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Supplementary Figures 1-7, Tables 1-4 from von Hippel-Lindau–Dependent Patterns of RNA Polymerase II Hydroxylation in Human Renal Clear Cell Carcinomas

Purpose: We have previously shown that von Hippel-Lindau (VHL) regulates ubiquitylation and proline 1465 hydroxylation of the large subunit of RNA polymerase II, Rpb1, in human renal clear cell carcinoma (RCC) cell lines. Here, our goal was to determine the effect of this VHL function and the status of P1465 hydroxylation in human RCC tumors.Experimental Design: Primary human tumors and matched normal kidney samples were probed for expression levels of the large subunit of RNA polymerase II (Rpb1), Rpb1 hydroxylated on P1465 [Rpb1(OH)], Rpb1 phosphorylated on Ser5 [Rpb1(S5P)], and proline hydroxylases PHD1, PHD2, and PHD3. Results from RCC tumors were categorized according to the status of VHL gene. Mechanistic analysis was performed in orthotopic xenograft model using 786-O RCC cells with wild-type (WT) VHL and knockdown of PHD2, characterized by high levels of Rpb1(OH) and PHD1.Results: Levels of Rpb1(OH), PHD1, and PHD2 were significantly higher in RCC tumors compared with normal kidneys. RCC tumors with WT VHL had higher levels of Rpb1(OH) and PHD1 and lower levels of PHD2 than tumors with VHL gene alterations. Levels of Rpb1(OH) significantly correlated with levels of PHD1 in tumors and normal kidneys. Knockdown of PHD2 in 786-O VHL(+) cells resulted in a more malignant phenotype in orthotopic xenografts and higher expression of specific cell cycle regulators (CDC25A, cyclin-dependent kinase 2, CCNA2) compared with VHL(−) RCC cells.Conclusions: Elevated PHD1 concomitant with decreased PHD2 are causatively related to Rpb1 hydroxylation and oncogenesis in human RCC tumors with WT VHL gene. Thus, P1465-hydroxylated Rpb1 and PHD1 represent attractive drug targets for new RCC treatments. Clin Cancer Res; 16(21); 5142–52. ©2010 AACR.

The COVID-19 pandemic caused by the novel SARS-CoV-2 is more contagious than other coronaviruses and has higher rates of mortality than influenza. Identification of effective therapeutics is a crucial tool to treat those infected with SARS-CoV-2 and limit the spread of this novel disease globally. We deployed a bioinformatics workflow to identify candidate drugs for the treatment of COVID-19. Using an “omics” repository, the Library of Integrated Network-Based Cellular Signatures (LINCS), we simultaneously probed transcriptomic signatures of putative COVID-19 drugs and publicly available SARS-CoV-2 infected cell lines to identify novel therapeutics. We identified a shortlist of 20 candidate drugs: 8 are already under trial for the treatment of COVID-19, the remaining 12 have antiviral properties and 6 have antiviral efficacy against coronaviruses specifically, in vitro. All candidate drugs are either FDA approved or are under investigation. Our candidate drug findings are discordant with (i.e., reverse) SARS-CoV-2 transcriptome signatures generated in vitro, and a subset are also identified in transcriptome signatures generated from COVID-19 patient samples, like the MEK inhibitor selumetinib. Overall, our findings provide additional support for drugs that are already being explored as therapeutic agents for the treatment of COVID-19 and identify promising novel targets that are worthy of further investigation.

MAP1LC3C (LC3C) is a member of the microtubule associated family of proteins that are essential in the formation of autophagosomes and lysosomal degradation of cargo. LC3C has tumor suppressing activity and its expression is dependent on kidney cancer tumor suppressors, such as VHL and FLCN. Recently we demonstrated that LC3C autophagy is regulated by noncannonical upstream regulatory complexes and targets for degradation postdivision midbody rings associated with cancer cells stemness. Here we show that loss of LC3C leads to peripheral positioning of the lysosomes and lysosomal exocytosis (LE) in a subset of cells. This process is independent of the autophagic activity of LC3C. Analysis of isogenic cells with low and high LE shows substantial transcriptomic reprogramming with altered expression of Zn-related genes and activity of Polycomb Repressor Complex 2 (PRC2), accompanied by a robust decrease in intracellular Zn. Metabolomic analysis revealed alterations in amino acid steady-state levels. Cells with augmented LE show tumor initiation properties and form aggressive tumors in xenograft models. Immunocytochemistry identified high levels of LAMP1 on the plasma membrane of cancer cells in human ccRCC and reduced levels of Zn, an indication that LE is a frequent event in ccRCC, potentially contributing to the loss of Zn. Overall, these data indicate that an important tumor suppressing activity of LC3C is contributing to the reprogramming of lysosomal activity and Zn metabolism with implication for epigenetic remodeling in a subpopulation of tumor propagating properties of cancer cells.

