Ruth Huttenhain

All Publications

• Profiling the proximal proteome of the activated μ-opioid receptor. Nature chemical biology Polacco, B. J., Lobingier, B. T., Blythe, E. E., Abreu, N., Khare, P., Howard, M. K., Gonzalez-Hernandez, A. J., Xu, J., Li, Q., Novy, B., Naing, Z. Z., Shoichet, B. K., Coyote-Maestas, W., Levitz, J., Krogan, N. J., Von Zastrow, M., Hüttenhain, R. 2024

  Abstract

  The μ-opioid receptor (μOR) represents an important target of therapeutic and abused drugs. So far, most understanding of μOR activity has focused on a subset of known signal transducers and regulatory molecules. Yet μOR signaling is coordinated by additional proteins in the interaction network of the activated receptor, which have largely remained invisible given the lack of technologies to interrogate these networks systematically. Here we describe a proteomics and computational approach to map the proximal proteome of the activated μOR and to extract subcellular location, trafficking and functional partners of G-protein-coupled receptor (GPCR) activity. We demonstrate that distinct opioid agonists exert differences in the μOR proximal proteome mediated by endocytosis and endosomal sorting. Moreover, we identify two new μOR network components, EYA4 and KCTD12, which are recruited on the basis of receptor-triggered G-protein activation and might form a previously unrecognized buffering system for G-protein activity broadly modulating cellular GPCR signaling.

  View details for DOI 10.1038/s41589-024-01588-3

  View details for PubMedID 38528119

  View details for PubMedCentralID 6428583

• A foundational atlas of autism protein interactions reveals molecular convergence. bioRxiv : the preprint server for biology Wang, B., Vartak, R., Zaltsman, Y., Naing, Z. Z., Hennick, K. M., Polacco, B. J., Bashir, A., Eckhardt, M., Bouhaddou, M., Xu, J., Sun, N., Lasser, M. C., Zhou, Y., McKetney, J., Guiley, K. Z., Chan, U., Kaye, J. A., Chadha, N., Cakir, M., Gordon, M., Khare, P., Drake, S., Drury, V., Burke, D. F., Gonzalez, S., Alkhairy, S., Thomas, R., Lam, S., Morris, M., Bader, E., Seyler, M., Baum, T., Krasnoff, R., Wang, S., Pham, P., Arbalaez, J., Pratt, D., Chag, S., Mahmood, N., Rolland, T., Bourgeron, T., Finkbeiner, S., Swaney, D. L., Bandyopadhay, S., Ideker, T., Beltrao, P., Willsey, H. R., Obernier, K., Nowakowski, T. J., Hüttenhain, R., State, M. W., Willsey, A. J., Krogan, N. J. 2023

  Abstract

  Translating high-confidence (hc) autism spectrum disorder (ASD) genes into viable treatment targets remains elusive. We constructed a foundational protein-protein interaction (PPI) network in HEK293T cells involving 100 hcASD risk genes, revealing over 1,800 PPIs (87% novel). Interactors, expressed in the human brain and enriched for ASD but not schizophrenia genetic risk, converged on protein complexes involved in neurogenesis, tubulin biology, transcriptional regulation, and chromatin modification. A PPI map of 54 patient-derived missense variants identified differential physical interactions, and we leveraged AlphaFold-Multimer predictions to prioritize direct PPIs and specific variants for interrogation in Xenopus tropicalis and human forebrain organoids. A mutation in the transcription factor FOXP1 led to reconfiguration of DNA binding sites and altered development of deep cortical layer neurons in forebrain organoids. This work offers new insights into molecular mechanisms underlying ASD and describes a powerful platform to develop and test therapeutic strategies for many genetically-defined conditions.

  View details for DOI 10.1101/2023.12.03.569805

  View details for PubMedID 38076945

  View details for PubMedCentralID PMC10705567

• The multi-lineage transcription factor ISL1 controls cardiomyocyte cell fate through interaction with NKX2.5. Stem cell reports Maven, B. E., Gifford, C. A., Weilert, M., Gonzalez-Teran, B., Hüttenhain, R., Pelonero, A., Ivey, K. N., Samse-Knapp, K., Kwong, W., Gordon, D., McGregor, M., Nishino, T., Okorie, E., Rossman, S., Costa, M. W., Krogan, N. J., Zeitlinger, J., Srivastava, D. 2023

  Abstract

  Congenital heart disease often arises from perturbations of transcription factors (TFs) that guide cardiac development. ISLET1 (ISL1) is a TF that influences early cardiac cell fate, as well as differentiation of other cell types including motor neuron progenitors (MNPs) and pancreatic islet cells. While lineage specificity of ISL1 function is likely achieved through combinatorial interactions, its essential cardiac interacting partners are unknown. By assaying ISL1 genomic occupancy in human induced pluripotent stem cell-derived cardiac progenitors (CPs) or MNPs and leveraging the deep learning approach BPNet, we identified motifs of other TFs that predicted ISL1 occupancy in each lineage, with NKX2.5 and GATA motifs being most closely associated to ISL1 in CPs. Experimentally, nearly two-thirds of ISL1-bound loci were co-occupied by NKX2.5 and/or GATA4. Removal of NKX2.5 from CPs led to widespread ISL1 redistribution, and overexpression of NKX2.5 in MNPs led to ISL1 occupancy of CP-specific loci. These results reveal how ISL1 guides lineage choices through a combinatorial code that dictates genomic occupancy and transcription.

  View details for DOI 10.1016/j.stemcr.2023.09.014

  View details for PubMedID 37863045

• SARS-CoV-2 variants evolve convergent strategies to remodel the host response. Cell Bouhaddou, M., Reuschl, A., Polacco, B. J., Thorne, L. G., Ummadi, M. R., Ye, C., Rosales, R., Pelin, A., Batra, J., Jang, G. M., Xu, J., Moen, J. M., Richards, A. L., Zhou, Y., Harjai, B., Stevenson, E., Rojc, A., Ragazzini, R., Whelan, M. V., Furnon, W., De Lorenzo, G., Cowton, V., Syed, A. M., Ciling, A., Deutsch, N., Pirak, D., Dowgier, G., Mesner, D., Turner, J. L., McGovern, B. L., Rodriguez, M. L., Leiva-Rebollo, R., Dunham, A. S., Zhong, X., Eckhardt, M., Fossati, A., Liotta, N. F., Kehrer, T., Cupic, A., Rutkowska, M., Mena, I., Aslam, S., Hoffert, A., Foussard, H., Olwal, C. O., Huang, W., Zwaka, T., Pham, J., Lyons, M., Donohue, L., Griffin, A., Nugent, R., Holden, K., Deans, R., Aviles, P., Lopez-Martin, J. A., Jimeno, J. M., Obernier, K., Fabius, J. M., Soucheray, M., Huttenhain, R., Jungreis, I., Kellis, M., Echeverria, I., Verba, K., Bonfanti, P., Beltrao, P., Sharan, R., Doudna, J. A., Martinez-Sobrido, L., Patel, A. H., Palmarini, M., Miorin, L., White, K., Swaney, D. L., Garcia-Sastre, A., Jolly, C., Zuliani-Alvarez, L., Towers, G. J., Krogan, N. J. 2023

  Abstract

  SARS-CoV-2 variants of concern (VOCs) emerged during the COVID-19 pandemic. Here, we used unbiased systems approaches to study the host-selective forces driving VOC evolution. We discovered that VOCs evolved convergent strategies to remodel the host by modulating viral RNA and protein levels, altering viral and host protein phosphorylation, and rewiring virus-host protein-protein interactions. Integrative computational analyses revealed that although Alpha, Beta, Gamma, and Delta ultimately converged to suppress interferon-stimulated genes (ISGs), Omicron BA.1 did not. ISG suppression correlated with the expression of viral innate immune antagonist proteins, including Orf6, N, and Orf9b, which we mapped to specific mutations. Later Omicron subvariants BA.4 and BA.5 more potently suppressed innate immunity than early subvariant BA.1, which correlated with Orf6 levels, although muted in BA.4 by a mutation that disrupts the Orf6-nuclear pore interaction. Our findings suggest that SARS-CoV-2 convergent evolution overcame human adaptive and innate immune barriers, laying the groundwork to tackle future pandemics.

  View details for DOI 10.1016/j.cell.2023.08.026

  View details for PubMedID 37738970

• An automated proximity proteomics pipeline for subcellular proteome and protein interaction mapping. bioRxiv : the preprint server for biology Zhong, X., Li, Q., Polacco, B. J., Patil, T., DiBerto, J. F., Vartak, R., Xu, J., Marley, A., Foussard, H., Roth, B. L., Eckhardt, M., Zastrow, M. V., Krogan, N. J., Huttenhain, R. 2023

  Abstract

  Proximity labeling (PL) coupled with mass spectrometry has emerged as a powerful technique to map proximal protein interactions in living cells. Large-scale sample processing for proximity proteomics necessitates a high-throughput workflow to reduce hands-on time and increase quantitative reproducibility. To address this issue, we developed a scalable and automated PL pipeline, including generation and characterization of monoclonal cell lines, automated enrichment of biotinylated proteins in a 96-well format, and optimization of the quantitative mass spectrometry (MS) acquisition method. Combined with data-independent acquisition (DIA) MS, our pipeline outperforms manual enrichment and data-dependent acquisition (DDA) MS regarding reproducibility of protein identification and quantification. We apply the pipeline to map subcellular proteomes for endosomes, late endosomes/lysosomes, the Golgi apparatus, and the plasma membrane. Moreover, using serotonin receptor (5HT 2A ) as a model, we investigated agonist-induced dynamics in protein-protein interactions. Importantly, the approach presented here is universally applicable for PL proteomics using all biotinylation-based PL enzymes, increasing both throughput and reproducibility of standard protocols.

