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207. ​Vilhais-Neto GC, Fournier M, Plassat J-L, Maruhashi M, Garnier J-M, Sardiu ME, Saraf A, Florens L, Washburn MP, & Pourquié O. (2017) A new coactivator complex required for retinoic acid-dependent regulation of embryonic symmetry. Nature Comm, In Press. Abstract.

206. ​Kuchay S, Giorgi C, Simoneschi D, Pagan J, Missiroli S, Saraf A, Florens L, Washburn MP, Collazo-Lorduy A, Castillo-Martin M, Cordon-Cardo C, Sebti SM, Pinton P, & Pagano M. (2017) PTEN counteracts FBXL2 to promote IP3R3- and Ca2+-mediated apoptosis limiting tumor growth. Nature, Jun 22;546(7659):554-558. Abstract.

205. ​Nemec CM, Yang F, Gilmore JM, Hintermair C, Ho Y-H, Tseng SC, Heidemann M, Zhang Y, Florens L, Gasch AP, Eick D, Washburn MP, Varani G, Ansari AZ. (2017) Different phospho-isoforms of RNA polymerase II engage the Rtt103 termination factor in a structurally analogous manner. Proc Natl Acad Sci USA, May 16;114(20):E3944-E3953. Abstract.

204. ​​​Ma X, Zhu X, Han Y, Story B, Do T, Song X, Wang S, Zhang Y, Blanchette M, Gogol M, Hall K, Perera A, Xie T (2017) Aubergine Controls Germline Stem Cell SelfRenewal and Progeny Differentiation via Distinct Mechanisms. Dev Cell, Apr 24;41(2):157-169.e5. Abstract.

203. ​​​Donato V, Bonora M, Simoneschi D, Sartini D, Kudo Y, Saraf A, Florens L, Washburn MP, Stadtfeld M, Pinton P, and Pagano M (2017) CThe TDH-GCN5L1-Fbxo15-KBP axis limits mitochondrial biogenesis in mouse embryonic stem cells. Nature Cell, Apr 19(4):341-351. Abstract.

202. Identification of Topological Network Modules in Perturbed Protein Interaction Networks.

Sardiu ME, Gilmore JM, Groppe B, Florens L, Washburn MP. (2017) Scientific Reports, Mar 8;7:43845.

Biological networks consist of functional modules, however detecting and characterizing such modules in networks remains challenging. Perturbing networks is one strategy for identifying modules. Here we used an advanced mathematical approach named topological data analysis (TDA) to interrogate two perturbed networks. In one, we disrupted the S. cerevisiae INO80 protein interaction network by isolating complexes after protein complex components were deleted from the genome. In the second, we reanalyzed previously published data demonstrating the disruption of the human Sin3 network with a histone deacetylase inhibitor. Here we show that disrupted networks contained topological network modules (TNMs) with shared properties that mapped onto distinct locations in networks. We define TMNs as proteins that occupy close network positions depending on their coordinates in a topological space. TNMs provide new insight into networks by capturing proteins from different categories including proteins within a complex, proteins with shared biological functions, and proteins disrupted across networks.​


201. ​​​Ruan L, Zhou C, Jin E, Kucharavy A, Zhang Y, Wen Z, Florens L, Li R (2017) Cytosolic proteostasis through importing of misfolded proteins into mitochondria. Nature, Mar 16;543(7645):443-446. Abstract.

200. ​​Tsai K-L, Yu X, Gopalan S, Chao TC, Zhang Y, Florens L, Washburn MP, Murakami K, Conaway RC, Conaway JW, Asturias FJ (2017) Mediator structure and rearrangements required for holoenzyme formation. Nature, Apr 13;544(7649):196-201. Abstract.

199. ​Dutta A, Sardiu M, Gogol M, Gilmore JM, Zhang D, Florens L, Washburn MP, Abmayr SM, Workman JL. (2017) Composition and Function of Mutant Swi/Snf Complexes. Cell Reports, Feb 28;18(9):2124-2134. Abstract.

198. Lee J, Choi E S, Seo HD, Kang K, Gilmore JM, Florens L, Washburn MP, Choe J, Workman JL, Lee D. (2017) Chromatin remodeler Fun30-Fft3 induces nucleosome disassembly to facilitate RNA polymerase II elongation. Nature Comm, Feb 20;8:14527. Abstract.

197. Dowdell AS, Murphy MD, Azodi C, Swanson SK, Florens L, Chen S. & Zückert WR. (2017) Comprehensive spatial analysis of the Borrelia burgdorferi lipoproteome reveals a compartmentalization bias toward the bacterial surface. J Bacteriology, Feb 28;199(6). Abstract.

196. Liang K, Volk AG, Haug JS, Marshall SA, Woodfin AR, Bartom ET, Gilmore JM, Florens L, Washburn MP, Sullivan KD, Espinosa JM, Cannova J, Zhang J, Smith ER, Crispino JD, and Shilatifard A. (2017) Therapeutic targeting of MLL degradation pathways in MLL-rearranged leukemia. Cell, Jan 12;168(1-2):59-72.e13. Abstract.


195. Terry-Lorenzo RT, Torres VI, Wagh D, Galaz J, Swanson SK, Florens L, Washburn MP, Waites CL, Gundelfinger ED, Reimer RJ, & Garner CC. (2016) Trio, a Rho Family GEF, interacts with the presynaptic active zone proteins Piccolo and Bassoon. PLoS ONE, Dec 1;11(12):e0167535. Abstract.

194. Advancement of mass spectrometry-based proteomics technologies to explore triple negative breast cancer.

Miah S, Banks CAS, Adams MK, Florens L, Lukong KE, & Washburn MP. (2017) Mol BioSyst, Dec 20;13(1):42-55.

Understanding the complexity of cancer biology requires extensive information about the cancer proteome over the course of the disease. The recent advances in mass spectrometry-based proteomics technologies have led to the accumulation of an incredible amount of such proteomic information. This information allows us to identify protein signatures or protein biomarkers, which can be used to improve cancer diagnosis, prognosis and treatment. For example, mass spectrometry-based proteomics has been used in breast cancer research for over two decades to elucidate protein function. Breast cancer is a heterogeneous group of diseases with distinct molecular features that are reflected in tumour characteristics and clinical outcomes. Compared with all other subtypes of breast cancer, triple-negative breast cancer is perhaps the most distinct in nature and heterogeneity. In this review, we provide an introductory overview of the application of advanced proteomic technologies to triple-negative breast cancer research.


193. Lan X, Atanassov BS, Li W, Zhang Y, Florens L, Mohan RD, Galardy PJ, Washburn MP, Workman JL, and Dent SYR. (2016) USP44 is an integral component of N-CoR that contributes to gene repression by deubiquitinating histone H2B. Cell Reports, Nov 22;17(9):2382-2393. Abstract.

192. Sato S, Tomomori-Sato C, Tsai K-L, Yu X, Sardiu M, Saraf A, Washburn MP, Florens L, Asturias F, Conaway RC and Conaway JW. (2016) Role for the MED21-MED7 hinge in assembly of the Mediator-RNA polymerase II holoenzyme. J Biol Chem, Dec 23;291(52):26886-26898. Abstract.

191. Dyer JO, Dutta A, Gogol M Weake VM, Dialynas G, Wu X, Seidel C, Zhang Y, Florens L, Washburn MP, Abmayr SM, & Workman JL. (2016) Myeloid Leukemia Factor acts in a chaperone complex to regulate transcription factor stability and gene expression. J Mol Biol, Jun 30;429(13):2093-2107. Abstract.

190. Brannan KW, Jin W, Huelga SC, Banks CAS, Gilmore JM, Florens L, Washburn MP, Van Nostrand EL, Pratt GA, Schwinn MK, Daniels D, Yeo GW. (2016) SONAR discovers RNA binding proteins from analysis of large-scale protein-protein interactomes. Mol Cell, Oct 20;64(2):282-293. Abstract.

189. TNIP2 is a hub protein in the NF-κB network with both protein and RNA mediated interactions.

Banks CAS, Boanca G, Lee ZT, Eubanks CG, Hattem GL, Peak A, Weems LE, Conkright JJ, Florens L, & Washburn MP. (2016) Mol Cell Proteomics, Nov 15(11):3435-3449.

The NF-κB family of transcription factors is pivotal in controlling cellular responses to environmental stresses; abnormal NF-κB signaling features in many autoimmune diseases and cancers. Several components of the NF-κB signaling pathway have been reported to interact with the protein TNIP2 (also known as ABIN2), and TNIP2 can both positively and negatively regulate NF-κB- dependent transcription of target genes. However, the function of TNIP2 remains elusive and the cellular machinery associating with TNIP2 has not been systematically defined. Here we first used a broad MudPIT/Halo AP-MS approach to map the network of proteins associated with the NF-κB transcription factors, and establish TNIP2 as an NF-κB network hub protein. We then combined AP-MS with biochemical approaches in a more focused study of truncated and mutated forms of TNIP2 to map protein associations with distinct regions of TNIP2. NF-κB interacted with the N-terminal region of TNIP2. A central region of TNIP2 interacted with the endosomal sorting complex ESCRT-I via its TSG101 subunit, a protein essential for HIV-1 budding, and a single point mutant in TNIP2 disrupted this interaction. The major gene ontology category for TNIP2 associated proteins was mRNA metabolism, and several of these associations, like KHDRBS1, were lost upon depletion of RNA. Given the major association of TNIP2 with mRNA metabolism proteins, we analyzed the RNA content of affinity purified TNIP2 using RNASeq. Surprisingly, a specific limited number of mRNAs was associated with TNIP2. These RNAs were enriched for transcription factor binding, transcription factor co-factor activity, and transcription regulator activity. They included mRNAs of genes in the Sin3A complex, the Mediator complex, JUN, HOXC6, and GATA2. Taken together, our findings suggest an expanded role for TNIP2, establishing a link between TNIP2, cellular transport machinery, and RNA transcript processing.


188. Li W, Atanassov BS, Lan X, Mohan RD, Swanson SK, Farria AT, Florens L, Washburn MP, Workman JL, and Dent SYR. (2016) Cytoplasmic ATXN7L3B interferes with nuclear functions of the SAGA deubiquitinase module. Mol Cell Biol, Oct 28;36(22):2855-2866. Print 2016 Nov 15. Abstract.

187. Haynes PA, Stein SE, Washburn MP. (2016) Data quality issues in proteomics ​– there are many paths to enlightenment. Proteomics, Sep 16(18):2433-4. Abstract.

186. ​Bunnik EM, Batugedara G, Saraf A, Prudhomme J, Florens L, Le Roch KG. (2016) The mRNA-bound proteome of the human malaria parasite Plasmodium falciparum. Genome Biology, Jul 5;17(1):147. Abstract

185. Dynamic and combinatorial landscape of histone modifications during the intraerythrocytic developmental cycle of the malaria parasite.

Saraf A, Cervantes S, Bunnik EM, Ponts N, Sardiu, ME, Chung DD, Prudhomme J, Wen Z, Varberg JM, Washburn MP, Florens L, Le Roch KG. (2016) J Proteome Res, Aug 5;15(8):2787-801.

A major obstacle in understanding the complex biology of the malaria parasite remains to discover how gene transcription is controlled during its life cycle. Accumulating evidence indicates that the parasite's epigenetic state plays a fundamental role in gene expression and virulence. Using a comprehensive and quantitative mass spectrometry approach, we determined the global and dynamic abundance of histones and their covalent post-transcriptional modifications throughout the intraerythrocytic developmental cycle of Plasmodium falciparum. We detected a total of 232 distinct modifications, of which 160 had never been detected in Plasmodium and 88 had never been identified in any other species. We further validated over 10% of the detected modifications and their expression patterns by multiple reaction monitoring assays. In addition, we uncovered an unusual chromatin organization with parasite-specific histone modifications and combinatorial dynamics that may be directly related to transcriptional activity, DNA replication, and cell cycle progression. Overall, our data suggest that the malaria parasite has a unique histone modification signature that correlates with parasite virulence.


184. Hrecka K, Hao C, Shun MC, Kaur S, Swanson SK, Florens L, Washburn MP, Skowronski J. (2016) HIV-1 and HIV-2 exhibit divergent interactions with HLTF and UNG2 DNA repair proteins. Proc Natl Acad Sci USA, Jul 5;113(27):E3921-30. Abstract.

183. WDR76 co-localizes with heterochromatin related proteins and rapidly responds to DNA damage.

​Gilmore JM, Sardiu ME, Groppe BD, Thornton JL, Liu X, Dayebgadoh G, Banks CA, Slaughter BD, Unruh JR, Workman JL, Florens L, Washburn MP. (2016) PLoS ONE, Jun 1;11(6):e0155492.

