficiently the gene sequence record such that it is difficult to reconstruct the phylogenetic tree of life. HGT among prokaryotes is not random, however. Some genes (informational genes) are more difficult to transfer than others (operational genes). Furthermore, environmental, metabolic, and genetic differences among organisms restrict HGT, so that prokaryotes preferentially share genes with other prokaryotes having properties in common, including genome size, genome G؉C composition, carbon utilization, oxygen utilization͞sensitivity, and temperature optima, further complicating attempts to reconstruct the tree of life. A new method of phylogenetic reconstruction based on gene presence and absence, called conditioned reconstruction, has improved our prospects for reconstructing prokaryotic evolution. It is also able to detect past genome fusions, such as the fusion that appears to have created the first eukaryote. This genome fusion between a deep branching eubacterium, possibly an ancestor of the cyanobacterium and a proteobacterium, with an archaeal eocyte (crenarchaea), appears to be the result of an early symbiosis. Given new tools and new genes from relevant organisms, it should soon be possible to test current and future fusion theories for the origin of eukaryotes and to discover the general outlines of the prokaryotic tree of life.
Summary. Molecular targeting of drugs and imaging agents remain important yet elusive goals in modern medicine. Technological advancements in genomics and proteomics methods have detected differentially expressed genes and proteins, uncovering many new candidate targets in a wide array of diseases and tissues. However, methods to validate potential targets in vivo tend to be quite laborious so that the validation and testing phase has become rate‐limiting in bringing treatments to the clinic. There is a critical need for integrated approaches combining state‐of‐the‐art methodologies in proteomics and in vivo imaging to accelerate validation of newly discovered vascular targets for nanomedicines, drugs, imaging agents, and gene vectors. This paper is a review of vascular targeting and proteomics, and will present recent developments in proteomic imaging. A new in vivo organellar proteomic imaging platform will be discussed, which combines subcellular fractionation, mass spectrometry, bioinformatic database interrogation, monoclonal antibody technology and a battery of imaging modalities to rapidly discover and validate tissue‐specific endothelial protein targets in vivo. Technological advancements are permitting large‐scale proteomic mapping to be performed. New targets have been discovered that permit organ‐specific targeting in vivo. Improvements in imaging are creating standards for validation of targets in vivo. Tumor imaging and radioimmunotherapy have also been improved through these efforts. Although we are moving towards a comprehensive mapping of the protein expression by the endothelium, much more needs to be done.
During decoding, a codon of messenger RNA is matched with its cognate aminoacyl-transfer RNA and the amino acid carried by the tRNA is added to the growing protein chain. Here we propose a molecular mechanism for the decoding phase of translation: the transorientation hypothesis. The model incorporates a newly identified tRNA binding site and utilizes a flip between two tRNA anticodon loop structures, the 5'-stacked and the 3'-stacked conformations. The anticodon loop acts as a three-dimensional hinge permitting rotation of the tRNA about a relatively fixed codon-anticodon pair. This rotation, driven by a conformational change in elongation factor Tu involving GTP hydrolysis, transorients the incoming tRNA into the A site from the D site of initial binding and decoding, where it can be proofread and accommodated. The proposed mechanisms are compatible with the known structures, conformations and functions of the ribosome and its component parts including tRNAs and EF-Tu, in both the GTP and GDP states.
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