  View details for DOI 10.1101/2023.04.11.536358

  View details for PubMedID 37090610

• Structure-function analysis of enterovirus protease 2A in complex with its essential host factor SETD3. Nature communications Peters, C. E., Schulze-Gahmen, U., Eckhardt, M., Jang, G. M., Xu, J., Pulido, E. H., Bardine, C., Craik, C. S., Ott, M., Gozani, O., Verba, K. A., Hüttenhain, R., Carette, J. E., Krogan, N. J. 2022; 13 (1): 5282

  Abstract

  Enteroviruses cause a number of medically relevant and widespread human diseases with no approved antiviral therapies currently available. Host-directed therapies present an enticing option for this diverse genus of viruses. We have previously identified the actin histidine methyltransferase SETD3 as a critical host factor physically interacting with the viral protease 2A. Here, we report the 3.5 Å cryo-EM structure of SETD3 interacting with coxsackievirus B3 2A at two distinct interfaces, including the substrate-binding surface within the SET domain. Structure-function analysis revealed that mutations of key residues in the SET domain resulted in severely reduced binding to 2A and complete protection from enteroviral infection. Our findings provide insight into the molecular basis of the SETD3-2A interaction and a framework for the rational design of host-directed therapeutics against enteroviruses.

  View details for DOI 10.1038/s41467-022-32758-3

  View details for PubMedID 36075902

• Signaling snapshots of a serotonin receptor activated by the prototypical psychedelic LSD. Neuron Cao, C., Barros-Alvarez, X., Zhang, S., Kim, K., Damgen, M. A., Panova, O., Suomivuori, C., Fay, J. F., Zhong, X., Krumm, B. E., Gumpper, R. H., Seven, A. B., Robertson, M. J., Krogan, N. J., Huttenhain, R., Nichols, D. E., Dror, R. O., Skiniotis, G., Roth, B. L. 2022

  Abstract

  Serotonin (5-hydroxytryptamine [5-HT]) 5-HT2-family receptors represent essential targets for lysergic acid diethylamide (LSD) and all other psychedelic drugs. Although the primary psychedelic drug effects are mediated by the 5-HT2A serotonin receptor (HTR2A), the 5-HT2B serotonin receptor (HTR2B) has been used as a model receptor to study the activation mechanisms of psychedelic drugs due to its high expression and similarity to HTR2A. In this study, we determined the cryo-EM structures of LSD-bound HTR2B in the transducer-free, Gq-protein-coupled, and beta-arrestin-1-coupled states. These structures provide distinct signaling snapshots of LSD's action, ranging from the transducer-free, partially active state to the transducer-coupled, fully active states. Insights from this study will both provide comprehensive molecular insights into the signaling mechanisms of the prototypical psychedelic LSD and accelerate the discovery of novel psychedelic drugs.

  View details for DOI 10.1016/j.neuron.2022.08.006

  View details for PubMedID 36087581

• Transcription Factor GATA4 Regulates Cell Type-Specific Splicing Through Direct Interaction With RNA in Human Induced Pluripotent Stem Cell-Derived Cardiac Progenitors. Circulation Zhu, L., Choudhary, K., Gonzalez-Teran, B., Ang, Y., Thomas, R., Stone, N. R., Liu, L., Zhou, P., Zhu, C., Ruan, H., Huang, Y., Jin, S., Pelonero, A., Koback, F., Padmanabhan, A., Sadagopan, N., Hsu, A., Costa, M. W., Gifford, C. A., van Bemmel, J., Huttenhain, R., Vedantham, V., Conklin, B. R., Black, B. L., Bruneau, B. G., Steinmetz, L., Krogan, N. J., Pollard, K. S., Srivastava, D. 2022: CIRCULATIONAHA121057620

  Abstract

  BACKGROUND: GATA4 (GATA-binding protein 4), a zinc finger-containing, DNA-binding transcription factor, is essential for normal cardiac development and homeostasis in mice and humans, and mutations in this gene have been reported in human heart defects. Defects in alternative splicing are associated with many heart diseases, yet relatively little is known about how cell type- or cell state-specific alternative splicing is achieved in the heart. Here, we show that GATA4 regulates cell type-specific splicing through direct interaction with RNA and the spliceosome in human induced pluripotent stem cell-derived cardiac progenitors.METHODS: We leveraged a combination of unbiased approaches including affinity purification of GATA4 and mass spectrometry, enhanced cross-linking with immunoprecipitation, electrophoretic mobility shift assays, in vitro splicing assays, and unbiased transcriptomic analysis to uncover GATA4's novel function as a splicing regulator in human induced pluripotent stem cell-derived cardiac progenitors.RESULTS: We found that GATA4 interacts with many members of the spliceosome complex in human induced pluripotent stem cell-derived cardiac progenitors. Enhanced cross-linking with immunoprecipitation demonstrated that GATA4 also directly binds to a large number of mRNAs through defined RNA motifs in a sequence-specific manner. In vitro splicing assays indicated that GATA4 regulates alternative splicing through direct RNA binding, resulting in functionally distinct protein products. Correspondingly, knockdown of GATA4 in human induced pluripotent stem cell-derived cardiac progenitors resulted in differential alternative splicing of genes involved in cytoskeleton organization and calcium ion import, with functional consequences associated with the protein isoforms.CONCLUSIONS: This study shows that in addition to its well described transcriptional function, GATA4 interacts with members of the spliceosome complex and regulates cell type-specific alternative splicing via sequence-specific interactions with RNA. Several genes that have splicing regulated by GATA4 have functional consequences and many are associated with dilated cardiomyopathy, suggesting a novel role for GATA4 in achieving the necessary cardiac proteome in normal and stress-responsive conditions.

  View details for DOI 10.1161/CIRCULATIONAHA.121.057620

  View details for PubMedID 35938400

• Global post-translational modification profiling of HIV-1-infected cells reveals mechanisms of host cellular pathway remodeling CELL REPORTS Johnson, J. R., Crosby, D. C., Hultquist, J. F., Kurland, A. P., Adhikary, P., Li, D., Marlett, J., Swann, J., Huttenhain, R., Verschueren, E., Johnson, T. L., Newton, B. W., Shales, M., Simon, V. A., Beltrao, P., Frankel, A. D., Marson, A., Cox, J. S., Fregoso, O., Young, J. T., Krogan, N. J. 2022; 39 (2): 110690

  Abstract

  Viruses must effectively remodel host cellular pathways to replicate and evade immune defenses, and they must do so with limited genomic coding capacity. Targeting post-translational modification (PTM) pathways provides a mechanism by which viruses can broadly and rapidly transform a hostile host environment into a hospitable one. We use mass spectrometry-based proteomics to quantify changes in protein abundance and two PTM types-phosphorylation and ubiquitination-in response to HIV-1 infection with viruses harboring targeted deletions of a subset of HIV-1 genes. PTM analysis reveals a requirement for Aurora kinase activity in HIV-1 infection and identified putative substrates of a phosphatase that is degraded during infection. Finally, we demonstrate that the HIV-1 Vpr protein inhibits histone H1 ubiquitination, leading to defects in DNA repair.

  View details for DOI 10.1016/j.celrep.2022.110690

  View details for Web of Science ID 000792280300007

  View details for PubMedID 35417684

  View details for PubMedCentralID PMC9429972

• Tau interactome maps synaptic and mitochondrial processes associated with neurodegeneration. Cell Tracy, T. E., Madero-Pérez, J., Swaney, D. L., Chang, T. S., Moritz, M., Konrad, C., Ward, M. E., Stevenson, E., Hüttenhain, R., Kauwe, G., Mercedes, M., Sweetland-Martin, L., Chen, X., Mok, S. A., Wong, M. Y., Telpoukhovskaia, M., Min, S. W., Wang, C., Sohn, P. D., Martin, J., Zhou, Y., Luo, W., Trojanowski, J. Q., Lee, V. M., Gong, S., Manfredi, G., Coppola, G., Krogan, N. J., Geschwind, D. H., Gan, L. 2022; 185 (4): 712-728.e14

  Abstract

  Tau (MAPT) drives neuronal dysfunction in Alzheimer disease (AD) and other tauopathies. To dissect the underlying mechanisms, we combined an engineered ascorbic acid peroxidase (APEX) approach with quantitative affinity purification mass spectrometry (AP-MS) followed by proximity ligation assay (PLA) to characterize Tau interactomes modified by neuronal activity and mutations that cause frontotemporal dementia (FTD) in human induced pluripotent stem cell (iPSC)-derived neurons. We established interactions of Tau with presynaptic vesicle proteins during activity-dependent Tau secretion and mapped the Tau-binding sites to the cytosolic domains of integral synaptic vesicle proteins. We showed that FTD mutations impair bioenergetics and markedly diminished Tau's interaction with mitochondria proteins, which were downregulated in AD brains of multiple cohorts and correlated with disease severity. These multimodal and dynamic Tau interactomes with exquisite spatial resolution shed light on Tau's role in neuronal function and disease and highlight potential therapeutic targets to block Tau-mediated pathogenesis.