Proteins that respond to DNA damage play critical roles in normal and diseased states in human biology. Studies have suggested that the S. cerevisiae protein CMR1/YDL156w is associated with histones and is possibly associated with DNA repair and replication processes. Through a quantitative proteomic analysis of affinity purifications here we show that the human homologue of this protein, WDR76, shares multiple protein associations with the histones H2A, H2B, and H4. Furthermore, our quantitative proteomic analysis of WDR76 associated proteins demonstrated links to proteins in the DNA damage response like PARP1 and XRCC5 and heterochromatin related proteins like CBX1, CBX3, and CBX5. Co-immunoprecipitation studies validated ​these interactions. Next, quantitative imaging studies demonstrated that WDR76 was recruited to laser induced DNA damage immediately after induction, and we compared the recruitment of WDR76 to laser induced DNA damage to known DNA damage proteins like PARP1, XRCC5, and RPA1. In addition, WDR76 co-localizes to puncta with the heterochromatin proteins CBX1 and CBX5, which are also recruited to DNA damage but much less intensely than WDR76. This work demonstrates the chromatin and DNA damage protein associations of WDR76 and demonstrates the rapid response of WDR76 to laser induced DNA damage.


182. ​Huang F, Saraf A, Florens L, Kusch T, Swanson SK, Szerszen LT, Li G, Dutta A, Washburn MP, Abmayr SM, Workman JL. (2016) The Enok acetyltransferase complex interacts with Elg1 and negatively regulates PCNA unloading to promote the G1/S transition. Genes & Dev, May 15;30(10):1198-210. [Epub 2016 May 19]. Abstract.

181. Stegeman R, Spreacker PJ, Swanson SK, Florens L, Washburn MP, Weake VM. (2016) The spliceosomal protein SF3B5 is a novel component of Drosophila SAGA that functions in gene expression independent of splicing​. J Mol Biol, May 13. pii: S0022-2836(16)30152-8. [Epub 2016 May 13]. Abstract.

180. ​Korfali N, Florens L, Schirmer EC. (2016) Isolation, proteomic analysis and microscopy confirmation of the liver nuclear envelope proteome. Methods Mol Biol, 1411:3-44. Abstract.

179. ​Atanassov BS, Mohan RD, Lan XJ, Kuang X, Lu Y, Lin K, McIvor E, Li W, Zhang Y, Florens L, Byrum SD, Mackintosh SG, Calhoun-Davis T, Koutelou E, Wang L, Tang DG, Tackett AJ, Washburn MP, Workman JL, Dent SY (2016) ATXN7L3 and ENY2 coordinate activity of multiple H2B deubiquinases important for cellular proliferation and tumor growth. Molecular Cell, 62, 1–14 [ePub April 28 2016]. Abstract.

178. Washburn MP. (2016) There is no human interactome. Genome Biol, 17(1):48. Abstract.
Protein complexes are dynamic. A new analysis of two quantitative proteomic datasets reveals cell type-specific changes in the stoichiometry of complexes, which often involve paralog switching.

177. Proteomic and Genomic Analyses of the Rvb1 and Rvb2 Interaction Network upon Deletion of R2TP Complex Components.

Lakshminarasimhan M, Boanca G, Banks CAS, Gabriel AE, Groppe BD, Hattem G, Smoyer C, Malanowski KE, Peak A, Florens L, Washburn MP. (2016) Mol Cell Proteomics, 15(3):960-74 [Epub 2016 Feb 1].

The highly conserved yeast R2TP complex, consisting of Rvb1, Rvb2, Pih1, and Tah1, participates in diverse cellular processes ranging from assembly of protein complexes to apoptosis. Rvb1 and Rvb2 are closely related proteins belonging to the AAA+ superfamily and are essential for cell survival. Although Rvbs have been shown to be associated with various protein complexes including the Ino80 and Swr1chromatin remodeling complexes, we performed a systematic quantitative proteomic analysis of their associated proteins and identified two additional complexes that associate with Rvb1 and Rvb2: the chaperonin-containing T-complex and the 19S regulatory particle of the proteasome complex. We also analyzed Rvb1 and Rvb2 purified from yeast strains devoid of PIH1 and TAH1. These analyses revealed that both Rvb1 and Rvb2 still associated with Hsp90 and were highly enriched with RNA polymerase II complex components. Our analyses also revealed that both Rvb1 and Rvb2 were recruited to the Ino80 and Swr1 chromatin remodeling complexes even in the absence of Pih1 and Tah1 proteins. Using further biochemical analysis, we showed that Rvb1 and Rvb2 directly interacted with Hsp90 as well as with the RNA polymerase II complex. RNA-Seq analysis of the deletion strains compared with the wild-type strains revealed an up-regulation of ribosome biogenesis and ribonucleoprotein complex biogenesis genes, down-regulation of response to abiotic stimulus genes, and down-regulation of response to temperature stimulus genes. A Gene Ontology analysis of the 80 proteins whose protein associations were altered in the PIH1 or TAH1 deletion strains found ribonucleoprotein complex proteins to be the most enriched category. This suggests an important function of the R2TP complex in ribonucleoprotein complex biogenesis at both the proteomic and genomic levels. Finally, these results demonstrate that deletion network analyses can provide novel insights into cellular systems.​


176. Reshaping the Chromatin and Epigenetic Landscapes with Quantitative Mass Spectrometry

Washburn MP, Zhao Y, Garcia BA. (2016) Mol Cell Proteomics, 15(3):753-4.

The solving of the structure of DNA by Watson and Crick is arguably one of the major breakthroughs in all of science during the 20th century. Subsequent feverish research led to the discovery of a DNA genetic code, and that this code was utilized by living cells to encode proteins, the workhorse molecules of most cellular functions. The fundamental idea of genetic information transfer encoded by a DNA code has revolutionized the biological sciences and has led to the creation of new related fields and industries such as genomics, proteomics and biotechnology. However, not all gene expression patterns and subsequent phenotype changes can be explained by changes in this genetic code (DNA sequence). Epigenetics refers to stable heritable changes in gene expression that are not due to changes in DNA sequence, caused by DNA methylation, RNA interference and histone post-translational modifications (PTMs).​



175. ​​​​​​​​​​​​​​​​​​​Suganuma T, Swanson SK, Florens L, Washburn MP, Workman JL. (2015) Moco biosynthesis and the ATAC acetyltransferase engage translation initiation by inhibiting latent PKR activity. J Mol Cell Biol, 8(1):44-50. Abstract.

174. Khan MR, Li L, Perez-Sanchez C, Saraf A, Florens L, Slaughter B, Unruh J, Si K. (2015) Amyloidogenic Oligomerization Transforms Drosophila Orb2 from a Translation Repressor to an Activator. Cell, 163(6):1468-83. Abstract.

173. ​Li S, Swanson SK, Gogol M, Florens L, Washburn MP, Workman JL, Suganuma T (2015) Serine and SAM responsive complex SESAME regulates histone modification crosstalk by sensing cellular metabolism. Mol Cell, 60(3):408-21. Abstract.

172. Ouyang J, Yu W, Liu J, Zhang N, Florens L, Chen J, Liu H, Washburn MP, Pei D, Xie T. (2015) Cyclin-Dependent Kinase-Mediated Sox2 Phosphorylation Enhances the Ability of Sox2 to Establish the Pluripotent. J Biol Chem, [Epub 2015 Jul 2]. Abstract.

171. The H-Index of 'An Approach to Correlate Tandem Mass Spectral Data of Peptides with Amino Acid Sequences in a Protein Database'

Washburn MP. (2015) J Am Soc Mass Spectrom, 87(9):4749-56 [Epub 2015 Jun 20].

Over 20 years ago a remarkable paper was published in the Journal of American Society for Mass Spectrometry. This paper from Jimmy Eng, Ashley McCormack, and John Yates described the use of protein databases to drive the interpretation of tandem mass spectra of peptides. This paper now has over 3660 citations and continues to average more than 260 per year over the last decade. This is an amazing scientific achievement. The reason for this is the paper was a cutting edge development at the moment in time when genomes of organisms were being sequenced, protein and peptide mass spectrometry was growing into the field of proteomics, and the power of computing was growing quickly in accordance with Moore's law. This work by the Yates lab grew in importance as genomics, proteomics, and computation all advanced and eventually resulted in the widely used SEQUEST algorithm and platform for the analysis of tandem mass spectrometry data. This commentary provides an analysis of the impact of this paper by analyzing the citations it has generated and the impact of these citing papers.


170. Masuda Y, Takahashi H, Sato S, Tomomori-Sato C, Saraf A, Washburn MP, Florens L, Conaway RC, Conaway JW, Hatakeyama S. (2015) TRIM29 regulates the assembly of DNA repair proteins into damaged chromatin. Nature Commun 6:7299 [Epub 2015 Jun 22]. Abstract.

169. Watanabe S, Tan D, Lakshminarasimhan M, Washburn MP, Hong EJ, Walz T, Peterson CL. (2015) Structural analyses of the chromatin remodelling enzymes INO80-C and SWR-C. Nature Commun, 6:7108. Abstract.

168. Weems J, Slaughter BD, Unruh JR, Hall SM, McLaird MB, Gilmore JM, Washburn, MP, Florens L, Yasukawa T, Aso T, Conaway JW, Conaway RC. (2015) Assembly of the Elongin A Ubiquitin Ligase Is Regulated by Genotoxic and Other Stresses. J Biol Chem, 290(24):15030-41 [Epub 2015 Apr 15]. Abstract.

167. Yan J, Hao C, DeLucia M, Swanson S, Florens L, Washburn MP, Ahn J, Skowronski J. (2015) Cyclin A2 - CDK kinase regulates SAMHD1 phosphohydrolase domain. J Biol Chem, 290(21):13279-9 [Epub 2015 Apr 6]. Abstract.

166. Improving Label-Free Quantitative Proteomics Strategies by Distributing Shared Peptides and Stabilizing Variance

Zhang Y, Wen Z, Washburn MP, Florens L. (2015) Anal Chem, 87(9):4749-56 [Epub 2015 Apr 3].

In a previous study, we demonstrated that spectral counts based label-free proteomic quantitation could be improved by distributing peptides shared between multiple proteins. Here, we compare four quantitative proteomic approaches; namely the Normalized Spectral Abundance Factor (NSAF), the Normalized Area Abundance Factor (NAAF), Normalized Parent Ion Intensity Abundance Factor (NIAF), and the Normalized Fragment Ion Intensity Abundance Factor (NFAF). We demonstrate that label-free proteomic quantitation methods based on chromatographic peak area (NAAF), parent ion intensity in MS1 (NIAF), and fragment ion intensity (NFAF) are also improved when shared peptides are distributed based on peptides unique to each isoform. To stabilize the variance inherent to label-free proteomic quantitation datasets, we use cyclic-locally weighted scatter plot smoothing (LOWESS) and linear regression normalization (LRN). Again, all four methods are improved when cyclic-LOWESS and LRN are applied to reduce variation. Finally, we demonstrate that absolute quantitative values may be derived from label-free parameters such as spectral counts, chromatographic peak area, and ion intensity when using spiked-in proteins of known amounts to generate standard curves.


165. Skaar JR, Ferris AL, Wu X, Saraf A, Khanna KK, Florens L, Washburn MP, Hughes SH, Pagano M. (2015) The Integrator complex controls the termination of transcription at diverse classes of gene targets. Cell Research in press.

164. Proteins interacting with cloning scars: a source of false positive protein-protein interactions

Banks CAS, Boanca G, Lee ZT, Florens L, Washburn MP. (2015) Sci Rep, 5:8530 [Epub 2015 Feb 23].

A common approach for exploring the interactome, the network of protein-protein interactions in cells, uses a commercially available ORF library to express affinity tagged bait proteins; these can be expressed in cells and endogenous cellular proteins that copurify with the bait can be identified as putative interacting proteins using mass spectrometry. Control experiments can be used to limit false-positive results, but in many cases, there are still a surprising number of prey proteins that appear to copurify specifically with the bait. Here, we have identified one source of false-positive interactions in such studies. We have found that a combination of: 1) the variable sequence of the C-terminus of the bait with 2) a C-terminal valine "cloning scar" present in a commercially available ORF library, can in some cases create a peptide motif that results in the aberrant co-purification of endogenous cellular proteins. Control experiments may not identify false positives resulting from such artificial motifs, as aberrant binding depends on sequences that vary from one bait to another. It is possible that such cryptic protein binding might occur in other systems using affinity tagged proteins; this study highlights the importance of conducting careful follow-up studies where novel protein-protein interactions are suspected.


163. Luo Z, Gao X, Lin C, Smith E, Marshal S, Swanson SK, Florens L, Washburn MP, Shilatifard A. (2015) Zic2 is an enhancer-binding factor required for embryonic stem cell specification. Mol Cell, Feb 19;57(4):685-94. Abstract

162. Liang K, Gao X, Gilmore JM, Florens L, Washburn MP, Smith E, Shilatifard A. (2015) Characterization of human cyclin-dependent kinase 12 (CDK12) and CDK13 complexes in C-terminal domain phosphorylation, gene transcription, and RNA processing. Mol Cell Biol. 2015 Mar;35(6):928-38 [Epub 2015 Jan 5]. Abstract.