Respiratory microbiome is an understudied area of research compared to other microbiomes of the human body. The respiratory tract is exposed to an array of environmental pollutants, including microbes. Yet, we know very little about the relationship between environmental and respiratory microbiome. The primary aim of our study was to compare the mycobiomes and bacteriomes between three sample types from the same participants, including home dust, saliva, and sputum. Samples were collected from 40 adolescents in a longitudinal cohort. We analyzed the samples using 16s bacterial rDNA and ITS fungal rDNA gene sequencing, as well as quantitative PCR with universal fungal and bacterial primers. Results showed that home dust had the greatest alpha diversity between the three sample types for both bacteria and fungi. Dust had the highest total fungal load and the lowest total bacterial load. Sputum had greater bacterial diversity than saliva, but saliva had greater fungal diversity than sputum. The distribution of major bacterial phyla differed between all sample types. However, the distribution of major fungal classes differed only between sputum and saliva. Future research should examine the biological significance of the taxa found in each sample type based on microbial ecology and associations with health effects.

722 Background: Previous large scale genomic studies have described somatic alterations that affect tumor progression. However, metabolic and immune pathway changes that occur with advancing disease stage are not well characterized. Herein, we performed transcriptomic profiling of clear-cell renal carcinoma (ccRCC) tumors across clinical stages to define these changes. Methods: TCGA Firehose Legacy cohort was used to determine differential gene expression between stage 3 ccRCCs from patients who remained disease free (S3DF) vs. those who relapsed (S3RL) within two years of nephrectomy. Single cell RNA-seq (scRNAseq) analysis was performed on primary ccRCCs from nine patients (stage 1 n=4, stage 3 n=4, metastatic n=1). Seurat V4 package and module score was used to analyze patterns of gene expression associated with tumor progression at the single cell level. Results: Transcriptomic comparison of ccRCCs from TCGA Firehose Legacy cohort revealed induction of genes encoding mitochondrial respiratory complexes and mitochondrial ribosomal proteins (MRPs) and decrease in expression of immune genes in S3RL as compared to S3DF tumors. Using a set of 23 differentially regulated mitochondrial and immune genes we established a transcriptomic signature that stratified patients into those at risk of relapse vs. not. ScRNA-seq data analysis determined increased expression of mitochondrial respiratory complexes’ gene-sets, and decreased levels of glycolytic genes and HIF targets during tumor progression from stage-I to metastatic. This was supported by spatial transcriptomics of cancer cells, which revealed clusters of cells with high mitochondrial activity and decreased MHC class I and II gene expression. Conclusions: The data support a model of progressive metabolic reprogramming from glycolysis and HIF-regulated pathways to mitochondrial respiration in parallel with diminished immune expression of antigens on cancer cells with advancing disease stage in ccRCC. Identification of targetable mitochondrial vulnerability could be a therapeutic option in patients who progress on immune checkpoint inhibitors.

Biological regulatory networks are dynamic, intertwined, and complex systems making them challenging to study. While quantitative measurements of transcripts and proteins are key to investigate the state of a biological system, they do not inform the "active" state of regulatory networks. In consideration of that fact, "functional" proteomics assessments are needed to decipher active regulatory processes. Phosphorylation, a key post-translation modification, is a reversible regulatory mechanism that controls the functional state of proteins. Recent advancements of high-throughput protein kinase activity profiling platforms allow for a broad assessment of protein kinase networks in complex biological systems. In conjunction with sophisticated computational modeling techniques, these profiling platforms provide datasets that inform the active state of regulatory systems in disease models and highlight potential drug targets. Taken together, system-wide profiling of protein kinase activity has become a critical component of modern molecular biology research and presents a promising avenue for drug discovery.

The work identifies a noncanonical autophagic program mediated by the hominid-specific LC3C paralog functioning downstream from the von Hippel–Lindau tumor suppressor. The program targets for lysosomal degradation postdivision midbodies in a mechanism requiring FIP200/ATG13/ULK3 and UVRAG/RUBCN/PIK3C2A/BECN1 complexes as well as the C-terminal peptide of LC3C., LC3s are canonical proteins necessary for the formation of autophagosomes. We have previously established that two paralogs, LC3B and LC3C, have opposite activities in renal cancer, with LC3B playing an oncogenic role and LC3C a tumor-suppressing role. LC3C is an evolutionary late gene present only in higher primates and humans. Its most distinct feature is a C-terminal 20-amino acid peptide cleaved in the process of glycine 126 lipidation. Here, we investigated mechanisms of LC3C-selective autophagy. LC3C autophagy requires noncanonical upstream regulatory complexes that include ULK3, UVRAG, RUBCN, PIK3C2A, and a member of ESCRT, TSG101. We established that postdivision midbody rings (PDMBs) implicated in cancer stem-cell regulation are direct targets of LC3C autophagy. LC3C C-terminal peptide is necessary and sufficient to mediate LC3C-dependent selective degradation of PDMBs. This work establishes a new noncanonical human-specific selective autophagic program relevant to cancer stem cells.