  View details for DOI 10.1016/j.cell.2021.12.041

  View details for PubMedID 35063084

  View details for PubMedCentralID PMC8857049

• Brahma safeguards canalization of cardiac mesoderm differentiation NATURE Hota, S. K., Rao, K. S., Blair, A. P., Khalilimeybodi, A., Hu, K. M., Thomas, R., So, K., Kameswaran, V., Xu, J., Polacco, B. J., Desai, R., Chatterjee, N., Hsu, A., Muncie, J. M., Blotnick, A. M., Winchester, S. B., Weinberger, L. S., Huttenhain, R., Kathiriya, I. S., Krogan, N. J., Saucerman, J. J., Bruneau, B. G. 2022; 602 (7895): 129-+

  Abstract

  Differentiation proceeds along a continuum of increasingly fate-restricted intermediates, referred to as canalization1,2. Canalization is essential for stabilizing cell fate, but the mechanisms that underlie robust canalization are unclear. Here we show that the BRG1/BRM-associated factor (BAF) chromatin-remodelling complex ATPase gene Brm safeguards cell identity during directed cardiogenesis of mouse embryonic stem cells. Despite the establishment of a well-differentiated precardiac mesoderm, Brm-/- cells predominantly became neural precursors, violating germ layer assignment. Trajectory inference showed a sudden acquisition of a non-mesodermal identity in Brm-/- cells. Mechanistically, the loss of Brm prevented de novo accessibility of primed cardiac enhancers while increasing the expression of neurogenic factor POU3F1, preventing the binding of the neural suppressor REST and shifting the composition of BRG1 complexes. The identity switch caused by the Brm mutation was overcome by increasing BMP4 levels during mesoderm induction. Mathematical modelling supports these observations and demonstrates that Brm deletion affects cell fate trajectory by modifying saddle-node bifurcations2. In the mouse embryo, Brm deletion exacerbated mesoderm-deleted Brg1-mutant phenotypes, severely compromising cardiogenesis, and reveals an in vivo role for Brm. Our results show that Brm is a compensable safeguard of the fidelity of mesoderm chromatin states, and support a model in which developmental canalization is not a rigid irreversible path, but a highly plastic trajectory.

  View details for DOI 10.1038/s41586-021-04336-y

  View details for Web of Science ID 000750429600031

  View details for PubMedID 35082446

  View details for PubMedCentralID PMC9196993

• Transcription factor protein interactomes reveal genetic determinants in heart disease. Cell Gonzalez-Teran, B., Pittman, M., Felix, F., Thomas, R., Richmond-Buccola, D., Hüttenhain, R., Choudhary, K., Moroni, E., Costa, M. W., Huang, Y., Padmanabhan, A., Alexanian, M., Lee, C. Y., Maven, B. E., Samse-Knapp, K., Morton, S. U., McGregor, M., Gifford, C. A., Seidman, J. G., Seidman, C. E., Gelb, B. D., Colombo, G., Conklin, B. R., Black, B. L., Bruneau, B. G., Krogan, N. J., Pollard, K. S., Srivastava, D. 2022

  Abstract

  Congenital heart disease (CHD) is present in 1% of live births, yet identification of causal mutations remains challenging. We hypothesized that genetic determinants for CHDs may lie in the protein interactomes of transcription factors whose mutations cause CHDs. Defining the interactomes of two transcription factors haplo-insufficient in CHD, GATA4 and TBX5, within human cardiac progenitors, and integrating the results with nearly 9,000 exomes from proband-parent trios revealed an enrichment of de novo missense variants associated with CHD within the interactomes. Scoring variants of interactome members based on residue, gene, and proband features identified likely CHD-causing genes, including the epigenetic reader GLYR1. GLYR1 and GATA4 widely co-occupied and co-activated cardiac developmental genes, and the identified GLYR1 missense variant disrupted interaction with GATA4, impairing in vitro and in vivo function in mice. This integrative proteomic and genetic approach provides a framework for prioritizing and interrogating genetic variants in heart disease.

  View details for DOI 10.1016/j.cell.2022.01.021

  View details for PubMedID 35182466

• Proteomic Approaches to Study SARS-CoV-2 Biology and COVID-19 Pathology JOURNAL OF PROTEOME RESEARCH Haas, P., Muralidharan, M., Krogan, N. J., Kaake, R. M., Huttenhain, R. 2021; 20 (2): 1133-1152

  Abstract

  The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), was declared a pandemic infection in March 2020. As of December 2020, two COVID-19 vaccines have been authorized for emergency use by the U.S. Food and Drug Administration, but there are no effective drugs to treat COVID-19, and pandemic mitigation efforts like physical distancing have had acute social and economic consequences. In this perspective, we discuss how the proteomic research community can leverage technologies and expertise to address the pandemic by investigating four key areas of study in SARS-CoV-2 biology. Specifically, we discuss how (1) mass spectrometry-based structural techniques can overcome limitations and complement traditional structural approaches to inform the dynamic structure of SARS-CoV-2 proteins, complexes, and virions; (2) virus-host protein-protein interaction mapping can identify the cellular machinery required for SARS-CoV-2 replication; (3) global protein abundance and post-translational modification profiling can characterize signaling pathways that are rewired during infection; and (4) proteomic technologies can aid in biomarker identification, diagnostics, and drug development in order to monitor COVID-19 pathology and investigate treatment strategies. Systems-level high-throughput capabilities of proteomic technologies can yield important insights into SARS-CoV-2 biology that are urgently needed during the pandemic, and more broadly, can inform coronavirus virology and host biology.

  View details for DOI 10.1021/acs.jproteome.0c00764

  View details for Web of Science ID 000618540700003

  View details for PubMedID 33464917

  View details for PubMedCentralID PMC7839417

• Genetic interaction mapping informs integrative structure determination of protein complexes SCIENCE Braberg, H., Echeverria, I., Bohn, S., Cimermancic, P., Shiver, A., Alexander, R., Xu, J., Shales, M., Dronamraju, R., Jiang, S., Dwivedi, G., Bogdanoff, D., Chaung, K. K., Huttenhain, R., Wang, S., Mavor, D., Pellarin, R., Schneidman, D., Bader, J. S., Fraser, J. S., Morris, J., Haber, J. E., Strahl, B. D., Gross, C. A., Dai, J., Boeke, J. D., Sali, A., Krogan, N. J. 2020; 370 (6522): 1294-+

  Abstract

  Determining structures of protein complexes is crucial for understanding cellular functions. Here, we describe an integrative structure determination approach that relies on in vivo measurements of genetic interactions. We construct phenotypic profiles for point mutations crossed against gene deletions or exposed to environmental perturbations, followed by converting similarities between two profiles into an upper bound on the distance between the mutated residues. We determine the structure of the yeast histone H3-H4 complex based on ~500,000 genetic interactions of 350 mutants. We then apply the method to subunits Rpb1-Rpb2 of yeast RNA polymerase II and subunits RpoB-RpoC of bacterial RNA polymerase. The accuracy is comparable to that based on chemical cross-links; using restraints from both genetic interactions and cross-links further improves model accuracy and precision. The approach provides an efficient means to augment integrative structure determination with in vivo observations.

  View details for DOI 10.1126/science.aaz4910

  View details for Web of Science ID 000597271300037

  View details for PubMedID 33303586

• Comparative host-coronavirus protein interaction networks reveal pan-viral disease mechanisms SCIENCE Gordon, D. E., Hiatt, J., Bouhaddou, M., Rezelj, V. V., Ulferts, S., Braberg, H., Jureka, A. S., Obernier, K., Guo, J. Z., Batra, J., Kaake, R. M., Weckstein, A. R., Owens, T. W., Gupta, M., Pourmal, S., Titus, E. W., Cakir, M., Soucheray, M., McGregor, M., Cakir, Z., Jang, G., O'Meara, M. J., Tummino, T. A., Zhang, Z., Foussard, H., Rojc, A., Zhou, Y., Kuchenov, D., Huttenhain, R., Xu, J., Eckhardt, M., Swaney, D. L., Fabius, J. M., Ummadi, M., Tutuncuoglu, B., Rathore, U., Modak, M., Haas, P., Haas, K. M., Naing, Z., Pulido, E. H., Shi, Y., Barrio-Hernandez, I., Memon, D., Petsalaki, E., Dunham, A., Marrero, M., Burke, D., Koh, C., Vallet, T., Silvas, J. A., Azumaya, C. M., Billesbolle, C., Brilot, A. F., Campbell, M. G., Diallo, A., Dickinson, M., Diwanji, D., Herrera, N., Hoppe, N., Kratochvil, H. T., Liu, Y., Merz, G. E., Moritz, M., Nguyen, H. C., Nowotny, C., Puchades, C., Rizo, A. N., Schulze-Gahmen, U., Smith, A. M., Sun, M., Young, I. D., Zhao, J., Asarnow, D., Biel, J., Bowen, A., Braxton, J. R., Chen, J., Chio, C. M., Chio, U., Deshpande, I., Doan, L., Faust, B., Flores, S., Jin, M., Kim, K., Lam, V. L., Li, F., Li, J., Li, Y., Li, Y., Liu, X., Lo, M., Lopez, K. E., Melo, A. A., Moss, F. R., Phuong Nguyen, Paulino, J., Pawar, K., Peters, J. K., Pospiech, T. H., Safari, M., Sangwan, S., Schaefer, K., Thomas, P., Thwin, A. C., Trenker, R., Tse, E., Tsui, T., Wang, F., Whitis, N., Yu, Z., Zhang, K., Zhang, Y., Zhou, F., Saltzberg, D., Hodder, A. J., Shun-Shion, A. S., Williams, D. M., White, K. M., Rosales, R., Kehrer, T., Miorin, L., Moreno, E., Patel, A. H., Rihn, S., Khalid, M. M., Vallejo-Gracia, A., Fozouni, P., Simoneau, C. R., Roth, T. L., Wu, D., Karim, M., Ghoussaini, M., Dunham, I., Berardi, F., Weigang, S., Chazal, M., Park, J., Logue, J., McGrath, M., Weston, S., Haupt, R., Hastie, C., Elliott, M., Brown, F., Burness, K. A., Reid, E., Dorward, M., Johnson, C., Wilkinson, S. G., Geyer, A., Giesel, D. M., Baillie, C., Raggett, S., Leech, H., Toth, R., Goodman, N., Keough, K. C., Lind, A. L., Klesh, R. J., Hemphill, K. R., Carlson-Stevermer, J., Oki, J., Holden, K., Maures, T., Pollard, K. S., Sali, A., Agard, D. A., Cheng, Y., Fraser, J. S., Frost, A., Jura, N., Kortemme, T., Manglik, A., Southworth, D. R., Stroud, R. M., Alessi, D. R., Davies, P., Frieman, M. B., Ideker, T., Abate, C., Jouvenet, N., Kochs, G., Shoichet, B., Ott, M., Palmarini, M., Shokat, K. M., Garcia-Sastre, A., Rassen, J. A., Grosse, R., Rosenberg, O. S., Verba, K. A., Basler, C. F., Vignuzzi, M., Peden, A. A., Beltrao, P., Krogan, N. J. 2020; 370 (6521): 1181-+

  Abstract

  The COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a grave threat to public health and the global economy. SARS-CoV-2 is closely related to the more lethal but less transmissible coronaviruses SARS-CoV-1 and Middle East respiratory syndrome coronavirus (MERS-CoV). Here, we have carried out comparative viral-human protein-protein interaction and viral protein localization analyses for all three viruses. Subsequent functional genetic screening identified host factors that functionally impinge on coronavirus proliferation, including Tom70, a mitochondrial chaperone protein that interacts with both SARS-CoV-1 and SARS-CoV-2 ORF9b, an interaction we structurally characterized using cryo-electron microscopy. Combining genetically validated host factors with both COVID-19 patient genetic data and medical billing records identified molecular mechanisms and potential drug treatments that merit further molecular and clinical study.