161. Pagan JK, Marzio A, Jones MJK, Saraf A, Jallepalli P, Florens L, Washburn MP, Pagano M. (2015) PLK1 and βTrCP-dependent degradation of Cep68 and PCNT cleavage mediate Cep215 removal from the PCM to allow centriole separation and disengagement/licensing. Nature Cell Biol. 17(1):31-43 [Epub 2014 Dec 15]. Abstract.


160. Quantitative Proteomics Characterization of Chromatin-Remodeling Complexes in Health and Disease. In: Systems Analysis of Chromatin-Related Protein Complexes in Cancer

Lakshminarasimhan M, Washburn MP. (2014) Springer Science+Business Media New York. pp. 177-196.

Recent advances in the fi eld of chromatin remodeling have elucidated its role in various cellular processes beyond transcription. The intricate dynamics and interplay between several chromatin-remodeling complexes has been shown to be responsible for events ranging from cell differentiation, epigenetic regulation, and human diseases. One of the biggest challenges in understanding the function of these large protein complexes is dissecting their assembly, interactions with other proteins, and identifying the role of individual components. Technological advances in quantitative proteomics make it one of the most sought after technique in identifying and analyzing multiprotein complexes and posttranslational modifi cations. In particular, multidimensional protein identifi cation technology and spectral counting based quantitative proteomic analysis is a popular choice for analyzing chromatin remodeling complexes. The reason being they are straightforward approaches and are able to identify and quantify low abundant proteins in a label-free manner. This chapter highlights the recent fi ndings of chromatin-remodeling complexes with respect to cellular processes and disease states and the role of quantitative proteomics has played in these findings.​

159. Conserved abundance and topological features in chromatin-remodeling protein interaction networks

Sardiu ME, Gilmore JM, Groppe BD, Herman D, Ramisetty SR, Cai Y, Jin J, Conaway RC, Conaway JW, Florens L, Washburn MP. (2014) EMBO Rep. Nov 26. pii: e201439403 [Epub ahead of print].

The study of conserved protein interaction networks seeks to better understand the evolution and regulation of protein interactions. Here, we present a quantitative proteomic analysis of 18 orthologous baits from three distinct chromatin-remodeling complexes in Saccharomyces cerevisiae and Homo sapiens. We demonstrate that abundance levels of orthologous proteins correlate strongly between the two organisms and both networks have highly similar topologies. We therefore used the protein abundances in one species to cross-predict missing protein abundance levels in the other species. Lastly, we identified a novel conserved low-abundance subnetwork further demonstrating the value of quantitative analysis of networks.


158. Dutta A, Gogol M, Kim JH, Smolle M, Venkatesh S, Gilmore J, Florens L, Washburn MP, Workman JL. (2014) Swi/Snf dynamics on stress-responsive genes is governed by competitive bromodomain interactions. Genes Dev. Oct 15;28(20):2314-30. doi: 10.1101/gad.243584.114. Abstract.

157. Banks CAS, Lakshminarasimhan M, Washburn MP. (2014) Shotgun Proteomics. eLS (Chichester, England: John Wiley & Sons, Ltd). Oct 15. doi:10.1002/9780470015902.a0006197.pub2. Abstract.

156. Hollopeter G, Lange JJ, Zhang Y, Vu TN, Gu M, Ailion M, Lambie EJ, Slaughter BD, Unruh JR, Florens L, Jorgensen EM. (2014) The membrane-associated proteins FCHo and SGIP are allosteric activators of the AP2 clathrin adaptor complex. Elife. Oct 10;3. doi: 10.7554/eLife.03648. Abstract.

155. Hewawasam GS, Mattingly M, Venkatesh S, Zhang Y, Florens L, Workman JL, Gerton JL. (2014) Phosphorylation by Casein Kinase 2 Facilitates Psh1 Protein-assisted Degradation of Cse4 Protein. J Biol Chem. Oct 17;289(42):29297-309. doi: 10.1074/jbc.M114.580589. Epub 2014 Sep 2. Abstract.

154. Herz HM, Morgan M, Gao X, Jackson J, Rickels R, Swanson SK, Florens L, Washburn MP, Eissenberg JC, Shilatifard A. (2014) Histone H3 lysine-to-methionine mutants as a paradigm to study chromatin signaling. Science. Aug 29;345(6200):1065-70. doi: 10.1126/science.1255104. Abstract.

153. Suberoylanilide Hydroxamic Acid (SAHA)-Induced Dynamics of a Human Histone Deacetylase Protein Interaction Network

Smith KT, Groppe BD, Gilmore JM, Saraf A, Egidy R, Peak A, Seidel CW, Florens L, Workman JL, Washburn MP. (2014) Mol Cell Proteomics. Nov;13(11):3114-25. doi: 10.1074/mcp.M113.037127. Epub 2014 Jul 29.

Histone deacetylases (HDACs) are targets for cancer therapy. Suberoylanilide hydroxamic acid (SAHA) is an HDAC inhibitor approved by the U.S. Food and Drug Administration for the treatment of cutaneous T-cell lymphoma. To obtain a better mechanistic understanding of the Sin3/HDAC complex in cancer, we extended its protein-protein interaction network and identified a mutually exclusive pair within the complex. We then assessed the effects of SAHA on the disruption of the complex network through six homologous baits. SAHA perturbs multiple protein interactions and therefore compromises the composition of large parts of the Sin3/HDAC network. A comparison of the effect of SAHA treatment on gene expression in breast cancer cells to a knockdown of the ING2 subunit indicated that a portion of the anticancer effects of SAHA may be attributed to the disruption of ING2's association with the complex. Our dynamic protein interaction network resource provides novel insights into the molecular mechanism of SAHA action and demonstrates the potential for drugs to rewire networks.


152. Controlling for gene expression changes in transcription factor protein networks

Banks CA, Lee ZT, Boanca G, Lakshminarasimhan M, Groppe BD, Wen Z, Hattem GL, Seidel CW, Florens L, Washburn MP. (2014) Mol Cell Proteomics. Jun;13(6):1510-22. doi: 10.1074/mcp.M113.033902. Epub 2014 Apr 10.

The development of affinity purification technologies combined with mass spectrometric analysis of purified protein mixtures has been used both to identify new protein-protein interactions and to define the subunit composition of protein complexes. Transcription factor protein interactions, however, have not been systematically analyzed using these approaches. Here, we investigated whether ectopic expression of an affinity tagged transcription factor as bait in affinity purification mass spectrometry experiments perturbs gene expression in cells, resulting in the false positive identification of bait-associated proteins when typical experimental controls are used. Using quantitative proteomics and RNA sequencing, we determined that the increase in the abundance of a set of proteins caused by overexpression of the transcription factor RelA is not sufficient for these proteins to then co-purify non-specifically and be misidentified as bait-associated proteins. Therefore, typical controls should be sufficient, and a number of different baits can be compared with a common set of controls. This is of practical interest when identifying bait interactors from a large number of different baits. As expected, we found several known RelA interactors enriched in our RelA purifications (NFκB1, NFκB2, Rel, RelB, IκBα, IκBβ, and IκBε). We also found several proteins not previously described in association with RelA, including the small mitochondrial chaperone Tim13. Using a variety of biochemical approaches, we further investigated the nature of the association between Tim13 and NFκB family transcription factors. This work therefore provides a conceptual and experimental framework for analyzing transcription factor protein interactions.


151. Ryu HW, Lee DH, Florens L, Swanson SK, Washburn MP, Kwon SH. (2014) Analysis of the heterochromatin protein 1 (HP1) interactome in Drosophila. J Proteomics. May 6;102:137-47. doi: 10.1016/j.jprot.2014.03.016. Epub 2014 Mar 25. Abstract.

150. Lu S, Lee KK, Harris B, Xiong B, Bose T, Saraf A, Hattem G, Florens L, Seidel C, Gerton JL. (2014) The cohesin acetyltransferase Eco1 coordinates rDNA replication and transcription. EMBO Rep. May 1;15(5):609-17. doi: 10.1002/embr.201337974. Epub 2014 Mar 14. Abstract.

149. Harris B, Bose T, Lee KK, Wang F, Lu S, Ross RT, Zhang Y, French SL, Beyer AL, Slaughter BD, Unruh JR, Gerton JL. (2014) Cohesion promotes nucleolar structure and function. Mol Biol Cell. Feb;25(3):337-46. doi: 10.1091/mbc.E13-07-0377. Epub 2013 Dec 4. Abstract.

148. White-Grindley E, Li L, Mohammad Khan R, Ren F, Saraf A, Florens L, Si K. (2014) Contribution of Orb2A stability in regulated amyloid-like oligomerization of Drosophila Orb2. PLoS Biol. Feb 11;12(2):e1001786. doi: 10.1371/journal.pbio.1001786. eCollection 2014 Feb. Abstract.

147. Mohan RD, Dialynas G, Weake VM, Liu J, Martin-Brown S, Florens L, Washburn MP, Workman JL, Abmayr SM. (2014) Loss of Drosophila Ataxin-7, a SAGA subunit, reduces H2B ubiquitination and leads to neural and retinal degeneration. Genes Dev. Feb 1;28(3):259-72. doi: 10.1101/gad.225151.113. Abstract.

146. Renaud M, Praz V, Vieu E, Florens L, Washburn MP, l'Hôte P, Hernandez N. (2014) Gene duplication and neofunctionalization: POLR3G and POLR3GL. Genome Research, Jan;24(1):37-51. doi: 10.1101/gr.161570.113. Epub 2013 Oct 9. Abstract.

145. Cervantes S, Bunnik EM, Saraf A, Conner CM, Escalante A, Sardiu ME, Ponts N, Prudhomme J, Florens L, Le Roch KG. (2014) The multifunctional autophagy pathway in the human malaria parasite, Plasmodium falciparum. Autophagy, Jan;10(1):80-92. doi: 10.4161/auto.26743. Epub 2013 Nov 11. Abstract.


144. Bunnik EM, Chung DW, Hamilton M, Ponts N, Saraf A, Prudhomme J, Florens L, Le Roch KG. (2013) Polysome profiling reveals translational control of gene expression 1 in the human malaria parasite Plasmodium falciparum. Genome Biology, Nov 22;14(11):R128 [Epub ahead of print]. Abstract.

143. Yates JR 3rd & Washburn MP. (2013) Quantitative Proteomics. Anal Chem, 85(19):8881. doi: 10.1021/ac402745w. Abstract.

142. Liu J, Wetzel L, Zhang Y, Nagayasu E, Ems-McClung S, Florens L, Hu K. (2013) Novel thioredoxin like proteins are components of a protein complex coating the cortical microtubules of Toxoplasma gondii. Eukaryotic Cell, Dec;12(12):1588-99. doi: 10.1128/EC.00082-13. Epub 2013 Jul 19. Abstract.

141. Hu D, Smith ER, Garruss AS, Mohaghegh N, Varberg JM, Lin C, Jackson J, Gao X, Saraf A, Florens L, Washburn MP, Eissenberg JC, Shilatifard A. (2013) The Little Elongation Complex Functions at Initiation and Elongation Phases of snRNA Gene Transcription. Mol Cell, Aug 22;51(4):493-505. Epub 2013 Aug 8. Abstract.

140. Mellacheruvu D, Wright Z, Couzens AL, Lambert JP, St-Denis NA, Li T, Miteva YV, Hauri S, Sardiu ME, Low TY, Halim VA, Bagshaw RD, Hubner NC, Al-Hakim A, Bouchard A, Faubert D, Fermin D, Dunham WH, Goudreault M, Lin ZY, Badillo BG, Pawson T, Durocher D, Coulombe B, Aebersold R, Superti-Furga G, Colinge J, Heck AJ, Choi H, Gstaiger M, Mohammed S, Cristea IM, Bennett KL, Washburn MP, Raught B, Ewing RM, Gingras AC, Nesvizhskii AI. (2013) The CRAPome: a contaminant repository for affinity purification-mass spectrometry data. Nat Methods, Aug;10(8):730-6. Epub 2013 Jul 7. Abstract.

139. Sela D, Conkright JJ, Chen L, Gilmore J, Washburn MP, Florens L, Conaway RC, Conaway JW. (2013) Role for Human Mediator Subunit MED25 in Recruitment of Mediator to Promoters by Endoplasmic Reticulum Stress-responsive Transcription Factor ATF6α. J Biol Chem, Sep 6;288(36):26179-87. Epub 2013 Jul 17. Abstract.