While the pathophysiology of schizophrenia has been extensively investigated using homogenized postmortem brain samples, few studies have examined changes in brain samples with techniques that may attribute perturbations to specific cell types. To fill this gap, we performed microarray assays on mRNA isolated from anterior cingulate cortex (ACC) superficial and deep pyramidal neurons from 12 schizophrenia and 12 control subjects using laser-capture microdissection. Among all the annotated genes, we identified 134 significantly increased and 130 decreased genes in superficial pyramidal neurons, while 93 significantly increased and 101 decreased genes were found in deep pyramidal neurons, in schizophrenia compared to control subjects. In these differentially expressed genes, we detected lamina-specific changes of 55 and 31 genes in superficial and deep neurons in schizophrenia, respectively. Gene set enrichment analysis (GSEA) was applied to the entire pre-ranked differential expression gene lists to gain a complete pathway analysis throughout all annotated genes. Our analysis revealed overrepresented groups of gene sets in schizophrenia, particularly in immunity and synapse-related pathways, suggesting the disruption of these pathways plays an important role in schizophrenia. We also detected other pathways previously demonstrated in schizophrenia pathophysiology, including cytokine and chemotaxis, postsynaptic signaling, and glutamatergic synapses. In addition, we observed several novel pathways, including ubiquitin-independent protein catabolic process. Considering the effects of antipsychotic treatment on gene expression, we applied a novel bioinformatics approach to compare our differential expression gene profiles with 51 antipsychotic treatment datasets, demonstrating that our results were not influenced by antipsychotic treatment. Taken together, we found pyramidal neuron-specific changes in neuronal immunity, synaptic dysfunction, and olfactory dysregulation in schizophrenia, providing new insights for the cell-subtype specific pathophysiology of chronic schizophrenia.

Tobacco smoking (TS) results in reprogramming of major metabolic pathways, including glycolysis, the citric acid (TCA) cycle, oxidative phosphorylation, and metabolism of aspartate, glutamate and glutamine in clear cell renal cell carcinoma (ccRCC). TS alters the distribution and activities of cadmium, arsenic and copper in a manner mechanistically supporting metabolic remodeling. Alterations in metabolism and metal distribution identify new actionable targets for treatment of ccRCC.

LC3s are canonical proteins necessary for the formation of autophagosomes. We have previously established that two paralogs, LC3B and LC3C, have opposite activities in renal cancer, with LC3B playing oncogenic role and LC3C tumor suppressing role. LC3C is an evolutionary late gene, present only in higher primates and humans. Its most distinct feature is a C-terminal 20 amino acid peptide cleaved in the process of glycine 126 lipidation. Here we investigated mechanisms of LC3C selective autophagy. LC3C autophagy requires noncanonical upstream regulatory complexes that include ULK3, UVRAG, RUBCN, PIK3C2A, and a member of ESCRT, TSG101. We established that Postdivision Midbody Rings (PDMBs) implicated in cancer stem cell regulation are direct targets of LC3C autophagy. LC3C C-terminal peptide is necessary and sufficient to mediate LC3C-dependent selective degradation of PDMBs. This work establishes a new noncanonical human-specific selective autophagic program relevant to cancer stem cells.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is the most common histologically defined renal cancer. However, it is not a uniform disease and includes several genetic subtypes with different prognoses. ccRCC is also characterized by distinctive metabolic reprogramming. Tobacco smoking (TS) is an established risk factor for ccRCC, with unknown effects on tumor pathobiology. METHODS: We investigated the landscape of ccRCCs and paired normal kidney tissues using integrated transcriptomic, metabolomic, and metallomic approaches in a cohort of white males who were long-term current smokers (LTS) or were never smokers (NS). RESULTS: All 3 Omics domains consistently identified a distinct metabolic subtype of ccRCCs in LTS, characterized by activation of oxidative phosphorylation (OXPHOS) coupled with reprogramming of the malate-aspartate shuttle and metabolism of aspartate, glutamate, glutamine, and histidine. Cadmium, copper, and inorganic arsenic accumulated in LTS tumors, showing redistribution among intracellular pools, including relocation of copper into the cytochrome c oxidase complex. A gene expression signature based on the LTS metabolic subtype provided prognostic stratification of The Cancer Genome Atlas ccRCC tumors that was independent of genomic alterations. CONCLUSION: The work identified the TS-related metabolic subtype of ccRCC with vulnerabilities that can be exploited for precision medicine approaches targeting metabolic pathways. The results provided rationale for the development of metabolic biomarkers with diagnostic and prognostic applications using evaluation of OXPHOS status. The metallomic analysis revealed the role of disrupted metal homeostasis in ccRCC, highlighting the importance of studying effects of metals from e-cigarettes and environmental exposures. FUNDING: Department of Defense, Veteran Administration, NIH, ACS, and University of Cincinnati Cancer Institute.