  View details for DOI 10.1126/science.abe9403

  View details for Web of Science ID 000596071300050

  View details for PubMedID 33060197

  View details for PubMedCentralID PMC7808408

• MassIVE.quant: a community resource of quantitative mass spectrometry-based proteomics datasets NATURE METHODS Choi, M., Carver, J., Chiva, C., Tzouros, M., Huang, T., Tsai, T., Pullman, B., Bernhardt, O. M., Huttenhain, R., Teo, G., Perez-Riverol, Y., Muntel, J., Mueller, M., Goetze, S., Pavlou, M., Verschueren, E., Wollscheid, B., Nesvizhskii, A. I., Reiter, L., Dunkley, T., Sabido, E., Bandeira, N., Vitek, O. 2020; 17 (10): 981-+

  Abstract

  MassIVE.quant is a repository infrastructure and data resource for reproducible quantitative mass spectrometry-based proteomics, which is compatible with all mass spectrometry data acquisition types and computational analysis tools. A branch structure enables MassIVE.quant to systematically store raw experimental data, metadata of the experimental design, scripts of the quantitative analysis workflow, intermediate input and output files, as well as alternative reanalyses of the same dataset.

  View details for DOI 10.1038/s41592-020-0955-0

  View details for Web of Science ID 000569908500001

  View details for PubMedID 32929271

  View details for PubMedCentralID PMC7541731

• The Global Phosphorylation Landscape of SARS-CoV-2 Infection. Cell Bouhaddou, M., Memon, D., Meyer, B., White, K. M., Rezelj, V. V., Correa Marrero, M., Polacco, B. J., Melnyk, J. E., Ulferts, S., Kaake, R. M., Batra, J., Richards, A. L., Stevenson, E., Gordon, D. E., Rojc, A., Obernier, K., Fabius, J. M., Soucheray, M., Miorin, L., Moreno, E., Koh, C., Tran, Q. D., Hardy, A., Robinot, R., Vallet, T., Nilsson-Payant, B. E., Hernandez-Armenta, C., Dunham, A., Weigang, S., Knerr, J., Modak, M., Quintero, D., Zhou, Y., Dugourd, A., Valdeolivas, A., Patil, T., Li, Q., Hüttenhain, R., Cakir, M., Muralidharan, M., Kim, M., Jang, G., Tutuncuoglu, B., Hiatt, J., Guo, J. Z., Xu, J., Bouhaddou, S., Mathy, C. J., Gaulton, A., Manners, E. J., Félix, E., Shi, Y., Goff, M., Lim, J. K., McBride, T., O'Neal, M. C., Cai, Y., Chang, J. C., Broadhurst, D. J., Klippsten, S., De Wit, E., Leach, A. R., Kortemme, T., Shoichet, B., Ott, M., Saez-Rodriguez, J., tenOever, B. R., Mullins, R. D., Fischer, E. R., Kochs, G., Grosse, R., García-Sastre, A., Vignuzzi, M., Johnson, J. R., Shokat, K. M., Swaney, D. L., Beltrao, P., Krogan, N. J. 2020; 182 (3): 685-712.e19

  Abstract

  The causative agent of the coronavirus disease 2019 (COVID-19) pandemic, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has infected millions and killed hundreds of thousands of people worldwide, highlighting an urgent need to develop antiviral therapies. Here we present a quantitative mass spectrometry-based phosphoproteomics survey of SARS-CoV-2 infection in Vero E6 cells, revealing dramatic rewiring of phosphorylation on host and viral proteins. SARS-CoV-2 infection promoted casein kinase II (CK2) and p38 MAPK activation, production of diverse cytokines, and shutdown of mitotic kinases, resulting in cell cycle arrest. Infection also stimulated a marked induction of CK2-containing filopodial protrusions possessing budding viral particles. Eighty-seven drugs and compounds were identified by mapping global phosphorylation profiles to dysregulated kinases and pathways. We found pharmacologic inhibition of the p38, CK2, CDK, AXL, and PIKFYVE kinases to possess antiviral efficacy, representing potential COVID-19 therapies.

  View details for DOI 10.1016/j.cell.2020.06.034

  View details for PubMedID 32645325

  View details for PubMedCentralID PMC7321036

• A systems approach to infectious disease NATURE REVIEWS GENETICS Eckhardt, M., Hultquist, J. F., Kaake, R. M., Huttenhain, R., Krogan, N. J. 2020; 21 (6): 339-354

  Abstract

  Ongoing social, political and ecological changes in the 21st century have placed more people at risk of life-threatening acute and chronic infections than ever before. The development of new diagnostic, prophylactic, therapeutic and curative strategies is critical to address this burden but is predicated on a detailed understanding of the immensely complex relationship between pathogens and their hosts. Traditional, reductionist approaches to investigate this dynamic often lack the scale and/or scope to faithfully model the dual and co-dependent nature of this relationship, limiting the success of translational efforts. With recent advances in large-scale, quantitative omics methods as well as in integrative analytical strategies, systems biology approaches for the study of infectious disease are quickly forming a new paradigm for how we understand and model host-pathogen relationships for translational applications. Here, we delineate a framework for a systems biology approach to infectious disease in three parts: discovery - the design, collection and analysis of omics data; representation - the iterative modelling, integration and visualization of complex data sets; and application - the interpretation and hypothesis-based inquiry towards translational outcomes.

  View details for DOI 10.1038/s41576-020-0212-5

  View details for Web of Science ID 000513384500001

  View details for PubMedID 32060427

  View details for PubMedCentralID PMC7839161

• A SARS-CoV-2 protein interaction map reveals targets for drug repurposing. Nature Gordon, D. E., Jang, G. M., Bouhaddou, M., Xu, J., Obernier, K., White, K. M., O'Meara, M. J., Rezelj, V. V., Guo, J. Z., Swaney, D. L., Tummino, T. A., Hüttenhain, R., Kaake, R. M., Richards, A. L., Tutuncuoglu, B., Foussard, H., Batra, J., Haas, K., Modak, M., Kim, M., Haas, P., Polacco, B. J., Braberg, H., Fabius, J. M., Eckhardt, M., Soucheray, M., Bennett, M. J., Cakir, M., McGregor, M. J., Li, Q., Meyer, B., Roesch, F., Vallet, T., Mac Kain, A., Miorin, L., Moreno, E., Naing, Z. Z., Zhou, Y., Peng, S., Shi, Y., Zhang, Z., Shen, W., Kirby, I. T., Melnyk, J. E., Chorba, J. S., Lou, K., Dai, S. A., Barrio-Hernandez, I., Memon, D., Hernandez-Armenta, C., Lyu, J., Mathy, C. J., Perica, T., Pilla, K. B., Ganesan, S. J., Saltzberg, D. J., Rakesh, R., Liu, X., Rosenthal, S. B., Calviello, L., Venkataramanan, S., Liboy-Lugo, J., Lin, Y., Huang, X. P., Liu, Y., Wankowicz, S. A., Bohn, M., Safari, M., Ugur, F. S., Koh, C., Savar, N. S., Tran, Q. D., Shengjuler, D., Fletcher, S. J., O'Neal, M. C., Cai, Y., Chang, J. C., Broadhurst, D. J., Klippsten, S., Sharp, P. P., Wenzell, N. A., Kuzuoglu-Ozturk, D., Wang, H. Y., Trenker, R., Young, J. M., Cavero, D. A., Hiatt, J., Roth, T. L., Rathore, U., Subramanian, A., Noack, J., Hubert, M., Stroud, R. M., Frankel, A. D., Rosenberg, O. S., Verba, K. A., Agard, D. A., Ott, M., Emerman, M., Jura, N., von Zastrow, M., Verdin, E., Ashworth, A., Schwartz, O., d'Enfert, C., Mukherjee, S., Jacobson, M., Malik, H. S., Fujimori, D. G., Ideker, T., Craik, C. S., Floor, S. N., Fraser, J. S., Gross, J. D., Sali, A., Roth, B. L., Ruggero, D., Taunton, J., Kortemme, T., Beltrao, P., Vignuzzi, M., García-Sastre, A., Shokat, K. M., Shoichet, B. K., Krogan, N. J. 2020; 583 (7816): 459-468

  Abstract

  A newly described coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is the causative agent of coronavirus disease 2019 (COVID-19), has infected over 2.3 million people, led to the death of more than 160,000 individuals and caused worldwide social and economic disruption1,2. There are no antiviral drugs with proven clinical efficacy for the treatment of COVID-19, nor are there any vaccines that prevent infection with SARS-CoV-2, and efforts to develop drugs and vaccines are hampered by the limited knowledge of the molecular details of how SARS-CoV-2 infects cells. Here we cloned, tagged and expressed 26 of the 29 SARS-CoV-2 proteins in human cells and identified the human proteins that physically associated with each of the SARS-CoV-2 proteins using affinity-purification mass spectrometry, identifying 332 high-confidence protein-protein interactions between SARS-CoV-2 and human proteins. Among these, we identify 66 druggable human proteins or host factors targeted by 69 compounds (of which, 29 drugs are approved by the US Food and Drug Administration, 12 are in clinical trials and 28 are preclinical compounds). We screened a subset of these in multiple viral assays and found two sets of pharmacological agents that displayed antiviral activity: inhibitors of mRNA translation and predicted regulators of the sigma-1 and sigma-2 receptors. Further studies of these host-factor-targeting agents, including their combination with drugs that directly target viral enzymes, could lead to a therapeutic regimen to treat COVID-19.