138. Cox JL, Wilder PJ, Gilmore JM, Wuebben EL, Washburn MP, Rizzino A. (2013) The SOX2-interactome in brain cancer cells identifies the requirement of MSI2 and USP9X for the growth of brain tumor cells. PLoS One. PLoS One, May 7;8(5):e62857. Abstract.

137. Kuchay S, Duan S, Schenkein E, Peschiaroli A, Saraf A, Florens L, Washburn MP, Pagano M. (2013) FBXL2- and PTPL1-mediated degradation of p110-free p85β regulatory subunit controls the PI(3)K signalling cascade. Nat Cell Biol, May;15(5):472-80. Epub 2013 Apr 21. Abstract.

136. Bonner AM, Hughes SE, Chisholm JA, Smith SK, Slaughter BD, Unruh JR, Collins KA, Friederichs JM, Florens L, Swanson SK, Pelot MC, Miller DE, Washburn MP, Jaspersen SL, Hawley RS. (2013) Binding of Drosophila Polo kinase to its regulator Matrimony is noncanonical and involves two separate functional domains. Proc Natl Acad Sci U S A, Mar 26;110(13):E1222-31. Epub 2013 Mar 11. Abstract.

135. Rossi M, Duan S, Jeong YT, Horn M, Saraf A, Florens L, Washburn MP, Antebi A, Pagano M. (2013) Regulation of the CRL4(Cdt2) Ubiquitin Ligase and Cell-Cycle Exit by the SCF(Fbxo11) Ubiquitin Ligase. Mol Cell, Mar 28;49(6):1159-66. Epub 2013 Mar 7. Abstract.

134. Mosley AL, Hunter GO, Sardiu M, Smolle ME, Workman JL, Florens L, Washburn MP. (2013) Quantitative Proteomics Demonstrates that the RNA Polymerase II Subunits Rpb4 and Rpb7 Dissociate During Transcription Elongation. Mol Cell Proteomics, Feb 15. Jun;12(6):1530-8. Epub 2013 Feb 15. Abstract.

133. Jeong YT, Rossi M, Cermak L, Saraf A, Florens L, Washburn MP, Sung P, Schildkraut C, Pagano M. (2013) FBH1 promotes DNA double-strand breakage and apoptosis in response to DNA replication stress. J Cell Biol, Jan 21;200(2):141-9. Abstract.


132. Fine DA, Rozenblatt-Rosen O, Padi M, Korkhin A, James RL, Adelmant G, Yoon R, Guo L, Berrios C, Zhang Y, Calderwood MA, Velmurgan S, Cheng J, Marto JA, Hill DE, Cusick ME, Vidal M, Florens L, Washburn MP, Litovchick L, DeCaprio JA. (2012) Identification of FAM111A as an SV40 host range restriction and adenovirus helper factor. PLoS Pathog, 2012;8(10):e1002949. Abstract.

131. Banks CA, Kong SE, Washburn MP. (2012) Affinity purification of protein complexes for analysis by multidimensional protein identification technology. Protein Expr Purif, 2012 Dec;86(2):105-19. Abstract.

130. Korfali N, Wilkie GS, Swanson SK, Srsen V, de Las Heras J, Batrakou DG, Malik P, Zuleger N, Kerr AR, Florens L, Schirmer EC. (2012) The nuclear envelope proteome differs notably between tissues. Nucleus, Nov-Dec;3(6):552-64. Abstract.

129. Smith KT, Sardiu ME, Martin-Brown SA, Seidel C, Mushegian A, Egidy R, Florens L, Washburn MP, Workman JL. (2012) Human family with sequence similarity 60 member A (FAM60A) protein: a new subunit of the Sin3 deacetylase complex. Mol Cell Proteomics, Dec;11(12):1815-28. Abstract.

128. Smolle M, Venkatesh S, Gogol MM, Li H, Zhang Y, Florens L, Washburn MP, Workman JL. (2012) Chromatin remodelers Isw1 and Chd1 maintain chromatin structure during transcription by preventing histone exchange. Nat Struct Mol Biol, Sep;19(9):884-92. Epub 2012 Aug 26. Abstract.

127. D'Angiolella V, Donato V, Forrester FM, Jeong Y-T, Kudo Y, Pellacani C, Saraf A, Florens L, Washburn MP, Pagano M. (2012) The Cyclin F-Ribonucleotide Reductase M2 axis controls genome integrity and DNA repair. Cell, 149:1023-1034. Abstract.

126. Sela D, Chen L, Martin-Brown S, Washburn MP, Florens L, Conaway JW, Conaway RC. (2012) Endoplasmic Reticulum Stress-Responsive Transcription Factor ATF6alpha Directs Recruitment of the Mediator of RNA Polymerase II Transcription and Multiple Histone Acetyltransferase Complexes. J Biol Chem, Jun 29;287(27):23035-45. Epub 2012 May 10. Abstract.

125. Luo Z, Lin C, Guest E, Garrett AS, Mohaghegh N, Swanson S, Marshall S, Florens L, Washburn MP, Shilatifard A. (2012) The super elongation complex family of RNA polymerase II elongation factors: gene target specificity and transcriptional output. Mol Cell Biol, Jul;32(13):2608-17. Epub 2012 Apr 30. Abstract.

124. Yang H, Zhang Y, Vallandingham J, Li H, Florens L, Mak HY. (2012) The RDE-10/RDE-11 complex triggers RNAi induced mRNA degradation by association with target mRNA in C. elegans. Genes Dev, Apr 15;26(8):846-56. Abstract.

123. Characterization of a highly conserved histone related protein, Ydl156w, and its functional associations using quantitative proteomic analyses.

Gilmore JM, Sardiu ME, Venkatesh S, Stutzman B, Peak A, Seidel CW, Workman JL, Florens L, Washburn MP. (2012) Mol Cell Proteomics. Apr;11(4):M111.011544.

A significant challenge in biology is to functionally annotate novel and uncharacterized proteins. Several approaches are available for deducing the function of proteins in silico based upon sequence homology and physical or genetic interaction, yet this approach is limited to proteins with well-characterized domains, paralogs and/or orthologs in other species, as well as on the availability of suitable large-scale data sets. Here, we present a quantitative proteomics approach extending the protein network of core histones H2A, H2B, H3 and H4 in S. cerevisiae, among which a novel associated protein, the previously uncharacterized Ydl156w, was identified. In order to predict the role of Ydl156w, we designed and applied integrative bioinformatics, quantitative proteomics and biochemistry approaches aiming to infer its function. Reciprocal analysis of Ydl156w protein interactions demonstrated a strong association with all four histones and also to proteins strongly associated with histones including Rim1, Rfa2 and 3, Yku70, and Yku80. Through a subsequent combination of the focused quantitative proteomics experiments with available large-scale genetic interaction data and Gene Ontology functional associations, we provided sufficient evidence to associate Ydl156w with multiple processes including chromatin remodeling, transcription and DNA repair/replication. To gain deeper insights into the role of Ydl156w in histone biology we investigated the effect of the genetic deletion of ydl156w on H4 associated proteins, which lead to a dramatic decrease in the association of H4 with RNA polymerase III proteins. The implication of a role for Ydl156w in RNA Polymerase III mediated transcription was consequently verified by RNA-Seq experiments. Finally, using these approaches we generated a refined network of Ydl156w-associated proteins.

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122. Examining the complexity of human RNA polymerase complexes using HaloTag technology coupled to label free quantitative proteomics.

Daniels DL, Méndez J, Mosley AL, Ramisetty SR, Murphy N, Benink H, Wood KV, Urh M, Washburn MP. (2012) J Proteome Res. Feb 3;11(2):564-75.

Efficient determination of protein interactions and cellular localization remains a challenge in higher order eukaryotes and creates a need for robust technologies for functional proteomics studies. To address this, the HaloTag technology was developed for highly efficient and rapid isolation of intracellular complexes and correlative in vivo cellular imaging. Here we demonstrate the strength of this technology by simultaneous capture of human eukaryotic RNA polymerases (RNAP) I, II, and III using a shared subunit, POLR2H, fused to the HaloTag. Affinity purifications showed successful isolation, as determined using quantitative proteomics, of all RNAP core subunits, even at expression levels near endogenous. Transient known RNAP II interacting partners were identified as well as three previously uncharacterized interactors. These interactions were validated and further functionally characterized using cellular imaging. The multiple capabilities of the HaloTag technology demonstrate the ability to efficiently isolate highly challenging multi-protein complexes, discover new interactions, and characterize cellular localization.


121. Takeo S, Swanson SK, Nandanan K, Nakai Y, Aigaki T, Washburn MP, Florens L, Hawley RS. (2012) Shaggy/glycogen synthase kinase 3beta and phosphorylation of Sarah/regulator of calcineurin are essential for completion of Drosophila female meiosis. PNAS, Apr 24;109(17):6382-9. Abstract.

120. Steinberg XP, Hepp MI, Fernandez Garcia Y, Suganuma T, Swanson SK, Washburn M, Workman JL, Gutierrez JL. (2012) Human CCAAT/Enhancer-Binding Protein beta Interacts with Chromatin Remodeling Complexes of the Imitation Switch Subfamily. Biochemistry, Feb 7;51(5):952-62. Abstract.

119. Suganuma T, Mushegian A, Swanson SK, Florens L, Washburn MP, Workman JL. (2012) A metazoan ATAC acetyltransferase subunit that regulates MAP kinase signaling is related to an ancient molybdopterin synthase component. Mol Cell Proteomics, May;11(5):90-9. Abstract.

118. Herz HM, Mohan M, Garrett A, Miller C, Casto D, Zhang Y, Seidel C, Haug J, Florens L, Washburn MP, Yamaguchi M, Shiekhattar R, Shilatifard A. (2012) Polycomb repressive complex 2-dependent and -independent functions of Jarid2 in transcriptional regulation in Drosophila. Mol Cell Biol, May;32(9):1683-93. Abstract.

117. Zhang DW, Mosley AL, Ramisetty SR, Rodríguez-Molina JB, Washburn MP, Ansari AZ. (2012) Ssu72 phosphatase-dependent erasure of phospho-Ser7 marks on the RNA polymerase II C-terminal domain is essential for viability and transcription termination. J Biol Chem, Mar 9;287(11):8541-51. Abstract.

116. Gao Z, Cox JL, Gilmore JM, Ormsbee BD, Mallanna SK, Washburn MP, Rizzino A. (2012) Determination of the protein interactome of the transcription factor Sox2 in embryonic stem cells engineered for inducible expression of four reprogramming factors. J Biol Chem, Feb 9. Abstract.


115. Unraveling the ubiquitome of the human malaria parasite.

Ponts N*, Saraf A*, Chung D-WD, Harris A, Prudhomme J, Washburn MP, Florens L, & Le Roch KG. (2011) J Biol Chem. 286(46):40320-30.

Malaria is one of the deadliest infectious diseases worldwide. The most severe form is caused by the eukaryotic protozoan parasite Plasmodium falciparum. Recent studies have highlighted the importance of post-translational regulations for the parasite's progression throughout its life cycle, protein ubiquitylation being certainly one of the most abundant. The specificity of its components and the wide range of biological processes in which it is involved make the ubiquitylation pathway a promising source of suitable targets for anti-malarial drug development. Here, we combined immunofluorescent microscopy, biochemical assays, in silico prediction, and mass spectrometry analysis using the multidimensional protein identification technology, or MudPIT, to describe the P. falciparum ubiquitome. We found that ubiquitin conjugates are detected at every morphological stage of the parasite erythrocytic cycle. Furthermore, we detected that more than half of the parasite's proteome represents possible targets for ubiquitylation, especially proteins found to be present at the most replicative stage of the asexual cycle, the trophozoite stage. A large proportion of ubiquitin conjugates were also detected at the schizont stage, consistent with a cell activity slowdown to prepare for merozoite differentiation and invasion. Finally, for the first time in the human malaria parasite, our results strongly indicate the presence of heterologous mixed conjugations, SUMO/UB. This discovery suggests that sumoylated proteins may be regulated by ubiquitylation in P. falciparum. Altogether, our results present the first stepping stone toward a better understanding of ubiquitylation and its role(s) in the biology of the human malaria parasite.


114. Improving Proteomics Mass Accuracy by Dynamic Offline Lock Mass.

Zhang Y, Wen Z, Washburn MP, Florens L. (2011) Anal Chem. Dec 15;83(24):9344-51.