The development of targeted treatment options for precision medicine is hampered by a slow and costly process of drug screening. While small molecule docking simulations are often applied in conjunction with cheminformatic methods to reduce the number of candidate molecules to be tested experimentally, the current approaches suffer from high false positive rates and are computationally expensive. Here, we present a novel in silico approach for drug discovery and repurposing, dubbed connectivity enhanced Structure Activity Relationship (ceSAR) that improves on current methods by combining docking and virtual screening approaches with pharmacogenomics and transcriptional signature connectivity analysis. ceSAR builds on the landmark LINCS library of transcriptional signatures of over 20,000 drug-like molecules and ~5,000 gene knock-downs (KDs) to connect small molecules and their potential targets. For a set of candidate molecules and specific target gene, candidate molecules are first ranked by chemical similarity to their ‘concordant’ LINCS analogs that share signature similarity with a knock-down of the target gene. An efficient method for chemical similarity search, optimized for sparse binary fingerprints of chemical moieties, is used to enable fast searches for large libraries of small molecules. A small subset of candidate compounds identified in the first step is then re-scored by combining signature connectivity with docking simulations. On a set of 20 DUD-E benchmark targets with LINCS KDs, the consensus approach reduces significantly false positive rates, improving the median precision 3-fold over docking methods at the extreme library reduction. We conclude that signature connectivity and docking provide complementary signals, offering an avenue to improve the accuracy of virtual screening while reducing run times by multiple orders of magnitude.

The COVID-19 pandemic caused by the novel SARS-CoV-2 is more contagious than other coronaviruses and has higher rates of mortality than influenza. As no vaccine or drugs are currently approved to specifically treat COVID-19, identification of effective therapeutics is crucial to treat the afflicted and limit disease spread. We deployed a bioinformatics workflow to identify candidate drugs for the treatment of COVID-19. Using an “omics” repository, the Library of Integrated Network-Based Cellular Signatures (LINCS), we simultaneously probed transcriptomic signatures of putative COVID-19 drugs and signatures of coronavirus-infected cell lines to identify therapeutics with concordant signatures and discordant signatures, respectively. Our findings include three FDA approved drugs that have established antiviral activity, including protein kinase inhibitors, providing a promising new category of candidates for COVID-19 interventions.

BackgroundIn silico data exploration is a key first step of exploring a research question. There are many publicly available databases and tools that offer appealing features to help with such a task. However, many applications lack exposure or are constrained with unfriendly or outdated user interfaces. Thus, it follows that there are many resources that are relevant to investigation of medical disorders that are underutilized.ResultsWe developed an R Shiny web application, called Kaleidoscope, to address this challenge. The application offers access to several omics databases and tools to let users explore research questions in silico. The application is designed to be user- friendly with a unified user interface, while also scalable by offering the option of uploading user-defined datasets. We demonstrate the application features with a starting query of a single gene (Disrupted in schizophrenia 1, DISC1) to assess its protein-protein interactions network. We then explore expression levels of the gene network across tissues and cell types in the brain, as well as across 34 schizophrenia versus control differential gene expression datasets.ConclusionKaleidoscope provides easy access to several databases and tools under a unified user interface to explore research questions in silico. The web application is open-source and freely available at https://kalganem.shinyapps.io/Kaleidoscope/. This application streamlines the process of in silico data exploration for users and expands the efficient use of these tools to stakeholders without specific bioinformatics expertise.

While the pathophysiology of schizophrenia has been extensively investigated using homogenized postmortem brain samples, few studies have examined changes in brain samples with techniques that may attribute perturbations to specific cell types. To fill this gap, we performed microarray assays on mRNA isolated from anterior cingulate cortex (ACC) superficial and deep pyramidal neurons from 12 schizophrenia and 12 control subjects using laser capture microdissection. Among all the annotated genes, we identified 134 significantly increased and 130 decreased genes in superficial pyramidal neurons, while 93 significantly increased and 101 decreased genes were found in deep pyramidal neurons, in schizophrenia compared to control subjects. In these differentially expressed genes, we detected lamina-specific changes of 55 and 31 genes in superficial and deep neurons in schizophrenia, respectively. Gene set enrichment analysis (GSEA) was applied to the entire pre-ranked differential expression gene lists to gain a complete pathway analysis throughout all annotated genes. Our analysis revealed over-represented groups of gene sets in schizophrenia, particularly in immunity and synapse related pathways in pyramidal neurons, suggesting the disruption of these pathways plays an important role in schizophrenia. We also detected pathways previously demonstrated in schizophrenia pathophysiology, including cytokine and chemotaxis, post-synaptic signaling, and glutamatergic synapses. In addition, we observed several novel pathways, including ubiquitin-independent protein catabolic process. By comparing our differential expression gene profiles with 51 antipsychotic treatment datasets, we demonstrated that our results were not influenced by antipsychotic treatment of our subjects. Taken together, we found pyramidal neuron-specific changes in neuronal immunity, synaptic dysfunction, and olfactory dysregulation in schizophrenia, providing new insights for the cell-subtype specific pathophysiology of chronic schizophrenia.

