Near-field penetrating optical microscopy: a live cell nanoscale refractive index measurement technique for quantification of internal macromolecular density.

Quantification of intracellular nanoscale macromolecular density distribution is a fundamental aspect to understanding cellular processes. We report a near-field penetrating optical microscopy (NPOM) technique to directly probe the internal nanoscale macromolecular density of biological cells through quantification of intracellular refractive index (RI). NPOM inserts a tapered optical fiber probe to successive depths into an illuminated sample. A 50 nm diameter probe tip collects signal that exhibits a linear relationship with the sample RI at a spatial resolution of approximately 50 nm for biologically relevant measurements, one order of magnitude finer than the Abbe diffraction limit. Live and fixed cell data illustrate the mechanical ability of a 50 nm probe to penetrate biological samples.

Proteogenomic Network Analysis of Context-Specific KRAS Signaling in Mouse-to-Human Cross-Species Translation.

The highest frequencies of KRAS mutations occur in colorectal carcinoma (CRC) and pancreatic ductal adenocarcinoma (PDAC). The ability to target downstream pathways mediating KRAS oncogenicity is limited by an incomplete understanding of the contextual cues modulating the signaling output of activated K-RAS. We performed mass spectrometry on mouse tissues expressing wild-type or mutant Kras to determine how tissue context and genetic background modulate oncogenic signaling. Mutant Kras dramatically altered the proteomes and phosphoproteomes of preneoplastic and neoplastic colons and pancreases in a context-specific manner. We developed an approach to statistically humanize the mouse networks with data from human cancer and identified genes within the humanized CRC and PDAC networks synthetically lethal with mutant KRAS. Our studies demonstrate the context-dependent plasticity of oncogenic signaling, identify non-canonical mediators of KRAS oncogenicity within the KRAS-regulated signaling network, and demonstrate how statistical integration of mouse and human datasets can reveal cross-species therapeutic insights.

Substrate-based kinase activity inference identifies MK2 as driver of colitis.

Inflammatory bowel disease (IBD) is a chronic and debilitating disorder that has few treatment options due to a lack of comprehensive understanding of its molecular pathogenesis. We used multiplexed mass spectrometry to collect high-content information on protein phosphorylation in two different mouse models of IBD. Because the biological function of the vast majority of phosphorylation sites remains unknown, we developed Substrate-based Kinase Activity Inference (SKAI), a methodology to infer kinase activity from phosphoproteomic data. This approach draws upon prior knowledge of kinase-substrate interactions to construct custom lists of kinases and their respective substrate sites, termed kinase-substrate sets that employ prior knowledge across organisms. This expansion as much as triples the amount of prior knowledge available. We then used these sets within the Gene Set Enrichment Analysis framework to infer kinase activity based on increased or decreased phosphorylation of its substrates in a dataset. When applied to the phosphoproteomic datasets from the two mouse models, SKAI predicted largely non-overlapping kinase activation profiles. These results suggest that chronic inflammation may arise through activation of largely divergent signaling networks. However, the one kinase inferred to be activated in both mouse models was mitogen-activated protein kinase-activated protein kinase 2 (MAPKAPK2 or MK2), a serine/threonine kinase that functions downstream of p38 stress-activated mitogen-activated protein kinase. Treatment of mice with active colitis with ATI450, an orally bioavailable small molecule inhibitor of the MK2 pathway, reduced inflammatory signaling in the colon and alleviated the clinical and histological features of inflammation. These studies establish MK2 as a therapeutic target in IBD and identify ATI450 as a potential therapy for the disease.

Tissue-Specific Oncogenic Activity of KRASA146T.

KRAS is the most frequently mutated oncogene. The incidence of specific KRAS alleles varies between cancers from different sites, but it is unclear whether allelic selection results from biological selection for specific mutant KRAS proteins. We used a cross-disciplinary approach to compare KRASG12D, a common mutant form, and KRASA146T, a mutant that occurs only in selected cancers. Biochemical and structural studies demonstrated that KRASA146T exhibits a marked extension of switch 1 away from the protein body and nucleotide binding site, which activates KRAS by promoting a high rate of intrinsic and guanine nucleotide exchange factor-induced nucleotide exchange. Using mice genetically engineered to express either allele, we found that KRASG12D and KRASA146T exhibit distinct tissue-specific effects on homeostasis that mirror mutational frequencies in human cancers. These tissue-specific phenotypes result from allele-specific signaling properties, demonstrating that context-dependent variations in signaling downstream of different KRAS mutants drive the KRAS mutational pattern seen in cancer. SIGNIFICANCE: Although epidemiologic and clinical studies have suggested allele-specific behaviors for KRAS, experimental evidence for allele-specific biological properties is limited. We combined structural biology, mass spectrometry, and mouse modeling to demonstrate that the selection for specific KRAS mutants in human cancers from different tissues is due to their distinct signaling properties.See related commentary by Hobbs and Der, p. 696.This article is highlighted in the In This Issue feature, p. 681.

Integrated in vivo multiomics analysis identifies p21-activated kinase signaling as a driver of colitis.

Inflammatory bowel disease (IBD) is a chronic disorder of the gastrointestinal tract that has limited treatment options. To gain insight into the pathogenesis of chronic colonic inflammation (colitis), we performed a multiomics analysis that integrated RNA microarray, total protein mass spectrometry (MS), and phosphoprotein MS measurements from a mouse model of the disease. Because we collected all three types of data from individual samples, we tracked information flow from RNA to protein to phosphoprotein and identified signaling molecules that were coordinately or discordantly regulated and pathways that had complex regulation in vivo. For example, the genes encoding acute-phase proteins were expressed in the liver, but the proteins were detected by MS in the colon during inflammation. We also ascertained the types of data that best described particular facets of chronic inflammation. Using gene set enrichment analysis and trans-omics coexpression network analysis, we found that each data set provided a distinct viewpoint on the molecular pathogenesis of colitis. Combining human transcriptomic data with the mouse multiomics data implicated increased p21-activated kinase (Pak) signaling as a driver of colitis. Chemical inhibition of Pak1 and Pak2 with FRAX597 suppressed active colitis in mice. These studies provide translational insights into the mechanisms contributing to colitis and identify Pak as a potential therapeutic target in IBD.

Systems biology of neurodegenerative diseases.

Neurodegenerative diseases (NDs) collectively afflict more than 40 million people worldwide. The majority of these diseases lack therapies to slow or stop progression due in large part to the challenge of disentangling the simultaneous presentation of broad, multifaceted pathophysiologic changes. Present technologies and computational capabilities suggest an optimistic future for deconvolving these changes to identify novel mechanisms driving ND onset and progression. In particular, integration of highly multi-dimensional omic analytical techniques (e.g., microarray, mass spectrometry) with computational systems biology approaches provides a systematic methodology to elucidate new mechanisms driving NDs. In this review, we begin by summarizing the complex pathophysiology of NDs associated with protein aggregation, emphasizing the shared complex dysregulation found in all of these diseases, and discuss available experimental ND models. Next, we provide an overview of technological and computational techniques used in systems biology that are applicable to studying NDs. We conclude by reviewing prior studies that have applied these approaches to NDs and comment on the necessity of combining analysis from both human tissues and model systems to identify driving mechanisms. We envision that the integration of computational approaches with multiple omic analyses of human tissues, and mouse and in vitro models, will enable the discovery of new therapeutic strategies for these devastating diseases.