Several methods to obtain low-ppm mass accuracy have been described. In particular, online or offline lock mass approaches can use background ions, produced by electrospray under ambient conditions, as calibrants. However, background ions such as protonated and ammoniated polydimethylcyclosiloxane ions have relatively weak and fluctuating intensity. To address this issue, we implemented dynamic offline lock mass (DOLM). Within every MS1 survey spectrum, DOLM dynamically selected the strongest n background ions for statistical treatments and m/z recalibration. We systematically optimized the mass profile abstraction method to find one single m/z value to represent an ion and the number of calibrants. To assess the influence of the intensity of the analyte ions, we used tandem mass spectroscopy (MS/MS) datasets obtained from MudPIT analyses of two protein samples with different dynamic ranges. DOLM outperformed both external mass calibration and offline lock mass that used predetermined calibrant ions, especially in the low-ppm range. The unique dynamic feature of DOLM was able to adapt to wide variations in calibrant intensities, leading to averaged mass error center at 0.03 ± 0.50 ppm for precursor ions. Such consistently tight mass accuracies meant that a precursor mass tolerance as low as 1.5 ppm could be used to search or filter post-search DOLM-recalibrated MS/MS datasets.

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113. Smith ER, Lin C, Garrett AS, Thornton J, Mohaghegh N, Hu D, Jackson J, Saraf A, Swanson SK, Seidel C, Florens L, Washburn MP, Eissenberg JC, Shilatifard A. (2011) The little elongation complex regulates small nuclear RNA transcription. Mol Cell, Dec 23;44(6):954-65. Abstract.

112. Mohan M, Herz HM, Smith ER, Zhang Y, Jackson J, Washburn MP, Florens L, Eissenberg JC, Shilatifard A. (2011) The COMPASS family of H3K4 methylases in Drosophila. Mol Cell Biol, Nov;31(21):4310-8. Abstract.

111. Construction of protein interaction networks based on the label-free quantitative proteomics.

Sardiu ME, Washburn MP. (2011) Methods Mol Biol. 2011;781:71-85.

Multiprotein complexes are essential building blocks for many cellular processes in an organism. Taking the process of transcription as an example, the interplay of several chromatin-remodeling complexes is responsible for the tight regulation of gene expression. Knowing how those proteins associate into protein complexes not only helps to improve our understanding of these cellular processes, but can also lead to the discovery of the function of novel interacting proteins. Given the large number of proteins with little to no functional annotation throughout many organisms, including human, the identification and characterization of protein complexes has grown into a major focus of network biology. Toward this goal, we have developed several computational approaches based upon label-free quantitative proteomics approaches for the analysis of protein complexes and protein interaction networks. Here, we describe the computational approaches used to build probabilistic protein interaction networks, which are detailed in this chapter using the example of complexes involved in chromatin remodeling and transcription.


110. Costessi A, Mahrour N, Tijchon E, Stunnenberg R, Stoel MA, Jansen PW, Sela D, Martin-Brown S, Washburn MP, Florens L, Conaway JW, Conaway RC, Stunnenberg HG. (2011) The tumor antigen PRAME is a subunit of a Cul2 ubiquitin ligases and associates with active NFY promoters. EMBO J., Aug 5;30(18):3786-98. Abstract

109. Weake VM, Dyer JO, Seidel C, Box A, Swanson SK, Peak A, Florens L, Washburn MP, Abmayr SM, Workman JL. (2011) Post-transcription initiation function of the ubiquitous SAGA complex in tissue-specific gene activation. Genes Dev, Jul 15;25(14):1499-509. Abstract.
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108. Combinatorial Depletion Analysis to Assemble the Network Architecture of the SAGA and ADA Chromatin Remodeling Complexes

Lee KK, Sardiu ME, Swanson SK, Gilmore JM, Torok M, Grant PA, Florens L, Workman JL, Washburn MP. (2011) Mol. Syst. Biol., Jul 5;7:503.

Despite the availability of several large-scale proteomics studies aiming to identify protein interactions on a global scale, little is known about how proteins interact and are organized within macromolecular complexes. Here, we describe a technique that consists of a combination of biochemistry approaches, quantitative proteomics and computational methods using wild-type and deletion strains to investigate the organization of proteins within macromolecular protein complexes. We applied this technique to determine the organization of two well-studied complexes, Spt–Ada–Gcn5 histone acetyltransferase (SAGA) and ADA, for which no comprehensive high-resolution structures exist. This approach revealed that SAGA/ADA is composed of five distinct functional modules, which can persist separately. Furthermore, we identified a novel subunit of the ADA complex, termed Ahc2, and characterized Sgf29 as an ADA family protein present in all Gcn5 histone acetyltransferase complexes. Finally, we propose a model for the architecture of the SAGA and ADA complexes, which predicts novel functional associations within the SAGA complex and provides mechanistic insights into phenotypical observations in SAGA mutants.

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107. Takahashi H, Parmely TJ, Sato S, Tomomori-Sato C, Banks CA, Kong SE, Szutorisz H, Swanson SK, Martin-Brown S, Washburn MP, Florens L, Seidel CW, Lin C, Smith ER, Shilatifard A, Conaway RC, Conaway JW. (2011) Human Mediator subunit MED26 functions as a docking site for transcription elongation factors. Cell, Jul 8;146(1):92-104. Abstract

106. Hrecka K, Hao C, Gierszewska M, Swanson SK, Kesik-Brodacka M, Srivastava S, Florens L, Washburn MP, Skowronski J. (2011) Vpx relieves inhibition of HIV-1 infection of macrophages mediated by the SMAHD1 protein. Nature, Jun 29;474(7353):658-61. Abstract.
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105. Hao Y, Xu N, Box AC, Schaefer L, Kannan K, Zhang Y, Florens L, Seidel C, Washburn MP, Wiegraebe W, Mak HY. (2011) Nuclear cGMP dependent kinase regulates gene expression via activity dependent recruitment of a conserved histone deacetylase complex. PLoS Genetics, May;7(5):e1002065. Epub 2011 May 5. Abstract

104. Litovchick L, Florens LA, Swanson SK, Washburn MP, DeCaprio JA. (2011) DYRK1A protein kinase promotes quiescence and senescence through DREAM complex assembly. Genes Dev., Apr 15;25(8):801-13. Abstract.
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103. Building Protein-Protein Interaction Networks with Proteomic and Informatics Tools

Sardiu ME, Washburn MP. (2011) J Biol Chem, Jul 8;286(27):23645-51. Epub 2011 May 12.

The systematic characterization of the whole interactomes of different model organisms has revealed that the eukaryotic proteome is highly interconnected. Therefore, biological research is progressively shifting away from classical approaches that focus only on a few proteins toward whole protein interaction networks to describe the relationship of proteins in biological processes. In this minireview, we survey the most common methods for the systematic identification of protein interactions and exemplify different strategies for the generation of protein interaction networks. In particular, we will focus on the recent development of protein interaction networks derived from quantitative proteomics data sets.


102. Unraveling the dynamics of protein interactions with quantitative mass spectrometry

Ramisetty SR, Washburn MP. (2011) Crit Rev Biochem Mol Biol, Jun;46(3):216-28. Epub 2011 Mar 26.

Knowledge of structure and dynamics of proteins and protein complexes is important to unveil the molecular basis and mechanisms involved in most biological processes. Protein complex dynamics can be defined as the changes in the composition of a protein complex during a cellular process. Protein dynamics can be defined as conformational changes in a protein during enzyme activation, for example, when a protein binds to a ligand or when a protein binds to another protein. Mass spectrometry (MS) combined with affinity purification has become the analytical tool of choice for mapping protein-protein interaction networks and the recent developments in the quantitative proteomics field has made it possible to identify dynamically interacting proteins. Furthermore, hydrogen/deuterium exchange MS is emerging as a powerful technique to study structure and conformational dynamics of proteins or protein assemblies in solution. Methods have been developed and applied for the identification of transient and/or weak dynamic interaction partners and for the analysis of conformational dynamics of proteins or protein complexes. This review is an overview of existing and recent developments in studying the overall dynamics of in vivo protein interaction networks and protein complexes using MS-based methods.


101. Driving biochemical discovery with quantitative proteomics

Washburn MP. (2011) Trends Biochem Sci, Mar;36(3):170-7. Epub 2010 Sep 27.

Proteomic analysis of biological samples plays an increasing role in modern research. Although the application of proteomics technologies varies across many disciplines, proteomics largely is a tool for discovery that then leads to novel hypotheses. In recent years, new methods and technologies have been developed and applied in many areas of proteomics, and there is a strong push towards using proteomics in a quantitative manner. Indeed, mass spectrometry-based, quantitative proteomics approaches have been applied to great success in a variety of biochemical studies. In particular, the use of quantitative proteomics provides new insights into protein complexes and post-translational modifications and leads to the generation of novel insights into these important biochemical systems.


100. Highly reproducible label free quantitative proteomic analysis of RNA polymerase complexes

Mosley AL, Sardiu ME, Pattenden SG, Workman JL, Florens L, Washburn MP. (2011) Mol. Cell. Proteomics, Feb;10(2):M110.000687. Epub 2010 Nov 3.

The use of quantitative proteomics methods to study protein complexes has the potential to provide in-depth information on the abundance of different protein components as well as their modification state in various cellular conditions. To interrogate protein complex quantitation using shotgun proteomic methods, we have focused on the analysis of protein complexes using label-free multidimensional protein identification technology and studied the reproducibility of biological replicates. For these studies, we focused on three highly related and essential multi-protein enzymes, RNA polymerase I, II, and III from Saccharomyces cerevisiae. We found that label-free quantitation using spectral counting is highly reproducible at the protein and peptide level when analyzing RNA polymerase I, II, and III. In addition, we show that peptide sampling does not follow a random sampling model, and we show the need for advanced computational models to predict peptide detection probabilities. In order to address these issues, we used the APEX protocol to model the expected peptide detectability based on whole cell lysate acquired using the same multidimensional protein identification technology analysis used for the protein complexes. Neither method was able to predict the peptide sampling levels that we observed using replicate multidimensional protein identification technology analyses. In addition to the analysis of the RNA polymerase complexes, our analysis provides quantitative information about several RNAP associated proteins including the RNAPII elongation factor complexes DSIF and TFIIF. Our data shows that DSIF and TFIIF are the most highly enriched RNAP accessory factors in Rpb3-TAP purifications and demonstrate our ability to measure low level associated protein abundance across biological replicates. In addition, our quantitative data supports a model in which DSIF and TFIIF interact with RNAPII in a dynamic fashion in agreement with previously published reports.

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99. Chen L, Cai Y, Jin J, Florens L, Swanson SK, Washburn MP, Conaway JW, Conaway RC. (2011) Subunit organization of the human INO80 chromatin remodeling complex: an evolutionary conserved core complex catalyzes ATP-dependent nucleosome remodeling. J. Biol. Chem. Apr 1;286(13):11283-9. Epub 2011 Feb 8. Abstract

98. Wilkie GS, Korfali N, Swanson SK, Malik P, Srsen V, Batrakou DG, de las Heras J, Zuleger N, Kerr AR, Florens L, Schirmer EC. (2011) Several novel nuclear envelope transmembrane proteins identified in skeletal muscle have cytoskeletal associations. Mol. Cell. Proteomics, Jan;10(1):M110.003129. Epub 2010 Sep 27. Abstract.
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97. Berdygulova Z, Westblade LF, Florens L, Koonin EV, Chait BT, Ramanculov E, Washburn MP, Darst SA, Severinov K, Minakhin L. (2011) Temporal regulation of gene expression of the Thermus thermophilus bacteriophage P23-45. J. Mol. Biol., Jan 7;405(1):125-42. Epub 2010 Nov 2. Abstract

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96. Enriching quantitative proteomics with SIN

Sardiu ME, Washburn MP. (2010) Nature Biotechnol., 28, 40-2.

A new metric called the normalized spectral index (SIN) provides a simple way to quantify and compare label-free proteomics data.


95. Advances in shotgun proteomics and the analysis of membrane proteomes

Gilmore JM, Washburn MP. (2010) , J. Proteomics, Oct 10;73(11):2078-91. Epub 2010 Aug 23.

The emergence of shotgun proteomics has facilitated the numerous biological discoveries made by proteomic studies. However, comprehensive proteomic analysis remains challenging and shotgun proteomics is a continually changing field. This review details the recent developments in shotgun proteomics and describes emerging technologies that will influence shotgun proteomics going forward. In addition, proteomic studies of integral membrane proteins remain challenging due to the hydrophobic nature in integral membrane proteins and their general low abundance levels. However, there have been many strategies developed for enriching, isolating and separating membrane proteins for proteomic analysis that have moved this field forward. In summary, while shotgun proteomics is a widely used and mature technology, the continued pace of improvements in mass spectrometry and proteomic technology and methods indicate that future studies will have an even greater impact on biological discovery.


94. Refinements to Label Free Proteome Quantitation: How to Deal with Peptides Shared by Multiple Proteins

Zhang Y, Wen Z, Washburn MP, Florens L. (2010) . Anal. Chem., Mar 15;82(6):2272-81.