378 Background: Patients with localized or locally advanced ccRCC undergo surgical resection by partial or radical nephrectomy. Approximately 50% of tumors recur within five years after surgery. Currently the only FDA approved drug in the adjuvant setting is sunitinib. However, the absence of an overall survival benefit and the prevalence of side effects limit widespread use of sunitinib. Accurate identification of patients at high risk of recurrence would enhance targeted adjuvant therapy, while sparing low-risk patients from side effects. A single scoring system, transcriptomic signature or genomic classifier, is unlikely to accurately define prognosis. Here, we describe an integrated transcriptomic-metallomic classifier to assess the risk of relapse in localized ccRCC. Methods: We investigated the transcriptomic landscape of Caucasian male patients with stage III ccRCCs who remained disease free (S3DF) or relapsed (S3RL) within 24 months after surgery using the TCGA Firehose Legacy cohort. In a separate cohort of S3RL and S3DF ccRCCs, copper (Cu) content and molecular distribution were analyzed by size exclusion chromatography coupled to inductively coupled plasma mass spectrometry (SEC-ICP-MS). Results: Transcriptomic analysis identified that S3RL tumors were enriched for genes encoding subunits of mitochondrial electron transport chain (ETC) and mitochondrial ribosomal proteins (MRPs), an indication that mitochondrial activity may contribute to the relapse. In contrast, S3DF tumors were enrichedfor genes related to immune responses, including genes encoding members ofMHC-II, suggesting tumor control by the immune system. We established a setof 23 genes (23G) as a signature that stratified ccRCCsinto distinct groups characterized by low, intermediateand high risk of relapse after surgery. Importantly, SEC-ICP-MS analysis identified augmented distribution of Cu to the Cu cytochrome c oxidase complex (Cu-COX) in tumor mitochondria from S3RL, consistent with the prediction of increased ETC activity in the transcriptomic analysis. Conclusions: Integration of the 23G transcriptomic signature and Cu-COX complex measurements in locoregional ccRCC can serve as a prognostic biomarker for recurrence, and as a predictive classifier for novel treatments, including inhibitors of ETC for the S3RL tumors. In particular, a combination classifier using both measurements may have a stronger prognostic/predictive power as compared to either biomarker alone.

Mobilization of hematopoietic stem cells (HSCs) from bone marrow (BM) to peripheral blood (PB) by cytokine granulocyte colony-stimulating factor (G-CSF) or the chemical antagonist of CXCR4, AMD3100, is important in the treatment of blood diseases. Due to clinical conditions of each application, there is a need for continued improvement of HSC mobilization regimens. Previous studies have shown that genetic ablation of the Rho GTPase Cdc42 in HSCs results in their mobilization without affecting survival. Here we rationally identified a Cdc42 activity-specific inhibitor (CASIN) that can bind to Cdc42 with submicromolar affinity and competitively interfere with guanine nucleotide exchange activity. CASIN inhibits intracellular Cdc42 activity specifically and transiently to induce murine hematopoietic stem/progenitor cell egress from the BM by suppressing actin polymerization, adhesion, and directional migration of stem/progenitor cells, conferring Cdc42 knockout phenotypes. We further show that, although, CASIN administration to mice mobilizes similar number of phenotypic HSCs as AMD3100, it produces HSCs with better long-term reconstitution potential than that by AMD3100. Our work validates a specific small molecule inhibitor for Cdc42, and demonstrates that signaling molecules downstream of cytokines and chemokines, such as Cdc42, constitute a useful target for long-term stem cell mobilization.

The Library of Integrated Network-based Cellular Signatures (LINCS) program is a national consortium funded by the NIH to generate a diverse and extensive reference library of cell-based perturbation-response signatures, along with novel data analytics tools to improve our understanding of human diseases at the systems level. In contrast to other large-scale data generation efforts, LINCS Data and Signature Generation Centers (DSGCs) employ a wide range of assay technologies cataloging diverse cellular responses. Integration of, and unified access to LINCS data has therefore been particularly challenging. The Big Data to Knowledge (BD2K) LINCS Data Coordination and Integration Center (DCIC) has developed data standards specifications, data processing pipelines, and a suite of end-user software tools to integrate and annotate LINCS-generated data, to make LINCS signatures searchable and usable for different types of users. Here, we describe the LINCS Data Portal (LDP) (http://lincsportal.ccs.miami.edu/), a unified web interface to access datasets generated by the LINCS DSGCs, and its underlying database, LINCS Data Registry (LDR). LINCS data served on the LDP contains extensive metadata and curated annotations. We highlight the features of the LDP user interface that is designed to enable search, browsing, exploration, download and analysis of LINCS data and related curated content.

