The Alzheimer's disease gene SORL1 regulates lysosome function in human microglia.

The SORL1 gene encodes the sortilin related receptor protein SORLA, a sorting receptor that regulates endo-lysosomal trafficking of various substrates. Loss of function variants in SORL1 are causative for Alzheimer's disease (AD) and decreased expression of SORLA has been repeatedly observed in human AD brains. SORL1 is highly expressed by microglia, the tissue resident immune cells of the brain. Loss of SORLA leads to enlarged lysosomes in hiPSC-derived microglia like cells (hMGLs). However, whether SORLA deficiency contributes to microglia dysfunction and how this is relevant to AD is not known. In this study, we show that loss of SORLA results in decreased lysosomal degradation and lysosomal enzyme activity due to altered trafficking of lysosomal enzymes in hMGLs. Furthermore, lysosomal exocytosis, an important process involved in immune responses and cellular signaling, is also impaired in SORL1 deficient microglia. Phagocytic uptake of fibrillar amyloid beta 1-42 and synaptosomes is increased in SORLA deficient hMGLs, but due to reduced lysosomal degradation, these substrates aberrantly accumulate in lysosomes. Overall, these data highlight the microglial endo-lysosomal network as a potential novel pathway through which SORL1 may increase AD risk and contribute to development of AD. Additionally, our findings may inform development of novel lysosome and microglia associated drug targets for AD.

Quantitative phosphoproteomics uncovers dysregulated kinase networks in Alzheimer's disease.

Alzheimer's disease (AD) is a form of dementia characterized by amyloid-ß plaques and tau neurofibrillary tangles that progressively disrupt neural circuits in the brain. The signaling networks underlying AD pathological changes are poorly characterized at the phosphoproteome level. Using mass spectrometry, we analyzed the proteome and tyrosine, serine and threonine phosphoproteomes of temporal cortex tissue from patients with AD and aged-matched controls. We identified cocorrelated peptide clusters that were linked to varying levels of phospho-tau, oligodendrocyte, astrocyte, microglia and neuron pathologies. We found that neuronal synaptic protein abundances were strongly anti-correlated with markers of microglial reactivity. We also observed that phosphorylation sites on kinases targeting tau and other new signaling factors were correlated with these peptide modules. Finally, we used data-driven statistical modeling to identify individual peptides and peptide clusters that were predictive of AD histopathologies. Together, these results build a map of pathology-associated phosphorylation signaling events occurring in AD.

Phosphoproteomics identifies microglial Siglec-F inflammatory response during neurodegeneration.

Alzheimer's disease (AD) is characterized by the appearance of amyloid-ß plaques, neurofibrillary tangles, and inflammation in brain regions involved in memory. Using mass spectrometry, we have quantified the phosphoproteome of the CK-p25, 5XFAD, and Tau P301S mouse models of neurodegeneration. We identified a shared response involving Siglec-F which was upregulated on a subset of reactive microglia. The human paralog Siglec-8 was also upregulated on microglia in AD. Siglec-F and Siglec-8 were upregulated following microglial activation with interferon gamma (IFNγ) in BV-2 cell line and human stem cell-derived microglia models. Siglec-F overexpression activates an endocytic and pyroptotic inflammatory response in BV-2 cells, dependent on its sialic acid substrates and immunoreceptor tyrosine-based inhibition motif (ITIM) phosphorylation sites. Related human Siglecs induced a similar response in BV-2 cells. Collectively, our results point to an important role for mouse Siglec-F and human Siglec-8 in regulating microglial activation during neurodegeneration.

High-fat diet in a mouse insulin-resistant model induces widespread rewiring of the phosphotyrosine signaling network.

Obesity-associated type 2 diabetes and accompanying diseases have developed into a leading human health risk across industrialized and developing countries. The complex molecular underpinnings of how lipid overload and lipid metabolites lead to the deregulation of metabolic processes are incompletely understood. We assessed hepatic post-translational alterations in response to treatment of cells with saturated and unsaturated free fatty acids and the consumption of a high-fat diet by mice. These data revealed widespread tyrosine phosphorylation changes affecting a large number of enzymes involved in metabolic processes as well as canonical receptor-mediated signal transduction networks. Targeting two of the most prominently affected molecular features in our data, SRC-family kinase activity and elevated reactive oxygen species, significantly abrogated the effects of saturated fat exposure in vitro and high-fat diet in vivo. In summary, we present a comprehensive view of diet-induced alterations of tyrosine signaling networks, including proteins involved in fundamental metabolic pathways.

Using support vector machines to improve elemental ion identification in macromolecular crystal structures.

In the process of macromolecular model building, crystallographers must examine electron density for isolated atoms and differentiate sites containing structured solvent molecules from those containing elemental ions. This task requires specific knowledge of metal-binding chemistry and scattering properties and is prone to error. A method has previously been described to identify ions based on manually chosen criteria for a number of elements. Here, the use of support vector machines (SVMs) to automatically classify isolated atoms as either solvent or one of various ions is described. Two data sets of protein crystal structures, one containing manually curated structures deposited with anomalous diffraction data and another with automatically filtered, high-resolution structures, were constructed. On the manually curated data set, an SVM classifier was able to distinguish calcium from manganese, zinc, iron and nickel, as well as all five of these ions from water molecules, with a high degree of accuracy. Additionally, SVMs trained on the automatically curated set of high-resolution structures were able to successfully classify most common elemental ions in an independent validation test set. This method is readily extensible to other elemental ions and can also be used in conjunction with previous methods based on a priori expectations of the chemical environment and X-ray scattering.

Automated identification of elemental ions in macromolecular crystal structures.

Many macromolecular model-building and refinement programs can automatically place solvent atoms in electron density at moderate-to-high resolution. This process frequently builds water molecules in place of elemental ions, the identification of which must be performed manually. The solvent-picking algorithms in phenix.refine have been extended to build common ions based on an analysis of the chemical environment as well as physical properties such as occupancy, B factor and anomalous scattering. The method is most effective for heavier elements such as calcium and zinc, for which a majority of sites can be placed with few false positives in a diverse test set of structures. At atomic resolution, it is observed that it can also be possible to identify tightly bound sodium and magnesium ions. A number of challenges that contribute to the difficulty of completely automating the process of structure completion are discussed.