Quantitative shotgun proteomics is dependent on the detection, identification, and quantitative analysis of peptides. An issue arises with peptides that are shared between multiple proteins. What protein did they originate from and how should these shared peptides be used in a quantitative proteomics workflow? To systematically evaluate shared peptides in label-free quantitative proteomics, we devised a well-defined protein sample consisting of known concentrations of six albumins from different species, which we added to a highly complex yeast lysate. We used the spectral counts based normalized spectral abundance factor (NSAF) as the starting point for our analysis and compared an exhaustive list of possible combinations of parameters to determine what was the optimal approach for dealing with shared peptides and shared spectral counts. We showed that distributing shared spectral counts based on the number of unique spectral counts led to the most accurate and reproducible results.

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93. Delayed Correlation of mRNA and Protein Expression in Rapamycin Treated Cells and a Role for Ggc1 in Cellular Sensitivity to Rapamycin

Fournier ML, Paulson A, Pavelka N, Mosley AL, Gaudenz K, Bradford WD, Glynn E, Li H, Sardiu ME, Fleharty B, Seidel C, Florens L, Washburn MP. (2010) Mol. Cell. Proteomics, Feb;9(2):271-84. Epub 2009 Nov 10.

To identify new molecular targets of rapamycin, an anticancer and immunosuppressive drug, we analyzed temporal changes in yeast over 6 h in response to rapamycin at the transcriptome and proteome levels and integrated the expression patterns with functional profiling. We show that the integration of transcriptomics, proteomics, and functional data sets provides novel insights into the molecular mechanisms of rapamycin action. We first observed a temporal delay in the correlation of mRNA and protein expression where mRNA expression at 1 and 2 h correlated best with protein expression changes after 6 h of rapamycin treatment. This was especially the case for the inhibition of ribosome biogenesis and induction of heat shock and autophagy essential to promote the cellular sensitivity to rapamycin. However, increased levels of vacuolar protease could enhance resistance to rapamycin. Of the 85 proteins identified as statistically significantly changing in abundance, most of the proteins that decreased in abundance were correlated with a decrease in mRNA expression. However, of the 56 proteins increasing in abundance, 26 were not correlated with an increase in mRNA expression. These protein changes were correlated with unchanged or down-regulated mRNA expression. These proteins, involved in mitochondrial genome maintenance, endocytosis, or drug export, represent new candidates effecting rapamycin action whose expression might be post-transcriptionally or post-translationally regulated. We identified GGC1, a mitochondrial GTP/GDP carrier, as a new component of the rapamycin/target of rapamycin (TOR) signaling pathway. We determined that the protein product of GGC1 was stabilized in the presence of rapamycin, and the deletion of the GGC1 enhanced growth fitness in the presence of rapamycin. A dynamic mRNA expression analysis of Deltaggc1 and wild-type cells treated with rapamycin revealed a key role for Ggc1p in the regulation of ribosome biogenesis and cell cycle progression under TOR control.


92. Cai Y, Jin J, Swanson SK, Cole MD, Choi SH, Florens L, Washburn MP, Conaway JW, Conaway RC. (2010) Subunit Composition and Substrate Specificity of a MOF-containing Histone Acetyltransferase Distinct From the Male-Specific Lethal (MSL) Complex. J. Biol. Chem. Feb 12;285(7):4268-72. Epub 2009 Dec 14. Abstract

91. Hewawasam G, Shivaraju M, Mattingly M, Venkatesh S, Martin-Brown S, Florens L, Workman JL, Gerton JL. (2010) Psh1 is an E3 ubiquitin ligase that targets Cse4 for proteolysis. Mol. Cell, Nov 12;40(3):444-54. Abstract

90. Pavelka N, Rancati G, Zhu J, Bradford WD, Saraf A, Florens L, Sanderson BW, Hattem GL, Li R. (2010) Aneuploidy confers quantitative proteome changes and phenotypic variation in budding yeast. Nature, Nov 11;468(7321):321-5. Epub 2010 Oct 20. Abstract.
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89. Mohan M, Herz HM, Takahashi YH, Lin C, Lai KC, Zhang Y, Washburn MP, Florens L, Shilatifard A. (2010) Linking H3K79 trimethylation to Wnt signaling through a novel Dot1 containing complex (DotCom). Genes Dev., Mar 15;24(6):574-89. Epub 2010 Mar 4. Abstract

88. Lin C, Smith ER, Takahashi H, Lai KC, Martin-Brown S, Florens L, Washburn MP, Conaway JW, Conaway RC, Shilatifard A. (2010) AFF4 is a core component of the RNA polymerase II elongation complex and a shared subunit of the MLL-chimeras: Linking transcription elongation to human leukemia. Mol. Cell, Feb 12;37(3):429-37. Abstract

87. Kim JH, Saraf A, Florens L, Washburn M, Workman JL. (2010) Gcn5 regulates the dissociation of SWI/SNF from chromatin by acetylation of Swi2/Snf2. Genes Dev., Dec 15;24(24):2766-71. Abstract

86. Kwon SH, Florens L, Swanson SK, Washburn MP, Abmayr SM, Workman JL., & Workman, J.L. (2010) Heterochromatin Protein 1 (HP1) connects the FACT histone chaperone complex to the phosphorylated CTD of RNA polymerase II. Genes Dev., Oct 1;24(19):2133-45. Abstract

85. Suganuma T, Mushegian A, Swanson SK, Abmayr SM, Florens L, Washburn MP, Workman JL. (2010) The ATAC Acetyltransferase Complex Coordinates MAP Kinases to Regulate JNK Target Genes. Cell, Sep 3;142(5):726-36. Abstract

84. Smith KT, Martin-Brown SA, Florens L, Washburn MP, Workman JL. (2010) Deacetylase inhibitors dissociate the histone-targeting ING2 subunit from the Sin3 complex. Chem. Biol., Jan 29;17(1):65-74. Abstract

83. D'Angiolella V, Donato V, Vijayakumar S, Saraf A, Florens L, Washburn MP, Dynlacht B, Pagano M. (2010) SCF-Cyclin F controls centrosome homeostasis and mitotic fidelity via CP110 degradation. Nature, Jul 1;466(7302):138-42. Abstract

82. Mallanna SK, Ormsbee BD, Iacovino M, Gilmore JM, Cox JL, Kyba M, Washburn MP,  & Rizzino, A. (2010) Proteomic analysis of Sox2-associated proteins during early stages of mouse embryonic stem cell differentiation identifies Sox21 as a novel regulator of stem cell fate. Stem Cells, Oct;28(10):1715-27. Abstract

81. Korfali N, Wilkie GS, Swanson SK, Srsen V, Batrakou DG, Fairley EA, Malik P, Zuleger N, Goncharevich A, de Las Heras J, Kelly DA, Kerr AR, Florens L, Schirmer EC. (2010) The leukocyte nuclear envelope proteome varies with cell activation and contains novel transmembrane proteins that affect genome architecture. Mol. Cell. Proteomics, Dec;9(12):2571-85. Epub 2010 Aug 6. Abstract.
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80. Determining protein complex connectivity using a probabilistic deletion network derived from quantitative proteomics

Sardiu ME, Gilmore JM, Carrozza MJ, Li B, Workman JL, Florens L, Washburn MP. (2009) PloS ONE, Oct 6;4(10):e7310.

Protein complexes are key molecular machines executing a variety of essential cellular processes. Despite the availability of genome-wide protein-protein interaction studies, determining the connectivity between proteins within a complex remains a major challenge. Here we demonstrate a method that is able to predict the relationship of proteins within a stable protein complex. We employed a combination of computational approaches and a systematic collection of quantitative proteomics data from wild-type and deletion strain purifications to build a quantitative deletion-interaction network map and subsequently convert the resulting data into an interdependency-interaction model of a complex. We applied this approach to a data set generated from components of the Saccharomyces cerevisiae Rpd3 histone deacetylase complexes, which consists of two distinct small and large complexes that are held together by a module consisting of Rpd3, Sin3 and Ume1. The resulting representation reveals new protein-protein interactions and new submodule relationships, providing novel information for mapping the functional organization of a complex.


79. Effect of Dynamic Exclusion Duration on Spectral Count Based Quantitative Proteomics

Zhang Y, Wen Z, Washburn MP, Florens L. (2009) Anal. Chem., Aug 1;81(15):6317-26.

To increase proteome coverage, dynamic exclusion (DE) is a widely used tool. When DE is enabled, more proteins can be identified, although the total spectral counts will decrease. To investigate the effects of DE duration on spectral-counting based quantitative proteomics, we analyzed the same sample via multidimensional protein identification technology while enabling different DE durations (15, 60, 90, 300, 600 s) or turning DE off. Normalized spectral abundance factors (NSAFs) measured for abundant proteins varied little with or without DE, while enabling DE lead to higher peptide counts, higher NSAFs, and better reproducibility of detection for proteins of relatively lower abundance. The optimal DE duration, which generated the maximum number of peptides, proteins, and peptides per protein, was observed to be 90 s in our settings. We developed a mathematical model for analyzing the effects of DE duration on peptide spectral counts. We found that the optimal DE duration depends on the average chromatographic peak width at the base of eluting peptides and mass spectrometry parameters, leading us to calculate an optimized DE duration of 97.9 s, in excellent agreement with our observations. In this study, we provide a systematic approach for the optimization of spectral counts for improved quantitative proteomics analysis.

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78. Evaluation of Clustering Algorithms for Protein Complex and Protein Interaction Network Assembly

Sardiu ME, Florens L, Washburn MP. (2009) J. Proteome Res., Jun;8(6):2944-52.

Assembling protein complexes and protein interaction networks from affinity purification-based proteomics data sets remains a challenge. When little a priori knowledge of the complexes exists, it is difficult to place proteins in the proper locations and evaluate the results of clustering approaches. Here we have systematically compared multiple hierarchical and partitioning clustering approaches using a well-characterized but highly complex human protein interaction network data set centered around the conserved AAA+ ATPases Tip49a and Tip49b. This network provides a challenge to clustering algorithms because Tip49a and Tip49b are present in four distinct complexes, the network contains modules, and the network has multiple attachments. We compared the use of binary data, quantitative proteomics data in the form of normalized spectral abundance factors, and the Z-score normalization. In our analysis, a partitioning approach indicated the major modules in a network. Next, while Euclidian distance was sensitive to scaling, with data transformation, all the attachments in a data set were recovered in one branch of a dendrogram. Finally, when Pearson correlation and hierarchical clustering were used, complexes were well separated and their attachments were placed in the proper locations. Each of these three approaches provided distinct information useful for assembly of a network of multiple protein complexes.


77. Generation and analysis of multidimensional protein identification technology datasets

Swanson SK, Florens L, Washburn MP. (2009) Methods Mol. Biol., 492:1-20.

Systems that couple two dimensional liquid chromatography (LC/LC) with tandem mass spectrometry are widely used in modern proteomics. One such system, multidimensional protein identification technology (MudPIT), couples strong cation exchange chromatography and reversed phase chromatography to tandem mass spectrometry in a single microcapillary column. Using database searching algorithms like SEQUEST and additional computational tools, researchers are able to analyze in great detail complex peptide mixtures generated from biofluids, tissues, cells, organelles, or protein complexes. This chapter describes the use of MudPIT on modern mass spectrometry instrumentation and describes a data analysis pipeline designed to provide low false positive rates and quantitative datasets.


76. Rtr1 Regulates the Transition from Serine 5 to Serine 2 Phosphorylation on the RNA Polymerase II C-terminal Domain during Transcription Elongation

Mosley AL, Pattenden SG, Carey M, Venkatesh S, Gilmore JM, Florens L, Workman JL, Washburn MP. (2009) Mol. Cell, Apr 24;34(2):168-78.

Messenger RNA processing is coupled to RNA polymerase II (RNAPII) transcription through coordinated recruitment of accessory proteins to the Rpb1 C-terminal domain (CTD). Dynamic changes in CTD phosphorylation during transcription elongation are responsible for their recruitment, with serine 5 phosphorylation (S5-P) occurring toward the 5' end of genes and serine 2 phosphorylation (S2-P) occurring toward the 3' end. The proteins responsible for regulation of the transition state between S5-P and S2-P CTD remain elusive. We show that a conserved protein of unknown function, Rtr1, localizes within coding regions, with maximum levels of enrichment occurring between the peaks of S5-P and S2-P RNAPII. Upon deletion of Rtr1, the S5-P form of RNAPII accumulates in both whole-cell extracts and throughout coding regions; additionally, RNAPII transcription is decreased, and termination defects are observed. Functional characterization of Rtr1 reveals its role as a CTD phosphatase essential for the S5-to-S2-P transition.