Background Abnormalities of cellular signaling are well characterized in neuropsychiatric illnesses, including schizophrenia. Changes in signaling pathways reflect the underlying genetic, environmental, and epigenetic perturbations driving disease phenotypes. A shortcoming of most signaling studies is a focus on one or a few protein kinases at a time, a limitation since protein kinases work in networks with other kinases, phosphatases, and regulatory molecules to effect signaling events. We addressed this challenge by employing a kinome array platform that simultaneously measures protein kinase activity at hundreds of reporter peptide substrates. We then developed a novel bioinformatics pipeline to identify protein kinase nodes, signaling networks, upstream biological pathways, and drug candidates that “reverse” kinomic disease signatures. Methods Postmortem DLPFC brain samples from subjects with schizophrenia (n = 20 per group, 10 males and 10 females per group), were compared to age, PMI and pH matched control subjects (n = 20 per group, 10 males and 10 females per group) using the Pamgene12 serine/threonine kinome array chip. Samples were pooled by diagnosis and gender, and run in triplicate. The R-shiny app KRSA was created to automate assignment of kinases, perform permutation analyses, identify biological pathways, and connect to iLINCs for identification of drugs that reverse kinomic disease signatures. We also performed targeted confirmation studies using specific kinase activity assays, QPCR, and western blot analyses. Results We identified unique and common kinase nodes for each diagnostic group. Several of the nodes (for example AKT) are well characterized in schizophrenia, while others have not previously been identified (such as AMPK). We used AMPK KD cultures and AMPK KO brain tissues to demonstrate the validity if the kinome array for this protein kinase. We used standard kinase activity assays for AMPK and found decreased activity for AMPK (P < 0.05). We also found decreased expression of transcripts for the regulatory subunits of AMPK (P < 0.05). We identified several unique biological pathways, as well as candidate drugs, associated with the disease signature in schizophrenia. Discussion Our results confirm well characterized signaling defects in severe neuropsychiatric illness, and identify novel signaling nodes for further study. Confirmation studies for AMPK kinase show significant changes in expression and activity of this kinase, suggesting perturbation of energy sensing and production pathways in schizophrenia. Bioenergetic pathways may be targeted by myriad mechanisms, and we identified several drug candidates that might help restore this pathway in afflicted persons. Overall our novel workflow and pipeline provides a promising new avenue for understanding the complex signaling perturbations found in brain diseases and may provide new leads for developing treatments for schizophrenia and other cognitive disorders.

We report the synthesis and preliminary characterization of IODVA1, a potent small molecule that is active in xenograft mouse models of Ras-driven lung and breast cancers. In an effort to inhibit oncogenic Ras signaling, we combined in silico screening with inhibition of proliferation and colony formation of Ras-driven cells. NSC124205 fulfilled all criteria. HPLC analysis revealed that NSC124205 was a mixture of at least three compounds, from which IODVA1 was determined to be the active component. IODVA1 decreased 2D and 3D cell proliferation, cell spreading and ruffle and lamellipodia formation through downregulation of Rac activity. IODVA1 significantly impaired xenograft tumor growth of Ras-driven cancer cells with no observable toxicity. Immuno-histochemistry analysis of tumor sections suggests that cell death occurs by increased apoptosis. Our data suggest that IODVA1 targets Rac signaling to induce death of Ras-transformed cells. Therefore, IODVA1 holds promise as an anti-tumor therapeutic agent.

There are only a few platforms that integrate multiple omics data types, bioinformatics tools, and interfaces for integrative analyses and visualization that do not require programming skills. Among these, iLINCS is unique in scope and versatility of the data provided and the analytics facilitated. iLINCS (http://ilincs.org) is an integrative web-based platform for analysis of omics data and signatures of cellular perturbations. The platform facilitates analysis of user-submitted omics signatures of diseases and cellular perturbations in the context of a large compendium of pre-computed signatures (>200,000), as well as mining and re-analysis of the large collection of omics datasets (>12,000), pre-computed signatures, and their connections. Analytics workflows driven by user-friendly interfaces enable users with only conceptual understanding of the analysis strategy to execute sophisticated analyses of omics signatures, such as systems biology analyses and interpretation of signatures, mechanism of action analysis, and signature-driven drug repositioning. In summary, iLINCS workflows integrate vast omics data resources and a range of analytics and interactive visualization tools into a comprehensive platform for analysis of omics signatures.

Protein kinases orchestrate signal transduction pathways involved in central nervous system functions ranging from neurodevelopment to synaptic transmission and plasticity. Abnormalities in kinase-mediated signaling are involved in the pathophysiology of neurological disorders, including neuropsychiatric disorders. Here, we expand on the hypothesis that kinase networks are dysregulated in schizophrenia. We investigated changes in serine/threonine kinase activity in cortical excitatory neurons differentiated from induced pluripotent stem cells (iPSCs) from a schizophrenia patient presenting with a 4 bp mutation in the disrupted in schizophrenia 1 (DISC1) gene and a corresponding control. Using kinome peptide arrays, we demonstrate large scale abnormalities in DISC1 cells, including a global depression of serine/threonine kinase activity, and changes in activity of kinases, including AMP-activated protein kinase (AMPK), extracellular signal-regulated kinases (ERK), and thousand-and-one amino acid (TAO) kinases. Using isogenic cell lines in which the DISC1 mutation is either introduced in the control cell line, or rescued in the schizophrenia cell line, we ascribe most of these changes to a direct effect of the presence of the DISC1 mutation. Investigating the gene expression signatures downstream of the DISC1 kinase network, and mapping them on perturbagen signatures obtained from the Library of Integrated Network-based Cellular Signatures (LINCS) database, allowed us to propose novel drug targets able to reverse the DISC1 kinase dysregulation gene expression signature. Altogether, our findings provide new insight into abnormalities of kinase networks in schizophrenia and suggest possible targets for disease intervention.