  View details for DOI 10.1038/s41586-020-2286-9

  View details for PubMedID 32353859

  View details for PubMedCentralID PMC7431030

• A Targeted Mass Spectrometry Strategy for Developing Proteomic Biomarkers: A Case Study of Epithelial Ovarian Cancer. Molecular & cellular proteomics : MCP Hüttenhain, R., Choi, M., Martin de la Fuente, L., Oehl, K., Chang, C. Y., Zimmermann, A. K., Malander, S., Olsson, H., Surinova, S., Clough, T., Heinzelmann-Schwarz, V., Wild, P. J., Dinulescu, D. M., Niméus, E., Vitek, O., Aebersold, R. 2019; 18 (9): 1836-1850

  Abstract

  Protein biomarkers for epithelial ovarian cancer are critical for the early detection of the cancer to improve patient prognosis and for the clinical management of the disease to monitor treatment response and to detect recurrences. Unfortunately, the discovery of protein biomarkers is hampered by the limited availability of reliable and sensitive assays needed for the reproducible quantification of proteins in complex biological matrices such as blood plasma. In recent years, targeted mass spectrometry, exemplified by selected reaction monitoring (SRM) has emerged as a method, capable of overcoming this limitation. Here, we present a comprehensive SRM-based strategy for developing plasma-based protein biomarkers for epithelial ovarian cancer and illustrate how the SRM platform, when combined with rigorous experimental design and statistical analysis, can result in detection of predictive analytes.Our biomarker development strategy first involved a discovery-driven proteomic effort to derive potential N-glycoprotein biomarker candidates for plasma-based detection of human ovarian cancer from a genetically engineered mouse model of endometrioid ovarian cancer, which accurately recapitulates the human disease. Next, 65 candidate markers selected from proteins of different abundance in the discovery dataset were reproducibly quantified with SRM assays across a large cohort of over 200 plasma samples from ovarian cancer patients and healthy controls. Finally, these measurements were used to derive a 5-protein signature for distinguishing individuals with epithelial ovarian cancer from healthy controls. The sensitivity of the candidate biomarker signature in combination with CA125 ELISA-based measurements currently used in clinic, exceeded that of CA125 ELISA-based measurements alone. The SRM-based strategy in this study is broadly applicable. It can be used in any study that requires accurate and reproducible quantification of selected proteins in a high-throughput and multiplexed fashion.

  View details for DOI 10.1074/mcp.RA118.001221

  View details for PubMedID 31289117

  View details for PubMedCentralID PMC6731088

• ARIH2 Is a Vif-Dependent Regulator of CUL5-Mediated APOBEC3G Degradation in HIV Infection CELL HOST & MICROBE Huttenhain, R., Xu, J., Burton, L. A., Gordon, D. E., Hultquist, J. F., Johnson, J. R., Satkamp, L., Hiatt, J., Rhee, D. Y., Baek, K., Crosby, D. C., Frankel, A. D., Marson, A., Harper, J., Alpi, A. F., Schulman, B. A., Gross, J. D., Krogan, N. J. 2019; 26 (1): 86-+

  Abstract

  The Cullin-RING E3 ligase (CRL) family is commonly hijacked by pathogens to redirect the host ubiquitin proteasome machinery to specific targets. During HIV infection, CRL5 is hijacked by HIV Vif to target viral restriction factors of the APOBEC3 family for ubiquitination and degradation. Here, using a quantitative proteomics approach, we identify the E3 ligase ARIH2 as a regulator of CRL5-mediated APOBEC3 degradation. The CUL5Vif/CBFß complex recruits ARIH2 where it acts to transfer ubiquitin directly to the APOBEC3 targets. ARIH2 is essential for CRL5-dependent HIV infectivity in primary CD4+ T cells. Furthermore, we show that ARIH2 cooperates with CRL5 to prime other cellular substrates for polyubiquitination, suggesting this may represent a general mechanism beyond HIV infection and APOBEC3 degradation. Taken together, these data identify ARIH2 as a co-factor in the Vif-hijacked CRL5 complex that contributes to HIV infectivity and demonstrate the operation of the E1-E2-E3/E3-substrate ubiquitination mechanism in a viral infection context.

  View details for DOI 10.1016/j.chom.2019.05.008

  View details for Web of Science ID 000474689300012

  View details for PubMedID 31253590

  View details for PubMedCentralID PMC7153695

• Enterovirus pathogenesis requires the host methyltransferase SETD3. Nature microbiology Diep, J. n., Ooi, Y. S., Wilkinson, A. W., Peters, C. E., Foy, E. n., Johnson, J. R., Zengel, J. n., Ding, S. n., Weng, K. F., Laufman, O. n., Jang, G. n., Xu, J. n., Young, T. n., Verschueren, E. n., Kobluk, K. J., Elias, J. E., Sarnow, P. n., Greenberg, H. B., Hüttenhain, R. n., Nagamine, C. M., Andino, R. n., Krogan, N. J., Gozani, O. n., Carette, J. E. 2019

  Abstract

  Enteroviruses (EVs) comprise a large genus of positive-sense, single-stranded RNA viruses whose members cause a number of important and widespread human diseases, including poliomyelitis, myocarditis, acute flaccid myelitis and the common cold. How EVs co-opt cellular functions to promote replication and spread is incompletely understood. Here, using genome-scale CRISPR screens, we identify the actin histidine methyltransferase SET domain containing 3 (SETD3) as critically important for viral infection by a broad panel of EVs, including rhinoviruses and non-polio EVs increasingly linked to severe neurological disease such as acute flaccid myelitis (EV-D68) and viral encephalitis (EV-A71). We show that cytosolic SETD3, independent of its methylation activity, is required for the RNA replication step in the viral life cycle. Using quantitative affinity purification-mass spectrometry, we show that SETD3 specifically interacts with the viral 2A protease of multiple enteroviral species, and we map the residues in 2A that mediate this interaction. 2A mutants that retain protease activity but are unable to interact with SETD3 are severely compromised in RNA replication. These data suggest a role of the viral 2A protein in RNA replication beyond facilitating proteolytic cleavage. Finally, we show that SETD3 is essential for in vivo replication and pathogenesis in multiple mouse models for EV infection, including CV-A10, EV-A71 and EV-D68. Our results reveal a crucial role of a host protein in viral pathogenesis, and suggest targeting SETD3 as a potential mechanism for controlling viral infections.

  View details for DOI 10.1038/s41564-019-0551-1

  View details for PubMedID 31527793

• Comparative Flavivirus-Host Protein Interaction Mapping Reveals Mechanisms of Dengue and Zika Virus Pathogenesis CELL Shah, P. S., Link, N., Jang, G. M., Sharp, P. P., Zhu, T., Swaney, D. L., Johnson, J. R., Von Dollen, J., Ramage, H. R., Satkamp, L., Newton, B., Huttenhain, R., Petit, M. J., Baum, T., Everitt, A., Laufman, O., Tassetto, M., Shales, M., Stevenson, E., Iglesias, G. N., Shokat, L., Tripathi, S., Balasubramaniam, V., Webb, L. G., Aguirre, S., Willsey, A., Garcia-Sastre, A., Pollard, K. S., Cherry, S., Gamarnik, A. V., Marazzi, I., Taunton, J., Fernandez-Sesma, A., Bellen, H. J., Andino, R., Krogan, N. J. 2018; 175 (7): 1931-+

  Abstract

  Mosquito-borne flaviviruses, including dengue virus (DENV) and Zika virus (ZIKV), are a growing public health concern. Systems-level analysis of how flaviviruses hijack cellular processes through virus-host protein-protein interactions (PPIs) provides information about their replication and pathogenic mechanisms. We used affinity purification-mass spectrometry (AP-MS) to compare flavivirus-host interactions for two viruses (DENV and ZIKV) in two hosts (human and mosquito). Conserved virus-host PPIs revealed that the flavivirus NS5 protein suppresses interferon stimulated genes by inhibiting recruitment of the transcription complex PAF1C and that chemical modulation of SEC61 inhibits DENV and ZIKV replication in human and mosquito cells. Finally, we identified a ZIKV-specific interaction between NS4A and ANKLE2, a gene linked to hereditary microcephaly, and showed that ZIKV NS4A causes microcephaly in Drosophila in an ANKLE2-dependent manner. Thus, comparative flavivirus-host PPI mapping provides biological insights and, when coupled with in vivo models, can be used to unravel pathogenic mechanisms.