75. Takahashi H, Martin-Brown S, Washburn MP, Florens L, Conaway JW, Conaway RC. (2009) Proteomic Analysis Reveals a Physical and Functional Link between Hepatocyte Nuclear Factor 4? and TFIID. J. Biol. Chem., Nov 20;284(47):32405-12. Epub 2009 Oct 5. Abstract

74. Gottschalk AJ, Timinszky G, Kong SE, Jin J, Cai Y, Swanson SK, Washburn MP, Florens L, Ladurner AG, Conaway JW, Conaway RC. (2009) Poly(ADP-ribosyl)ation directs recruitment and activation of an ATP-dependent chromatin remodeler. Proc. Natl. Acad. Sci. USA, Aug 18;106(33):13770-4. Epub 2009 Aug 6. Abstract

73. Takahashi YH, Lee JS, Swanson SK, Saraf A, Florens L, Washburn MP, Trievel RC, Shilatifard A. (2009) Regulation of H3K4 trimethylation via Cps40 (Spp1) of COMPASS is monoubiquitination independent: Implication for a Phe/Tyr switch by the catalytic domain of Set1. Mol. Cell. Biol., Jul;29(13):3478-86. Epub 2009 Apr 27. Abstract

72. Weake VM, Swanson SK, Mushegian A, Florens L, Washburn MP, Abmayr SM, Workman JL. (2009) A novel histone-fold domain containing protein that replaces TAF6 in Drosophila SAGA is required for SAGA-dependent gene expression. Genes Dev. Dec 15;23(24):2818-23. Abstract

71. Shi M, Vivian CJ, Lee KJ, Ge C, Morotomi-Yano K, Manzl C, Bock F, Sato S, Tomomori-Sato C, Zhu R, Haug JS, Swanson SK, Washburn MP, Chen DJ, Chen BP, Villunger A, Florens L, Du C. (2009) DNA-PKcs-PIDDosome: a nuclear caspase-2-activating complex with role in G2/M checkpoint maintenance. Cell. 136, 508-20. Retraction Notice: Cell, 145, 161 April 1, 2011. Integrity of Proteomics Center contribution confirmed by official Stowers Institute inquiry Nov. 7, 2011. Abstract

70. Lee KK, Swanson SK, Florens L, Washburn MP, Workman JL. (2009) Yeast Sgf73/Ataxin-7 Serves as an Anchor for the Deubiquitination Module of Both SAGA and Slik(SALSA) HAT Complexes. Epigenetics & Chromatin, Feb 18;2(1):2. Abstract

69. Dinglasan RR, Devenport M, Florens L, Johnson JR, McHugh CA, Donnelly-Doman M, Carucci DJ, Yates JR 3rd, Jacobs-Lorena M. (2009) The Anopheles gambiae Midgut Peritrophic Matrix 1 Proteome. Insect Biochem. Mol. Biol., Feb;39(2):125-34. Epub 2008 Nov 11. Abstract.
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68. Skaar JR, Richard DJ, Saraf A, Toschi A, Bolderson E, Florens L, Washburn MP, Khanna KK, Pagano M.  (2009) INTS3 controls the hSSB1-mediated DNA damage response. J. Cell Biol. Oct 5;187(1):25-32. Epub 2009 Sep 28. Abstract

67. Korfali N, Fairley EA, Swanson SK, Florens L, Schirmer EC. (2009) Use of Sequential Chemical Extractions to Purify Nuclear Membrane Proteins for Proteomics Identification. Methods Mol Biol., 528:201-25. Abstract

66. A Label Free Quantitative Proteomic Analysis of the Saccharomyces Cerevisiae Nucleus

Mosley AL, Florens L, Wen Z, Washburn MP. (2009) J. Proteomics, Feb 15;72(1):110-20. Epub 2008 Nov 8.

To gain insight into the nuclear proteome of Saccharomyces cerevisiae, nuclei were isolated and fractionated via sucrose gradient sedimentation. The resulting fractions were analyzed using multidimensional protein identification technology and the detected proteins were quantified using normalized spectral counts. A large number of low abundance proteins, many of which are involved in transcriptional regulation, were recovered. Sucrose gradient elution profiles of known protein complex components demonstrated that this approach may provide insight into the question of what percentage of the total population of a protein is in one complex, versus another protein complex, or exists as a free protein.

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65. Sample preparation and in-solution protease digestion of proteins for chromatography-based proteomic analysis

Washburn MP. (2008) Curr. Protoc. Protein Sci., Chapter 23:Unit 23.6.1-23.6.11.

The adoption of chromatography-based proteomics approaches has resulted in the development of new methods for optimal use of these technologies. One such technology, named multidimensional protein identification technology (MudPIT), directly couples liquid chromatography with tandem mass spectrometry. In the MudPIT approach, digested protein samples are directly loaded onto a microcapillary column packed sequentially with reversed-phase and strong cation exchange resins. Once digested protein samples are loaded onto the column, the column is placed in-line between a high-performance liquid chromatography system and an electrospray ionization tandem mass spectrometer (ESI-MS/MS). The digested protein samples for MudPIT analysis must be directly compatible with ESI-MS/MS, so the sample preparation and digestion protocols must be optimized for this purpose. The primary objective of all of the protocols in this unit is to yield a final digested protein mixture of less than 300 microl that can then be directly loaded onto a MudPIT column.


64. Coupled multidimensional chromatography and tandem mass spectrometry systems for complex peptide mixture analysis in Multidimensional Liquid Chromatography

Washburn MP. (2008) (Cohen, S.A. and Schure, M.R. eds. Wiley-Interscience, Hoboken, NJ) 243-259.

63. Probabilistic Assembly of Human Protein Interaction Networks from Label Free Quantitative Proteomics

Sardiu ME, Cai Y, Jin J, Swanson SK, Conaway RC, Conaway JW, Florens L, Washburn MP. (2008) . Proc. Natl. Acad. Sci. USA, Feb 5;105(5):1454-9. Epub 2008 Jan 24.

Large-scale affinity purification and mass spectrometry studies have played important roles in the assembly and analysis of comprehensive protein interaction networks for lower eukaryotes. However, the development of such networks for human proteins has been slowed by the high cost and significant technical challenges associated with systematic studies of protein interactions. To address this challenge, we have developed a method for building local and focused networks. This approach couples vector algebra and statistical methods with normalized spectral counting (NSAF) derived from the analysis of affinity purifications via chromatography-based proteomics. After mathematical removal of contaminant proteins, the core components of multiprotein complexes are determined by singular value decomposition analysis and clustering. The probability of interactions within and between complexes is computed solely based upon NSAFs using Bayes' approach. To demonstrate the application of this method to small-scale datasets, we analyzed an expanded human TIP49a and TIP49b dataset. This dataset contained proteins affinity-purified with 27 different epitope-tagged components of the chromatin remodeling SRCAP, hINO80, and TRRAP/TIP60 complexes, and the nutrient sensing complex Uri/Prefoldin. Within a core network of 65 unique proteins, we captured all known components of these complexes and novel protein associations, especially in the Uri/Prefoldin complex. Finally, we constructed a probabilistic human interaction network composed of 557 protein pairs.


62. Statistical similarities between transcriptomics and quantitative shotgun proteomics data

Pavelka N, Fournier ML, Swanson SK, Pelizzola M, Ricciardi-Castagnoli P, Florens L, Washburn MP. (2008) Mol. Cell Proteomics, Apr;7(4):631-44. Epub 2007 Nov 19.

If the large collection of microarray-specific statistical tools was applicable to the analysis of quantitative shotgun proteomics datasets, it would certainly foster an important advancement of proteomics research. Here we analyze two large multidimensional protein identification technology datasets, one containing eight replicates of the soluble fraction of a yeast whole-cell lysate and one containing nine replicates of a human immunoprecipitate, to test whether normalized spectral abundance factor (NSAF) values share substantially similar statistical properties with transcript abundance values from Affymetrix GeneChip data. First we show similar dynamic range and distribution properties of these two types of numeric values. Next we show that the standard deviation (S.D.) of a protein's NSAF values was dependent on the average NSAF value of the protein itself, following a power law. This relationship can be modeled by a power law global error model (PLGEM), initially developed to describe the variance-versus-mean dependence that exists in GeneChip data. PLGEM parameters obtained from NSAF datasets proved to be surprisingly similar to the typical parameters observed in GeneChip datasets. The most important common feature identified by this approach was that, although in absolute terms the S.D. of replicated abundance values increases as a function of increasing average abundance, the coefficient of variation, a relative measure of variability, becomes progressively smaller under the same conditions. We next show that PLGEM parameters were reasonably stable to decreasing numbers of replicates. We finally illustrate one possible application of PLGEM in the identification of differentially abundant proteins that might potentially outperform standard statistical tests. In summary, we believe that this body of work lays the foundation for the application of microarray-specific tools in the analysis of NSAF datasets.

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61. Geisbrecht ER, Haralalka S, Swanson SK, Florens L, Washburn MP, Abmayr SM. (2008) Drosophila ELMO/CED-12 interacts with Myoblast city to direct myoblast fusion and ommatidial organization. Dev. Biol., Feb 1;314(1):137-49. Epub 2007 Nov 28. Abstract

60. Yao T, Song L, Jin J, Cai Y, Takahashi H, Swanson SK, Washburn MP, Florens L, Conaway RC, Cohen RE, Conaway JW. (2008) Distinct Modes of Regulation of the Uch37 Deubiquitinating Enzyme in the Proteasome and in the Ino80 Chromatin Remodeling Complex. Mol. Cell, Sep 26;31(6):909-17. Abstract

59. Mahrour N, Redwine WB, Florens L, Swanson SK, Martin-Brown S, Bradford WD, Staehling-Hampton K, Washburn MP, Conaway RC, Conaway JW. (2008) Characterization of cullin-box sequences that direct recruitment of Cul2-Rbx1 and Cul5-Rbx2 modules to elongin BC-based ubiquitin ligases. J. Biol. Chem., Mar 21;283(12):8005-13. Epub 2008 Jan 10. Abstract

58. Fan X, Martin-Brown S, Florens L, Li R. (2008) Intrinsic capability of budding yeast cofilin to promote turnover of tropomyosin-bound actin filaments. PLoS ONE, 3(11):e3641. Epub 2008 Nov 4. Abstract

57. Wu M, Wang PF, Lee JS, Martin-Brown S, Florens L, Washburn M, Shilatifard A. (2008) Molecular regulation of H3K4 trimethylation by WDR82, a component of human Set1/COMPASSS. Mol. Cell. Biol., Dec;28(24):7337-44. Epub 2008 Oct 6. Abstract

56. Lin CH, Li B, Swanson S, Zhang Y, Florens L, Washburn MP, Abmayr SM, Workman JL. (2008) Heterochromatin Protein 1a Stimulates Histone H3 Lysine 36 Demethylation by the Drosophila KDM4A Demethylase. Mol. Cell, Dec 5;32(5):696-706. Abstract

55. Suganuma T, Gutiérrez JL, Li B, Florens L, Swanson SK, Washburn MP, Abmayr SM, Workman JL. (2008) ATAC is a Double Histone Acetyltransferase Complex that Stimulates Nucleosome Sliding. Nature Struct. Mol. Biol., Apr;15(4):364-72. Epub 2008 Mar 9. Abstract

54. Black JC, Mosley A, Kitada T, Washburn M, Carey M. (2008) The SIRT2 deacetylase regulates autoacetylation of p300. Mol. Cell, Nov 7;32(3):449-55. Abstract

53. Florens L, Korfali N, Schirmer EC. (2008) Subcellular Fractionation and Proteomics of Nuclear Envelopes. Methods Mol Biol., 432:117-37. Abstract

52. Minakhin L, Goel M, Berdygulova Z, Ramanculov E, Florens L, Glazko G, Karamychev VN, Slesarev AI, Kozyavkin SA, Khromov I, Ackermann HW, Washburn M, Mushegian A, Severinov K. (2008) Genome comparison and proteomic characterization of Thermus thermophilus bacteriophages P23-45 and P74-26: siphoviruses with triplex-forming sequences and the longest known tails. J. Mol. Biol., Apr 25;378(2):468-80. Epub 2008 Feb 15. Abstract

51. Savalia D, Westblade LF, Goel M, Florens L, Kemp P, Akulenko N, Pavlova O, Padovan JC, Chait BT, Washburn MP, Ackermann HW, Mushegian A, Gabisonia T, Molineux I, Severinov K. (2008) Genomic and Proteomic Analysis of phiEco32, a Novel Escherichia coli Phage. J. Mol. Biol., Mar 28;377(3):774-89. Abstract

50. Srivastava S, Swanson SK, Manel N, Florens L, Washburn MP, Skowronski J. (2008) Lentiviral Vpx accessory factor targets VprBP/DCAF1 substrate adaptor for Cullin 4 E3 ubiquitin ligase to enable macrophage infection. PLoS Pathogens, May 9;4(5):e1000059. Abstract

49. Liu WL, Coleman RA, Grob P, King DS, Florens L, Washburn MP, Geles KG, Yang JL, Ramey V, Nogales E, Tjian R. (2008) Structural changes in TAF4b-TFIID correlate with promoter selectivity. Mol. Cell, Jan 18;29(1):81-91. Abstract

48. KKoutelou E, Sato S, Tomomori-Sato C, Florens L, Swanson SK, Washburn MP, Kokkinaki M, Conaway RC, Conaway JW, Moschonas NK. (2008) Neuralized-like1 targeted to the plasma membrane by N-myristoylation regulates the Notch ligand Jagged1. J. Biol. Chem. Feb 15;283(7):3846-53. Epub 2007 Dec 12. Abstract

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47. Quantitative Shotgun Proteomics Using a Protease with Broad Specificity and Normalized Spectral Abundance Factor

Zybailov BL, Florens L, Washburn MP. (2007) Mol. Biosyst, May;3(5):354-60. Epub 2007 Apr 10.