Rapid progress in proteomics and large-scale profiling of biological systems at the protein level necessitates the continued development of efficient computational tools for the analysis and interpretation of proteomics data. Here, we present the piNET server that facilitates integrated annotation, analysis and visualization of quantitative proteomics data, with emphasis on PTM networks and integration with the LINCS library of chemical and genetic perturbation signatures in order to provide further mechanistic and functional insights. The primary input for the server consists of a set of peptides or proteins, optionally with PTM sites, and their corresponding abundance values. Several interconnected workflows can be used to generate: (i) interactive graphs and tables providing comprehensive annotation and mapping between peptides and proteins with PTM sites; (ii) high resolution and interactive visualization for enzyme-substrate networks, including kinases and their phospho-peptide targets; (iii) mapping and visualization of LINCS signature connectivity for chemical inhibitors or genetic knockdown of enzymes upstream of their target PTM sites. piNET has been built using a modular Spring-Boot JAVA platform as a fast, versatile and easy to use tool. The Apache Lucene indexing is used for fast mapping of peptides into UniProt entries for the human, mouse and other commonly used model organism proteomes. PTM-centric network analyses combine PhosphoSitePlus, iPTMnet and SIGNOR databases of validated enzyme-substrate relationships, for kinase networks augmented by DeepPhos predictions and sequence-based mapping of PhosphoSitePlus consensus motifs. Concordant LINCS signatures are mapped using iLINCS. For each workflow, a RESTful API counterpart can be used to generate the results programmatically in the json format. The server is available at http://pinet-server.org, and it is free and open to all users without login requirement.

We have reported child anxiety sensitivity (Child Anxiety Sensitivity Index [CASI]) predicts chronic postsurgical pain (CPSP). Herein, we evaluated DNA methylation profiles to understand the gene-environment interactions underlying CPSP and CASI, to identify shared, enriched, genomic pathways. In 73 prospectively recruited adolescents undergoing spine fusion, preoperative CASI and pain data over 12 months after surgery were collected. DNA from the peripheral blood of evaluable subjects with (n = 16) and without CPSP (n = 40) were analyzed using MethylationEPIC arrays. We identified 637 and 2,445 differentially DNA methylated positions (DMPs) associated with CPSP and CASI, respectively (P ≤ .05). Ingenuity pathway analysis of 39 genes with DMPs for both CPSP and CASI revealed enrichment of several canonical pathways, including GABA receptor (P = .00016 for CPSP; P =.0008 for CASI) and dopamine-DARPP32 feedback in cyclic adenosine monophosphate (P = .004 for CPSP and P =.00003 for CASI) signaling. Gene-gene interaction network enrichment analysis revealed participation of pathways in cell signaling, molecular transport, metabolism, and neurologic diseases (P < 10-8). Bioinformatic approaches to identify histone marks and transcription factor (TF) binding events underlying DMPs, showed their location in active regulatory regions in pain pathway relevant brain cells. Using Enrichr/Pinet enrichment and Library of Integrated Network-Based Cellular Signatures knockdown signatures, we identified TFs regulating genes with DMPs in association with CPSP and CASI. In conclusion, we identified epigenetically enriched pathways associated with CPSP and anxiety sensitivity in children undergoing surgery. Our findings support GABA hypofunction and the roles of the dopamine-DARPP32 pathway in emotion/reward and pain. This pilot study provides new epigenetic insights into the pathophysiology of CPSP and a basis for future studies in biomarker development and targetable interventions. PERSPECTIVE: Differential DNA methylation in regulatory genomic regions enriching shared neural pathways were associated with CPSP and CASI in adolescents undergoing spine surgery. Our findings support GABA hypofunction and the roles of the dopamine-DARPP32 pathway in emotion/reward contributing to behavioral maintenance of pain 10 to 12 months after surgery.

We report the synthesis and preliminary characterization of IODVA1, a potent small molecule that is active in xenograft mouse models of Ras-driven lung and breast cancers. In an effort to inhibit oncogenic Ras signaling, we combined in silico screening with inhibition of proliferation and colony formation of Ras-driven cells. NSC124205 fulfilled all criteria. HPLC analysis revealed that NSC124205 was a mixture of at least three compounds, from which IODVA1 was determined to be the active component. IODVA1 decreased 2D proliferation, 3D spheroid formation, spreading and lamellipodia formation in breast cancer cells. IODVA1 significantly impaired xenograft tumor growth of Ras-driven cancer cells with no observable toxicity. Immuno-histochemistry analysis of tumor sections suggests that cell death occurs by increased apoptosis. Our data suggest that IODVA1 targets a node used by Ras to regulate cell survival, spreading, and movement including lamellipodia formation. Therefore, IODVA1 holds promise as an anti-tumor therapeutic agent.