  View details for DOI 10.1016/j.cell.2018.11.028

  View details for Web of Science ID 000453242200020

  View details for PubMedID 30550790

  View details for PubMedCentralID PMC6474419

• CRL4(AMBRA1) targets Elongin C for ubiquitination and degradation to modulate CRL5 signaling EMBO JOURNAL Chen, S., Jang, G. M., Huttenhain, R., Gordon, D. E., Du, D., Newton, B. W., Johnson, J. R., Hiatt, J., Hultquist, J. F., Johnson, T. L., Liu, Y., Burton, L. A., Ye, J., Reichermeier, K. M., Stroud, R. M., Marson, A., Debnath, J., Gross, J. D., Krogan, N. J. 2018; 37 (18)

  Abstract

  Multi-subunit cullin-RING ligases (CRLs) are the largest family of ubiquitin E3 ligases in humans. CRL activity is tightly regulated to prevent unintended substrate degradation or autocatalytic degradation of CRL subunits. Using a proteomics strategy, we discovered that CRL4AMBRA1 (CRL substrate receptor denoted in superscript) targets Elongin C (ELOC), the essential adapter protein of CRL5 complexes, for polyubiquitination and degradation. We showed that the ubiquitin ligase function of CRL4AMBRA1 is required to disrupt the assembly and attenuate the ligase activity of human CRL5SOCS3 and HIV-1 CRL5VIF complexes as AMBRA1 depletion leads to hyperactivation of both CRL5 complexes. Moreover, CRL4AMBRA1 modulates interleukin-6/STAT3 signaling and HIV-1 infectivity that are regulated by CRL5SOCS3 and CRL5VIF, respectively. Thus, by discovering a substrate of CRL4AMBRA1, ELOC, the shared adapter of CRL5 ubiquitin ligases, we uncovered a novel CRL cross-regulation pathway.

  View details for DOI 10.15252/embj.201797508

  View details for Web of Science ID 000444804700002

  View details for PubMedID 30166453

  View details for PubMedCentralID PMC6138441

• The Psychiatric Cell Map Initiative: A Convergent Systems Biological Approach to Illuminating Key Molecular Pathways in Neuropsychiatric Disorders CELL Willsey, A., Morris, M. T., Wang, S., Willsey, H. R., Sun, N., Teerikorpi, N., Baum, T. B., Cagney, G., Bender, K. J., Desai, T. A., Srivastava, D., Davis, G. W., Doudna, J., Chang, E., Sohal, V., Lowenstein, D. H., Li, H., Agard, D., Keiser, M. J., Shoichet, B., von Zastrow, M., Mucke, L., Finkbeiner, S., Gan, L., Sestan, N., Ward, M. E., Huttenhain, R., Nowakowski, T. J., Bellen, H. J., Frank, L. M., Khokha, M. K., Lifton, R. P., Kampmann, M., Ideker, T., State, M. W., Krogan, N. J. 2018; 174 (3): 505-520

  Abstract

  Although gene discovery in neuropsychiatric disorders, including autism spectrum disorder, intellectual disability, epilepsy, schizophrenia, and Tourette disorder, has accelerated, resulting in a large number of molecular clues, it has proven difficult to generate specific hypotheses without the corresponding datasets at the protein complex and functional pathway level. Here, we describe one path forward-an initiative aimed at mapping the physical and genetic interaction networks of these conditions and then using these maps to connect the genomic data to neurobiology and, ultimately, the clinic. These efforts will include a team of geneticists, structural biologists, neurobiologists, systems biologists, and clinicians, leveraging a wide array of experimental approaches and creating a collaborative infrastructure necessary for long-term investigation. This initiative will ultimately intersect with parallel studies that focus on other diseases, as there is a significant overlap with genes implicated in cancer, infectious disease, and congenital heart defects.

  View details for DOI 10.1016/j.cell.2018.06.016

  View details for Web of Science ID 000439870500004

  View details for PubMedID 30053424

  View details for PubMedCentralID PMC6247911

• Similarities and Differences of Blood N-Glycoproteins in Five Solid Carcinomas at Localized Clinical Stage Analyzed by SWATH-MS CELL REPORTS Sajic, T., Liu, Y., Arvaniti, E., Surinova, S., Williams, E. G., Schiess, R., Huttenhain, R., Sethi, A., Pan, S., Brentnall, T. A., Chen, R., Blattmann, P., Friedrich, B., Nimeus, E., Malander, S., Omlin, A., Gillessen, S., Claassen, M., Aebersold, R. 2018; 23 (9): 2819-+

  Abstract

  Cancer is mostly incurable when diagnosed at a metastatic stage, making its early detection via blood proteins of immense clinical interest. Proteomic changes in tumor tissue may lead to changes detectable in the protein composition of circulating blood plasma. Using a proteomic workflow combining N-glycosite enrichment and SWATH mass spectrometry, we generate a data resource of 284 blood samples derived from patients with different types of localized-stage carcinomas and from matched controls. We observe whether the changes in the patient's plasma are specific to a particular carcinoma or represent a generic signature of proteins modified uniformly in a common, systemic response to many cancers. A quantitative comparison of the resulting N-glycosite profiles discovers that proteins related to blood platelets are common to several cancers (e.g., THBS1), whereas others are highly cancer-type specific. Available proteomics data, including a SWATH library to study N-glycoproteins, will facilitate follow-up biomarker research into early cancer detection.

  View details for DOI 10.1016/j.celrep.2018.04.114

  View details for Web of Science ID 000433427000025

  View details for PubMedID 29847809

• An Approach to Spatiotemporally Resolve Protein Interaction Networks in Living Cells CELL Lobingier, B. T., Huttenhain, R., Eichel, K., Miller, K. B., Ting, A. Y., von Zastrow, M., Krogan, N. J. 2017; 169 (2): 350-360

  Abstract

  Cells operate through protein interaction networks organized in space and time. Here, we describe an approach to resolve both dimensions simultaneously by using proximity labeling mediated by engineered ascorbic acid peroxidase (APEX). APEX has been used to capture entire organelle proteomes with high temporal resolution, but its breadth of labeling is generally thought to preclude the higher spatial resolution necessary to interrogate specific protein networks. We provide a solution to this problem by combining quantitative proteomics with a system of spatial references. As proof of principle, we apply this approach to interrogate proteins engaged by G-protein-coupled receptors as they dynamically signal and traffic in response to ligand-induced activation. The method resolves known binding partners, as well as previously unidentified network components. Validating its utility as a discovery pipeline, we establish that two of these proteins promote ubiquitin-linked receptor downregulation after prolonged activation.

  View details for DOI 10.1016/j.cell.2017.03.022

  View details for Web of Science ID 000398349500018

  View details for PubMedID 28388416

• Prediction of colorectal cancer diagnosis based oncirculating plasma proteins EMBO MOLECULAR MEDICINE Surinova, S., Choi, M., Tao, S., Schueffler, P. J., Chang, C., Clough, T., Vyslouzil, K., Khoylou, M., Srovnal, J., Liu, Y., Matondo, M., Huettenhain, R., Weisser, H., Buhmann, J. M., Hajduch, M., Brenner, H., Vitek, O., Aebersold, R. 2015; 7 (9): 1166-1178

  Abstract

  Non-invasive detection of colorectal cancer with blood-based markers is a critical clinical need. Here we describe a phased mass spectrometry-based approach for the discovery, screening, and validation of circulating protein biomarkers with diagnostic value. Initially, we profiled human primary tumor tissue epithelia and characterized about 300 secreted and cell surface candidate glycoproteins. These candidates were then screened in patient systemic circulation to identify detectable candidates in blood plasma. An 88-plex targeting method was established to systematically monitor these proteins in two large and independent cohorts of plasma samples, which generated quantitative clinical datasets at an unprecedented scale. The data were deployed to develop and evaluate a five-protein biomarker signature for colorectal cancer detection.

  View details for DOI 10.15252/emmm.201404873

  View details for Web of Science ID 000360963900007

  View details for PubMedID 26253081

  View details for PubMedCentralID PMC4568950

• Targeted Peptide Measurements in Biology and Medicine: Best Practices for Mass Spectrometry- based Assay Development Using a Fit- for- Purpose Approach MOLECULAR & CELLULAR PROTEOMICS Carr, S. A., Abbatiello, S. E., Ackermann, B. L., Borchers, C., Domon, B., Deutsch, E. W., Grant, R. P., Hoofnagle, A. N., Huettenhain, R., Koomen, J. M., Liebler, D. C., Liu, T., MacLean, B., Mani, D. R., Mansfield, E., Neubert, H., Paulovich, A. G., Reiter, L., Vitek, O., Aebersold, R., Anderson, L., Bethem, R., Blonder, J., Boja, E., Botelho, J., Boyne, M., Bradshaw, R. A., Burlingame, A. L., Chan, D., Keshishian, H., Kuhn, E., Kinsinger, C., Lee, J. H., Lee, S., Moritz, R., Oses-Prieto, J., Rifai, N., Ritchie, J., Rodriguez, H., Srinivas, P. R., Townsend, R., Van Eyk, J., Whiteley, G., Wiita, A., Weintraub, S. 2014; 13 (3): 907-917

  Abstract

  Adoption of targeted mass spectrometry (MS) approaches such as multiple reaction monitoring (MRM) to study biological and biomedical questions is well underway in the proteomics community. Successful application depends on the ability to generate reliable assays that uniquely and confidently identify target peptides in a sample. Unfortunately, there is a wide range of criteria being applied to say that an assay has been successfully developed. There is no consensus on what criteria are acceptable and little understanding of the impact of variable criteria on the quality of the results generated. Publications describing targeted MS assays for peptides frequently do not contain sufficient information for readers to establish confidence that the tests work as intended or to be able to apply the tests described in their own labs. Guidance must be developed so that targeted MS assays with established performance can be made widely distributed and applied by many labs worldwide. To begin to address the problems and their solutions, a workshop was held at the National Institutes of Health with representatives from the multiple communities developing and employing targeted MS assays. Participants discussed the analytical goals of their experiments and the experimental evidence needed to establish that the assays they develop work as intended and are achieving the required levels of performance. Using this "fit-for-purpose" approach, the group defined three tiers of assays distinguished by their performance and extent of analytical characterization. Computational and statistical tools useful for the analysis of targeted MS results were described. Participants also detailed the information that authors need to provide in their manuscripts to enable reviewers and readers to clearly understand what procedures were performed and to evaluate the reliability of the peptide or protein quantification measurements reported. This paper presents a summary of the meeting and recommendations.

