Methotrexate-based PROTACs as DHFR-specific chemical probes.

Methotrexate (MTX) is a tight-binding dihydrofolate reductase (DHFR) inhibitor, used as both an antineoplastic and immunosuppressant therapeutic. MTX, like folate undergoes folylpolyglutamate synthetase-mediated γ-glutamylation, which affects cellular retention and target specificity. Mechanisms of MTX resistance in cancers include a decrease in MTX poly-γ-glutamylation and an upregulation of DHFR. Here, we report a series of potent MTX-based proteolysis targeting chimeras (PROTACs) to investigate DHFR degradation pharmacology and one-carbon biochemistry. These on-target, cell-active PROTACs show proteasome- and E3 ligase-dependent activity, and selective degradation of DHFR in multiple cancer cell lines. By comparison, treatment with MTX increases cellular DHFR protein expression. Importantly, these PROTACs produced distinct, less-lethal phenotypes compared to MTX. The chemical probe set described here should complement conventional DHFR inhibitors and serve as useful tools for studying one-carbon biochemistry and dissecting complex polypharmacology of MTX and related drugs. Such compounds may also serve as leads for potential autoimmune and antineoplastic therapeutics.

Translational Control during Cellular Senescence.

Senescence is a state of long-term cell cycle arrest that arises in cells that have incurred sublethal damage. While senescent cells no longer replicate, they remain metabolically active and further develop unique and stable phenotypes that are not present in proliferating cells. On one hand, senescent cells increase in size, maintain an active mTORC1 complex, and produce and secrete a substantial amount of inflammatory proteins as part of the senescence-associated secretory phenotype (SASP). On the other hand, these progrowth phenotypes contrast with the p53-mediated growth arrest typical of senescent cells that is associated with nucleolar stress and an inhibition of rRNA processing and ribosome biogenesis. In sum, translation in senescent cells paradoxically comprises both a global repression of translation triggered by DNA damage and a select increase in the translation of specific proteins, including SASP factors.

A methodology for discovering novel brain-relevant peptides: Combination of ribosome profiling and peptidomics.

Brain derived peptides function as signaling molecules in the brain and regulate various physiological and behavioral processes. The low abundance and atypical fragmentation of these brain derived peptides makes detection using traditional proteomic methods challenging. In this study, we introduce and validate a new methodology for the discovery of novel peptides derived from mammalian brain. This methodology combines ribosome profiling and mass spectrometry-based peptidomics. Using this framework, we have identified a novel peptide in mouse whole brain whose expression is highest in the basal ganglia, hypothalamus and amygdala. Although its functional role is unknown, it has been previously detected in peripheral tissue as a component of the mRNA decapping complex. Continued discovery and studies of novel regulating peptides in mammalian brain may also provide insight into brain disorders.

Integrated microfluidic chip and online SCX separation allows untargeted nanoscale metabolomic and peptidomic profiling.

Metabolomics and peptidomics are systems biology approaches in which broad populations of molecular species produced in a cell or tissue sample are identified and quantified. These two molecular populations, metabolites and peptides, can be extracted from tissues in a similar fashion, and we therefore have here developed an integrated platform for their extraction and characterization. This was accomplished by liquid-liquid extraction of peptides and metabolites from tissue samples and online strong cation exchange (SCX) separation to allow characterization of each population individually. The platform was validated both by a mixed set of purified standards and by an analysis of splenic tissue from SIV-infected macaques, showing both good reproducibility in chromatography, with relative standard deviation (RSD) of hold time less than 0.4%, and clear separation of charge state, with ∼ 95% of molecular features in SCX separated runs at charge states of +1 or +2. Finally, we used this platform to analyze the physiological response to infection in the spleen, showing that the spleen contains an abundance of hemoglobin-derived peptides, which do not appear to change in response to infection, and that there appears to be a large and variable metabolic response to infection. We therefore present a method for peptidomic and metabolomic profiling which is simple, robust, and easy to implement.

Data maximization by multipass analysis of protein mass spectra.

With the proliferation of search engines for the analysis of MS data, multisearch techniques aimed at boosting the discriminating power of the search engines' score functions have recently become popular. Much statistical and algorithmic work has been done, therefore, in order to be able to combine and parse multiple search streams. However, multisearch techniques suffer from long run times, and may have little impact on false negatives because of similar peptide filtering heuristics between searches. This review focuses, rather, on multipass techniques, which use the results of one search to guide the selection of spectra, parameters and sequences in subsequent searches. This reduces the number of false-negative peptide identifications due to peptide candidate filtering while preserving statistical significance of existing (correct) identifications. Furthermore, this technique avoids substantial increases in running time and, by limiting the search space, does not reduce the statistical significance of correct identifications or introduce a statistically significant number of false-positive identifications. However, we argue that the existing combiner tools are not reliably applicable to these multipass situations, because of algorithmic assumptions about search space and statistical assumptions about the rate of true positives. Here we provide an overview of the advantages of and issues in multipass analysis techniques, the existing methods and workflows available to proteomic researchers, and the unsolved statistical and algorithmic issues amenable to future research.