355 Background: Management of patients with localized or locally advanced renal cell cancer (RCC) involves surgical resection. However, 20-40% of all localized kidney cancer patients experience a recurrence. Adjuvant treatment in high-risk kidney cancer has not proven to successfully improve overall survival (OS) and the only drug approved so far in this setting is vascular endothelial growth factor (VEGF) inhibitor, sunitinib. As multiple trials are evaluating drugs including immune checkpoint inhibitors in patients with high risk localized clear cell RCC, there is an urgent need to identify biomarkers to help with therapeutic decisions as well as for risk stratification. Here we present analysis pertaining to genes involved in metabolic reprogramming in kidney cancer. Methods: A deep transcriptomic analysis of patients with stage III ccRCC in the TCGA KIRC Firehose Legacy cohort was undertaken. Caucasian males with stage III ccRCC in the TCGA cohort for whom recurrence data was available with a minimum follow-up of 2 years were identified for the analysis. Expression profiles of differentially expressed genes were clustered using the Bayesian infinite mixture model and samples clustered using average linkage hierarchical clustering based on pairwise Pearson’s correlations as the measure of similarity. Enrichment analysis of up- and down-regulated genes was performed using logistic regression. R package was used to generate Kaplan-Meier curves and assess statistical significance of differences in overall and disease-free survival using log-rank test. Results: The cohort evaluated for this study included Caucasian male patients that remained disease free for at least 24 months after surgery (n = 22) and patients whose cancer recurred within 24 months (n = 20). We identified metabolic genes, encoding subunits of mitochondrial electron transport chain as well as malate aspartate shuttle (MAS), where loss of coordinated co-expression between these genes identified patients at risk of recurrence after surgery. The gene signature stratified the 42 patients into three subtypes: significantly enriched for a) recurrence (subtype 1), for b) disease free status (subtype 2) and b) intermediate (subtype 3). Work is underway to combine the use of individual gene expression and ratios of gene expression within the signature to optimize prognostic biomarkers for localized ccRCC. Conclusions: In this analysis, we have identified a novel metabolic gene signature which can help identify patients with localized ccRCC at high risk of recurrence after surgery to guide aggressive therapy. These findings require further validation in tumors collected from patients with stage III ccRCC.

Human noroviruses (huNoVs), which cause epidemic acute gastroenteritis, recognize histo-blood group antigens (HBGAs) as host attachment factors affecting host susceptibility. HuNoVs are genetically diverse, containing at least 31 genotypes in the two major genogroups (genogroup I [GI] and GII). Three GII genotypes, GII genotype 17 (GII.17), GII.13, and GII.21, form a unique genetic lineage, in which the GII.17 genotype retains the conventional GII HBGA binding site (HBS), while the GII.13/21 genotypes acquire a completely new HBS. To understand the molecular bases behind these evolutionary changes, we solved the crystal structures of the HBGA binding protruding domains of (i) an early GII.17 variant (the 1978 variant) that does not bind or binds weakly to HBGAs, (ii) the new GII.17 variant (the 2014/15 variant) that binds A/B/H antigens strongly via an optimized GII HBS, and (iii) a GII.13 variant (the 2010 variant) that binds the Lewis a (Le(a)) antigen via the new HBS. These serial, high-resolution structural data enable a comprehensive structural comparison to understand the evolutionary changes of the GII.17/13/21 lineage, including the emergence of the new HBS of the GII.13/21 sublineage and the possible HBS optimization of the recent GII.17 variant for an enhanced HBGA binding ability. Our study elucidates the structural adaptations of the GII.17/13/21 lineage through distinct evolutionary paths, which may allow a theory explaining huNoV adaptations and evolutions to be put forward. IMPORTANCE Our understanding of the molecular bases behind the interplays between human noroviruses and their host glycan ligands, as well as their evolutionary changes over time with alterations in their host ligand binding capability and host susceptibility, remains limited. By solving the crystal structures of the glycan ligand binding protruding (P) domains with or without glycan ligands of three representative noroviruses of the GII.17/13/21 genetic lineage, we elucidated the molecular bases of the human norovirus-glycan interactions of this special genetic lineage. We present solid evidence on how noroviruses of this genetic lineage evolved via different evolutionary paths to (i) optimize their glycan binding site for higher glycan binding function and (ii) acquire a completely new glycan binding site for new ligands. Our data shed light on the mechanism of the structural adaptations of human noroviruses through different evolutionary paths, facilitating our understanding of human norovirus adaptations, evolutions, and epidemiology.

We utilized a cell-level approach to examine glycolytic pathways in the DLPFC of subjects with schizophrenia (n=16) and control (n=16) subjects and found decreased mRNA expression of glycolytic enzymes in pyramidal neurons, but not astrocytes. To replicate these novel bioenergetic findings, we probed independent datasets for bioenergetic targets and found similar abnormalities. Next, we used a novel strategy to build a schizophrenia bioenergetic profile by a tailored application of the Library of Integrated Network-Based Cellular Signatures data portal (iLINCS) and investigated connected cellular pathways, kinases, and transcription factors using Enrichr. Finally, with the goal of identifying drugs capable of “reversing” the bioenergetic schizophrenia signature, we performed a connectivity analysis with iLINCS and identified peroxisome proliferator-activated receptor (PPAR) agonists as promising therapeutic targets. We administered a PPAR agonist to the GluN1 knockdown model of schizophrenia and found it improved long-term memory. Taken together, our findings suggest that tailored bioinformatics approaches, coupled with the LINCS library of transcriptional signatures of chemical and genetic perturbagens may be employed to identify novel treatment strategies for schizophrenia and related diseases.