  View details for DOI 10.1074/mcp.M113.036095

  View details for Web of Science ID 000332391100017

  View details for PubMedID 24443746

  View details for PubMedCentralID PMC3945918

• Mass spectrometric protein maps for biomarker discovery and clinical research EXPERT REVIEW OF MOLECULAR DIAGNOSTICS Liu, Y., Huettenhain, R., Collins, B., Aebersold, R. 2013; 13 (8): 811-825

  Abstract

  Among the wide range of proteomic technologies, targeted mass spectrometry (MS) has shown great potential for biomarker studies. To extend the degree of multiplexing achieved by selected reaction monitoring (SRM), we recently developed SWATH MS. SWATH MS is a variant of the emerging class of data-independent acquisition (DIA) methods and essentially converts the molecules in a physical sample into perpetually re-usable digital maps. The thus generated SWATH maps are then mined using a targeted data extraction strategy, allowing us to profile disease-related proteomes at a high degree of reproducibility. The successful application of both SRM and SWATH MS requires the a priori generation of reference spectral maps that provide coordinates for quantification. Herein, we demonstrate that the application of the mass spectrometric reference maps and the acquisition of personalized SWATH maps hold a particular promise for accelerating the current process of biomarker discovery.

  View details for DOI 10.1586/14737159.2013.845089

  View details for Web of Science ID 000326029100012

  View details for PubMedID 24138574

  View details for PubMedCentralID PMC3833812

• Automated selected reaction monitoring data analysis workflow for large-scale targeted proteomic studies NATURE PROTOCOLS Surinova, S., Huettenhain, R., Chang, C., Espona, L., Vitek, O., Aebersold, R. 2013; 8 (8): 1602-1619

  Abstract

  Targeted proteomics based on selected reaction monitoring (SRM) mass spectrometry is commonly used for accurate and reproducible quantification of protein analytes in complex biological mixtures. Strictly hypothesis-driven, SRM assays quantify each targeted protein by collecting measurements on its peptide fragment ions, called transitions. To achieve sensitive and accurate quantitative results, experimental design and data analysis must consistently account for the variability of the quantified transitions. This consistency is especially important in large experiments, which increasingly require profiling up to hundreds of proteins over hundreds of samples. Here we describe a robust and automated workflow for the analysis of large quantitative SRM data sets that integrates data processing, statistical protein identification and quantification, and dissemination of the results. The integrated workflow combines three software tools: mProphet for peptide identification via probabilistic scoring; SRMstats for protein significance analysis with linear mixed-effect models; and PASSEL, a public repository for storage, retrieval and query of SRM data. The input requirements for the protocol are files with SRM traces in mzXML format, and a file with a list of transitions in a text tab-separated format. The protocol is especially suited for data with heavy isotope-labeled peptide internal standards. We demonstrate the protocol on a clinical data set in which the abundances of 35 biomarker candidates were profiled in 83 blood plasma samples of subjects with ovarian cancer or benign ovarian tumors. The time frame to realize the protocol is 1-2 weeks, depending on the number of replicates used in the experiment.

  View details for DOI 10.1038/nprot.2013.091

  View details for Web of Science ID 000322228600010

  View details for PubMedID 23887179

• N-Glycoprotein SRMAtlas A RESOURCE OF MASS SPECTROMETRIC ASSAYS FOR N-GLYCOSITES ENABLING CONSISTENT AND MULTIPLEXED PROTEIN QUANTIFICATION FOR CLINICAL APPLICATIONS MOLECULAR & CELLULAR PROTEOMICS Huettenhain, R., Surinova, S., Ossola, R., Sun, Z., Campbell, D., Cerciello, F., Schiess, R., Bausch-Fluck, D., Rosenberger, G., Chen, J., Rinner, O., Kusebauch, U., Hajduch, M., Moritz, R. L., Wollscheid, B., Aebersold, R. 2013; 12 (4): 1005-1016

  Abstract

  Protein biomarkers have the potential to transform medicine as they are clinically used to diagnose diseases, stratify patients, and follow disease states. Even though a large number of potential biomarkers have been proposed over the past few years, almost none of them have been implemented so far in the clinic. One of the reasons for this limited success is the lack of technologies to validate proposed biomarker candidates in larger patient cohorts. This limitation could be alleviated by the use of antibody-independent validation methods such as selected reaction monitoring (SRM). Similar to measurements based on affinity reagents, SRM-based targeted mass spectrometry also requires the generation of definitive assays for each targeted analyte. Here, we present a library of SRM assays for 5568 N-glycosites enabling the multiplexed evaluation of clinically relevant N-glycoproteins as biomarker candidates. We demonstrate that this resource can be utilized to select SRM assay sets for cancer-associated N-glycoproteins for their subsequent multiplexed and consistent quantification in 120 human plasma samples. We show that N-glycoproteins spanning 5 orders of magnitude in abundance can be quantified and that previously reported abundance differences in various cancer types can be recapitulated. Together, the established N-glycoprotein SRMAtlas resource facilitates parallel, efficient, consistent, and sensitive evaluation of proposed biomarker candidates in large clinical sample cohorts.

  View details for DOI 10.1074/mcp.O112.026617

  View details for Web of Science ID 000317341500018

  View details for PubMedID 23408683

  View details for PubMedCentralID PMC3617325

• Quantitative measurements of N-linked glycoproteins in human plasma by SWATH-MS PROTEOMICS Liu, Y., Huettenhain, R., Surinova, S., Gillet, L. J., Mouritsen, J., Brunner, R., Navarro, P., Aebersold, R. 2013; 13 (8): 1247-1256

  Abstract

  SWATH-MS is a data-independent acquisition method that generates, in a single measurement, a complete recording of the fragment ion spectra of all the analytes in a biological sample for which the precursor ions are within a predetermined m/z versus retention time window. To assess the performance and suitability of SWATH-MS-based protein quantification for clinical use, we compared SWATH-MS and SRM-MS-based quantification of N-linked glycoproteins in human plasma, a commonly used sample for biomarker discovery. Using dilution series of isotopically labeled heavy peptides representing biomarker candidates, the LOQ of SWATH-MS was determined to reach 0.0456 fmol at peptide level by targeted data analysis, which corresponds to a concentration of 5-10 ng protein/mL in plasma, while SRM reached a peptide LOQ of 0.0152 fmol. Moreover, the quantification of endogenous glycoproteins using SWATH-MS showed a high degree of reproducibility, with the mean CV of 14.90%, correlating well with SRM results (R(2) = 0.9784). Overall, SWATH-MS measurements showed a slightly lower sensitivity and a comparable reproducibility to state-of-the-art SRM measurements for targeted quantification of the N-glycosites in human blood. However, a significantly larger number of peptides can be quantified per analysis. We suggest that SWATH-MS analysis combined with N-glycoproteome enrichment in plasma samples is a promising integrative proteomic approach for biomarker discovery and verification.

  View details for DOI 10.1002/pmic.201200417

  View details for Web of Science ID 000317684800004

  View details for PubMedID 23322582

• Reproducible Quantification of Cancer-Associated Proteins in Body Fluids Using Targeted Proteomics SCIENCE TRANSLATIONAL MEDICINE Huttenhain, R., Soste, M., Selevsek, N., Roest, H., Sethi, A., Carapito, C., Farrah, T., Deutsch, E. W., Kusebauch, U., Moritz, R. L., Nimeus-Malmstroem, E., Rinner, O., Aebersold, R. 2012; 4 (142): 142ra94

  Abstract

  The rigorous testing of hypotheses on suitable sample cohorts is a major limitation in translational research. This is particularly the case for the validation of protein biomarkers; the lack of accurate, reproducible, and sensitive assays for most proteins has precluded the systematic assessment of hundreds of potential marker proteins described in the literature. Here, we describe a high-throughput method for the development and refinement of selected reaction monitoring (SRM) assays for human proteins. The method was applied to generate such assays for more than 1000 cancer-associated proteins, which are functionally related to candidate cancer driver mutations. We used the assays to determine the detectability of the target proteins in two clinically relevant samples: plasma and urine. One hundred eighty-two proteins were detected in depleted plasma, spanning five orders of magnitude in abundance and reaching below a concentration of 10 ng/ml. The narrower concentration range of proteins in urine allowed the detection of 408 proteins. Moreover, we demonstrate that these SRM assays allow reproducible quantification by monitoring 34 biomarker candidates across 83 patient plasma samples. Through public access to the entire assay library, researchers will be able to target their cancer-associated proteins of interest in any sample type using the detectability information in plasma and urine as a guide. The generated expandable reference map of SRM assays for cancer-associated proteins will be a valuable resource for accelerating and planning biomarker verification studies.

