Regional Distribution, Non-invasive Detection, and Genetic Diversity of the Black-spotted Newt (Notophthalmus meridionalis)

The Black-spotted Newt (Notophthalmus meridionalis ) is one of three a salamander species native to Gulf Coast prairies of Texas and Mexico, with respective state and federal protections. This species has been neglected by the scientific community despite concerns of dramatic population declines and a globally endangered status, with the most recent work being conducted in the early 1990’s going unpublished. This study presents the most recent examination of the species providing probabilistic distribution maps, descriptions of three novel populations, assessments of surveying techniques, and the first known examination of intrapopulation genetics including the first documented genetic examination of the southern subspecies. An updated status review is provided in response to the results of this study and an expansive literature review

“Inverse Drug Discovery” Strategy To Identify Proteins That Are Targeted by Latent Electrophiles As Exemplified by Aryl Fluorosulfates

Drug candidates are generally discovered using biochemical screens employing an isolated target protein or by utilizing cell-based phenotypic assays. Both noncovalent and covalent hits emerge from such endeavors. Herein, we exemplify an “Inverse Drug Discovery” strategy in which organic compounds of intermediate complexity harboring weak, but activatable, electrophiles are matched with the protein(s) they react with in cells or cell lysate. An alkyne substructure in each candidate small molecule enables affinity chromatography–mass spectrometry, which produces a list of proteins that each distinct compound reacts with. A notable feature of this approach is that it is agnostic with respect to the cellular proteins targeted. To illustrate this strategy, we employed aryl fluorosulfates, an underexplored class of sulfur­(VI) halides, that are generally unreactive unless activated by protein binding. Reversible aryl fluorosulfate binding, correct juxtaposition of protein side chain functional groups, and transition-state stabilization of the S­(VI) exchange reaction all seem to be critical for conjugate formation. The aryl fluorosulfates studied thus far exhibit chemoselective reactivity toward Lys and, particularly, Tyr side chains, and can be used to target nonenzymes (e.g., a hormone carrier or a small-molecule carrier protein) as well as enzymes. The “Inverse Drug Discovery” strategy should be particularly attractive as a means to explore latent electrophiles not typically used in medicinal chemistry efforts, until one reacts with a protein target of exceptional interest. Structure–activity data can then be used to enhance the selectivity of conjugate formation or the covalent probe can be used as a competitor to develop noncovalent drug candidates. Here we use the “Inverse Drug Discovery” platform to identify and validate covalent ligands for 11 different human proteins. In the case of one of these proteins, we have identified and validated a small-molecule probe for the first time

“Inverse Drug Discovery” Strategy To Identify Proteins That Are Targeted by Latent Electrophiles As Exemplified by Aryl Fluorosulfates

Drug candidates are generally discovered using biochemical screens employing an isolated target protein or by utilizing cell-based phenotypic assays. Both noncovalent and covalent hits emerge from such endeavors. Herein, we exemplify an “Inverse Drug Discovery” strategy in which organic compounds of intermediate complexity harboring weak, but activatable, electrophiles are matched with the protein(s) they react with in cells or cell lysate. An alkyne substructure in each candidate small molecule enables affinity chromatography–mass spectrometry, which produces a list of proteins that each distinct compound reacts with. A notable feature of this approach is that it is agnostic with respect to the cellular proteins targeted. To illustrate this strategy, we employed aryl fluorosulfates, an underexplored class of sulfur­(VI) halides, that are generally unreactive unless activated by protein binding. Reversible aryl fluorosulfate binding, correct juxtaposition of protein side chain functional groups, and transition-state stabilization of the S­(VI) exchange reaction all seem to be critical for conjugate formation. The aryl fluorosulfates studied thus far exhibit chemoselective reactivity toward Lys and, particularly, Tyr side chains, and can be used to target nonenzymes (e.g., a hormone carrier or a small-molecule carrier protein) as well as enzymes. The “Inverse Drug Discovery” strategy should be particularly attractive as a means to explore latent electrophiles not typically used in medicinal chemistry efforts, until one reacts with a protein target of exceptional interest. Structure–activity data can then be used to enhance the selectivity of conjugate formation or the covalent probe can be used as a competitor to develop noncovalent drug candidates. Here we use the “Inverse Drug Discovery” platform to identify and validate covalent ligands for 11 different human proteins. In the case of one of these proteins, we have identified and validated a small-molecule probe for the first time

Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease.

Identifying accurate biomarkers of cognitive decline is essential for advancing early diagnosis and prevention therapies in Alzheimer's disease. The Alzheimer's disease DREAM Challenge was designed as a computational crowdsourced project to benchmark the current state-of-the-art in predicting cognitive outcomes in Alzheimer's disease based on high dimensional, publicly available genetic and structural imaging data. This meta-analysis failed to identify a meaningful predictor developed from either data modality, suggesting that alternate approaches should be considered for prediction of cognitive performance

Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease

Identifying accurate biomarkers of cognitive decline is essential for advancing early diagnosis and prevention therapies in Alzheimer's disease. The Alzheimer's disease DREAM Challenge was designed as a computational crowdsourced project to benchmark the current state-of-the-art in predicting cognitive outcomes in Alzheimer's disease based on high dimensional, publicly available genetic and structural imaging data. This meta-analysis failed to identify a meaningful predictor developed from either data modality, suggesting that alternate approaches should be considered for prediction of cognitive performance