  View details for DOI 10.1126/scitranslmed.3003989

  View details for Web of Science ID 000306356000003

  View details for PubMedID 22786679

  View details for PubMedCentralID PMC3766734

• Range of protein detection by selected/multiple reaction monitoring mass spectrometry in an unfractionated human cell culture lysate PROTEOMICS Ebhardt, H., Sabido, E., Huettenhain, R., Collins, B., Aebersold, R. 2012; 12 (8): 1185-1193

  Abstract

  Selected or multiple reaction monitoring is a targeted mass spectrometry method (S/MRM-MS), in which many peptides are simultaneously and consistently analyzed during a single liquid chromatography-mass spectrometry (LC-S/MRM-MS) measurement. These capabilities make S/MRM-MS an attractive method to monitor a consistent set of proteins over various experimental conditions. To increase throughput for S/MRM-MS it is advantageous to use scheduled methods and unfractionated protein extracts. Here, we established the practically measurable dynamic range of proteins reliably detectable and quantifiable in an unfractionated protein extract from a human cell line using LC-S/MRM-MS. Initially, we analyzed S/MRM transition peak groups in terms of interfering signals and compared S/MRM transition peak groups to MS1-triggered MS2 spectra using dot-product analysis. Finally, using unfractionated protein extract from human cell lysate, we quantified the upper boundary of copies per cell to be 35 million copies per cell, while 7500 copies per cell represents a lower boundary using a single 35 min linear gradient LC-S/MRM-MS measurement on a current, standard commercial instrument.

  View details for DOI 10.1002/pmic.201100543

  View details for Web of Science ID 000303918200013

  View details for PubMedID 22577020

• Protein Significance Analysis in Selected Reaction Monitoring (SRM) Measurements MOLECULAR & CELLULAR PROTEOMICS Chang, C., Picotti, P., Huettenhain, R., Heinzelmann-Schwarz, V., Jovanovic, M., Aebersold, R., Vitek, O. 2012; 11 (4): M111.014662

  Abstract

  Selected reaction monitoring (SRM) is a targeted mass spectrometry technique that provides sensitive and accurate protein detection and quantification in complex biological mixtures. Statistical and computational tools are essential for the design and analysis of SRM experiments, particularly in studies with large sample throughput. Currently, most such tools focus on the selection of optimized transitions and on processing signals from SRM assays. Little attention is devoted to protein significance analysis, which combines the quantitative measurements for a protein across isotopic labels, peptides, charge states, transitions, samples, and conditions, and detects proteins that change in abundance between conditions while controlling the false discovery rate. We propose a statistical modeling framework for protein significance analysis. It is based on linear mixed-effects models and is applicable to most experimental designs for both isotope label-based and label-free SRM workflows. We illustrate the utility of the framework in two studies: one with a group comparison experimental design and the other with a time course experimental design. We further verify the accuracy of the framework in two controlled data sets, one from the NCI-CPTAC reproducibility investigation and the other from an in-house spike-in study. The proposed framework is sensitive and specific, produces accurate results in broad experimental circumstances, and helps to optimally design future SRM experiments. The statistical framework is implemented in an open-source R-based software package SRMstats, and can be used by researchers with a limited statistics background as a stand-alone tool or in integration with the existing computational pipelines.

  View details for DOI 10.1074/mcp.M111.014662

  View details for Web of Science ID 000302786500017

  View details for PubMedID 22190732

  View details for PubMedCentralID PMC3322573

• PASSEL: The PeptideAtlas SRM experiment library PROTEOMICS Farrah, T., Deutsch, E. W., Kreisberg, R., Sun, Z., Campbell, D. S., Mendoza, L., Kusebauch, U., Brusniak, M., Huettenhain, R., Schiess, R., Selevsek, N., Aebersold, R., Moritz, R. L. 2012; 12 (8): 1170-1175

  Abstract

  Public repositories for proteomics data have accelerated proteomics research by enabling more efficient cross-analyses of datasets, supporting the creation of protein and peptide compendia of experimental results, supporting the development and testing of new software tools, and facilitating the manuscript review process. The repositories available to date have been designed to accommodate either shotgun experiments or generic proteomic data files. Here, we describe a new kind of proteomic data repository for the collection and representation of data from selected reaction monitoring (SRM) measurements. The PeptideAtlas SRM Experiment Library (PASSEL) allows researchers to easily submit proteomic data sets generated by SRM. The raw data are automatically processed in a uniform manner and the results are stored in a database, where they may be downloaded or browsed via a web interface that includes a chromatogram viewer. PASSELenables cross-analysis of SRMdata, supports optimization of SRMdata collection, and facilitates the review process of SRMdata. Further, PASSELwill help in the assessment of proteotypic peptide performance in a wide array of samples containing the same peptide, as well as across multiple experimental protocols.

  View details for DOI 10.1002/pmic.201100515

  View details for Web of Science ID 000303918200011

  View details for PubMedID 22318887

  View details for PubMedCentralID PMC3832291

• mProphet: automated data processing and statistical validation for large-scale SRM experiments NATURE METHODS Reiter, L., Rinner, O., Picotti, P., Huettenhain, R., Beck, M., Brusniak, M., Hengartner, M. O., Aebersold, R. 2011; 8 (5): 430-U85

  Abstract

  Selected reaction monitoring (SRM) is a targeted mass spectrometric method that is increasingly used in proteomics for the detection and quantification of sets of preselected proteins at high sensitivity, reproducibility and accuracy. Currently, data from SRM measurements are mostly evaluated subjectively by manual inspection on the basis of ad hoc criteria, precluding the consistent analysis of different data sets and an objective assessment of their error rates. Here we present mProphet, a fully automated system that computes accurate error rates for the identification of targeted peptides in SRM data sets and maximizes specificity and sensitivity by combining relevant features in the data into a statistical model.

  View details for DOI 10.1038/NMETH.1584

  View details for Web of Science ID 000289987100022

  View details for PubMedID 21423193

• On the Development of Plasma Protein Biomarkers JOURNAL OF PROTEOME RESEARCH Surinova, S., Schiess, R., Huettenhain, R., Cerciello, F., Wollscheid, B., Aebersold, R. 2011; 10 (1): 5-16

  Abstract

  The development of plasma biomarkers has proven to be more challenging than initially anticipated. Many studies have reported lists of candidate proteins rather than validated candidate markers with an assigned performance to a specific clinical objective. Biomarker research necessitates a clear rational framework with requirements on a multitude of levels. On the technological front, the platform needs to be effective to detect low abundant plasma proteins and be able to measure them in a high throughput manner over a large amount of samples reproducibly. At a conceptual level, the choice of the technological platform and available samples should be part of an overall clinical study design that depends on a joint effort between basic and clinical research. Solutions to these needs are likely to facilitate more feasible studies. Targeted proteomic workflows based on SRM mass spectrometry show the potential of fast verification of biomarker candidates in plasma and thereby closing the gap between discovery and validation in the biomarker development pipeline. Biological samples need to be carefully chosen based on well-established guidelines either for candidate discovery in the form of disease models with optimal fidelity to human disease or for candidate evaluation as well-designed and annotated clinical cohort groups. Most importantly, they should be representative of the target population and directly address the investigated clinical question. A conceptual structure of a biomarker study can be provided in the form of several sequential phases, each having clear objectives and predefined goals. Furthermore, guidelines for reporting the outcome of biomarker studies are critical to adequately assess the quality of the research, interpretation and generalization of the results. By being attentive to and applying these considerations, biomarker research should become more efficient and lead to directly translatable biomarker candidates into clinical evaluation.

  View details for DOI 10.1021/pr1008515

  View details for Web of Science ID 000285812000003

  View details for PubMedID 21142170

• A combined top-down and bottom-up MS approach for the characterization of hemoglobin variants in Rhesus monkeys PROTEOMICS Huettenhain, R., Hess, S. 2010; 10 (20): 3657-3668

  Abstract

  Sickle cell disease is caused by one of the 1200 known hemoglobin variations. A single-point mutation β6(A3)Glu→Val leads to sickling of red blood cells, which in turn causes a lack of oxygen supply to tissue and organs. Although sickle cell disease is well understood, treatment options are currently underdeveloped. The only Food and Drug Administration-approved drug is hydroxyurea, an inducer of fetal γ-hemoglobin, which is known to have a higher oxygen affinity than adult hemoglobins and thus alleviates symptoms. In the search for better cures, Rhesus monkeys (Macaca mulatta) serve as models for monitoring success of induction of fetal γ-hemoglobins and with recent advances in proteomics, MS has become the leading technique to determine globin expression. Similar to humans, Rhesus monkeys possess hemoglobin variants that have not been sufficiently characterized to initiate such a study. Therefore, we developed a combined bottom-up and top-down approach to identify and characterize novel hemoglobin variants of the umbilical cord blood of Rhesus monkeys. A total of four different variants were studied: α, β, γ1 and γ2. A new α- and β-hemoglobin variant was identified, and the two previously hypothesized γ-hemoglobins were identified. In addition, glutathionylation of both γ-hemoglobin variants at their cysteines has been characterized. The combined approach outperformed either bottom-up or top-down alone and can be used for characterization of unknown hemoglobin variants and their PTMs.

  View details for DOI 10.1002/pmic.201000161

  View details for Web of Science ID 000284044900010

  View details for PubMedID 20848672

  View details for PubMedCentralID PMC3036950

• Perspectives of targeted mass spectrometry for protein biomarker verification CURRENT OPINION IN CHEMICAL BIOLOGY Huettenhain, R., Malmstroem, J., Picotti, P., Aebersold, R. 2009; 13 (5-6): 518-525

  Abstract

  The identification of specific biomarkers will improve the early diagnosis of disease, facilitate the development of targeted therapies, and provide an accurate method to monitor treatment response. A major challenge in the process of verifying biomarker candidates in blood plasma is the complexity and high dynamic range of proteins. This article reviews the current, targeted proteomic strategies that are capable of quantifying biomarker candidates at concentration ranges where biomarkers are expected in plasma (i.e. at the ng/ml level). In addition, a workflow is presented that allows the fast and definitive generation of targeted mass spectrometry-based assays for most biomarker candidate proteins. These assays are stored in publicly accessible databases and have the potential to greatly impact the throughput of biomarker verification studies.

  View details for DOI 10.1016/j.cbpa.2009.09.014

  View details for Web of Science ID 000272984600004

  View details for PubMedID 19818677

  View details for PubMedCentralID PMC2795387