Non-specific proteases are rarely used in quantitative shotgun proteomics due to potentially high false discovery rates. Yet, there are instances when application of a non-specific protease is desirable to obtain sufficient sequence coverage of otherwise poorly accessible proteins or structural domains. Using the non-specific protease, proteinase K, we analyzed Saccharomyces cerevisiae preparations grown in (14)N rich media and (15)N minimal media and obtained relative quantitation from the dataset using normalized spectral abundance factors (NSAFs). A critical step in using a spectral counting based approach for quantitative proteomics is ensuring the inclusion of high quality spectra in the dataset. One way to do this is to minimize the false discovery rate, which can be accomplished by applying different filters to a searched dataset. Natural log transformation of proteinase K derived NSAF values followed a normal distribution and allowed for statistical analysis by the t-test. Using this approach, we generated a dataset of 719 unique proteins found in each of the three independent biological replicates, of which 84 showed a statistically significant difference in expression levels between the two growth conditions.


46. Multidimensional separations-based shotgun proteomics

Fournier ML, Gilmore JM, Martin-Brown SA, Washburn MP. (2007) Chem. Rev., Aug;107(8):3654-86. Epub 2007 Jul 25.


45. Deciphering the combinatorial histone code

Gilmore JM, Washburn MP. (2007) Nat. Methods, 4(6):480-1.

A new mass spectrometry (MS) approach has been developed, allowing combinatorial analysis of histone H3.2 post-translational modifications that may provide the key to unlocking the histone code.


44. Application of shotgun proteomics to transcriptional regulatory pathways in Spectral Techniques in Proteomics

Mosley AL, Washburn MP. (2007) (Sem, D.S., ed. CRC Press, Boca Raton, FL) 207-222.

43. Cai Y, Jin J, Yao T, Gottschalk AJ, Swanson SK, Wu S, Shi Y, Washburn MP, Florens L, Conaway RC, Conaway JW. (2007) YY1 functions with INO80 to activate transcription. Nature Struct. Mol. Biol., Sep;14(9):872-4. Epub 2007 Aug 26. Abstract

42. Banks CA, Kong SE, Spahr H, Florens L, Martin-Brown S, Washburn MP, Conaway JW, Mushegian A, Conaway RC. (2007) Identification and characterization of a Schizosaccharomyces pombe RNA polymerase II elongation factor with similarity to the metazoan transcription factor ELL. J. Biol. Chem., Feb 23;282(8):5761-9. Epub 2006 Dec 6. Abstract

41. Camahort R, Li B, Florens L, Swanson SK, Washburn MP, Gerton JL. (2007) Scm3 is essential to establish and maintain functional kinetochores in S. cerevisiae. Mol. Cell, Jun 22;26(6):853-65. Epub 2007 Jun 14. Abstract

40. Xiang Y, Takeo S, Florens L, Hughes SE, Huo LJ, Gilliland WD, Swanson SK, Teeter K, Schwartz JW, Washburn MP, Jaspersen SL, Hawley RS. (2007) The Inhibition of Polo Kinase by Matrimony Facilitates G2 Arrest in the Meiotic Cell Cycle. PLoS Biology, Dec;5(12):e323. Abstract

39. Lee JS, Shukla A, Schneider J, Swanson SK, Washburn MP, Florens L, Bhaumik SR, Shilatifard A. (2007) Translating histone crosstalk between H2B monoubiquitination and H3 methylation by COMPASS. Cell, Dec 14;131(6):1084-96. Abstract

38. Wood A, Shukla A, Schneider J, Lee JS, Stanton JD, Dzuiba T, Swanson SK, Florens L, Washburn MP, Wyrick J, Bhaumik SR, Shilatifard A. (2007) Ctk complex mediated regulation of histone methylation by COMPASS. Mol. Cell. Biol., Jan;27(2):709-20. Epub 2006 Nov 6. Abstract

37. Litovchick L, Sadasivam S, Florens L, Zhu X, Swanson SK, Velmurugan S, Chen R, Washburn MP, Liu XS, DeCaprio JA. (2007) Evolutionally conserved multi-subunit RBL2/p130 and E2F4 protein complex represses human cell cycle-dependent genes in quiescence. Mol. Cell, May 25;26(4):539-51. Abstract

36. Skaar JR, Florens L, Tsutsumi T, Arai T, Tron A, Swanson SK, Washburn MP, DeCaprio JA. (2007) PARC and CUL7 form atypical Cullin RING ligase complexes. Cancer Res., Mar 1;67(5):2006-14. Abstract

35. Yuan CC, Zhao X, Florens L, Swanson SK, Washburn MP, Hernandez N. (2007) CHD8 associates with human Staf and contributes to efficient U6 RNA polymerase III transcription. Mol. Cell. Biol., Dec;27(24):8729-38. Epub 2007 Oct 15. Abstract

34. Hrecka K, Gierszewska M, Srivastava S, Kozaczkiewicz L, Swanson SK, Florens L, Washburn MP, Skowronski J. (2007) Lentiviral Vpr usurps Cul4-DDB1[VprBP] E3 ubiquitin ligase to modulate cell cycle. Proc. Natl. Acad. Sci. USA, Jul 10;104(28):11778-83. Epub 2007 Jul 3. Abstract

33. Chen X, Wurtmann EJ, Van Batavia J, Zybailov B, Washburn MP, Wolin SL. (2007) An ortholog of the Ro autoantigen functions in 23S rRNA maturation in D. radiodurans, Genes Dev, Jun 1;21(11):1328-39. Epub 2007 May 17. Abstract

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32. Quantitation in proteomic experiments utilizing mass spectrometry

Paoletti AC, Washburn MP. (2006) Biotechnol. Genet. Eng. Rev., 22:1-19.

Advances in the field of quantitative proteomics are beginning to routinely allow scientists to reliable and accurately determine absolute protein concentrations or changes in protein concentration from complex samples in vivo. These changes can be measured in response to differential growth conditions, drug regimens, disease states, and even more specifically, quantitative measurements can determine levels of PTMs, such as phosphorylation in cell signalling pathways or glycosylation levels of haemogloblin in diabetic patients. These measurements are allowing scientists unprecedented insight into roles individual protein levels and modifications play in the overall view of the system being studied.


31. Quantitative Proteomic Analysis of Distinct Mammalian Mediator Complexes using Normalized Spectral Abundance Factors

Paoletti AC, Parmely TJ, Tomomori-Sato C, Sato S, Zhu D, Conaway RC, Conaway JW, Florens L, Washburn MP. (2006) Proc. Natl. Acad. Sci. USA, 1Dec 12;103(50):18928-33. Epub 2006 Nov 30

Components of multiprotein complexes are routinely determined by using proteomic approaches. However, this information lacks functional content except when new complex members are identified. To analyze quantitatively the abundance of proteins in human Mediator we used normalized spectral abundance factors generated from shotgun proteomics data sets. With this approach we define a common core of mammalian Mediator subunits shared by alternative forms that variably associate with the kinase module and RNA polymerase (pol) II. Although each version of affinity-purified Mediator contained some kinase module and RNA pol II, Mediator purified through F-Med26 contained the most RNA pol II and the least kinase module as demonstrated by the normalized spectral abundance factor approach. The distinct forms of Mediator were functionally characterized by using a transcriptional activity assay, where F-Med26 Mediator/RNA pol II was the most active. This method of protein complex visualization has important implications for the analysis of multiprotein complexes and assembly of protein interaction networks.


30. Analyzing Chromatin Remodeling Complexes Using Shotgun Proteomics and Normalized Spectral Abundance Factors

Florens L, Carozza MJ, Swanson SK, Fournier M, Coleman MK, Workman JL, Washburn MP. (2006) Methods, Dec;40(4):303-11

Mass spectrometry-based approaches are commonly used to identify proteins from multiprotein complexes, typically with the goal of identifying new complex members or identifying post-translational modifications. However, with the recent demonstration that spectral counting is a powerful quantitative proteomic approach, the analysis of multiprotein complexes by mass spectrometry can be reconsidered in certain cases. Using the chromatography-based approach named multidimensional protein identification technology, multiprotein complexes may be analyzed quantitatively using the normalized spectral abundance factor that allows comparison of multiple independent analyses of samples. This study describes an approach to visualize multiprotein complex datasets that provides structure function information that is superior to tabular lists of data. In this method review, we describe a reanalysis of the Rpd3/Sin3 small and large histone deacetylase complexes previously described in a tabular form to demonstrate the normalized spectral abundance factor approach.


29. Statistical Analysis of Membrane Proteome Expression Changes in Saccharomyces cerevisiae

Zybailov B, Mosley AL, Sardiu ME, Coleman MK, Florens L, Washburn MP. (2006) J. Proteome Res., Sep;5(9):2339-47.

We have devised an approach for analyzing shotgun proteomics datasets based on the normalized spectral abundance factor that can be used for quantitative proteomics analysis. Three biological replicates of samples enriched for plasma membranes were isolated from S. cerevisiae grown in 14N-rich media and 15N-minimal media and analyzed via quantitative multidimensional protein identification technology. The natural log transformation of NSAF values from S. cerevisiae cells grown in 14N YPD media and 15N-minimal media had a normal distribution. The t-test analysis demonstrated 221 of 1316 proteins were significantly overexpressed in one or the other growth conditions with a p value < 0.05. Notably, amino acid transporters were among the 14 membrane proteins that were significantly upregulated in cells grown in minimal media, and we functionally validated these increases in protein expression with radioisotope uptake assays for selected proteins.


28. Proteomic Analysis by Multidimensional Protein Identification Technology

Florens L, Washburn MP. (2006) Methods Mol Biol., 328:159-75.

Multidimensional chromatography coupled to mass spectrometry is an emerging technique for the analysis of complex protein mixtures. One approach in this general category, multidimensional protein identification technology (MudPIT), couples biphasic or triphasic microcapillary columns to high-performance liquid chromatography, tandem mass spectrometry, and database searching. The integration of each of these components is critical to the implementation of MudPIT in a laboratory. MudPIT can be used for the analysis of complex peptide mixtures generated from biofluids, tissues, cells, organelles, or protein complexes. The information described in this chapter will provide researchers with details for sample preparation, column assembly, and chromatography parameters for complex peptide mixture analysis.


27. Yao T, Song L, Xu W, DeMartino GN, Florens L, Swanson SK, Washburn MP, Conaway RC, Conaway JW, Cohen RE. (2006) Proteasome recruitment and activation of the Uch37 deubiquitinating enzyme by Adrm1. Nature Cell Biol., Sep;8(9):994-1002. Epub 2006 Aug 13. Abstract

26. Ruhl DD, Jin J, Cai Y, Swanson S, Florens L, Washburn MP, Conaway RC, Conaway JW, Chrivia JC. (2006) Purification of a Human SRCAP Complex that Remodels Chromatin by Incorporating the Histone Variant H2A.Z into Nucleosomes. Biochemistry, May 2;45(17):5671-7. Abstract

25. Henry JM, Camahort R, Rice DA, Florens L, Swanson SK, Washburn MP, Gerton JL. (2006) Mnd1/Hop2 facilitates Dmc1-dependent interhomolog crossover formation in meiosis of budding yeast Mol. Cell. Biol., Apr;26(8):2913-23. Abstract

24. Guelman S, Suganuma T, Florens L, Weake V, Swanson SK, Washburn MP, Abmayr SM, Workman JL. (2006) The Essential Gene wda Encodes a WD40 Repeat Subunit of Drosophila SAGA Required for Histone H3 Acetylation. Mol. Cell. Biol., Oct;26(19):7178-89 Abstract

23. Guelman S, Suganuma T, Florens L, Swanson SK, Kiesecker CL, Kusch T, Anderson S, Yates JR 3rd, Washburn MP, Abmayr SM, Workman JL. (2006) Host Cell Factor (dHCF) and an uncharacterized SANT-domain protein are stable components of ATAC, a novel dAda2A/dGcn5-containing HAT complex in Drosophila. Mol. Cell. Biol., Feb;26(3):871-82. Abstract

22. Emran F, Florens L, Ma B, Swanson SK, Washburn MP, Hernandez N. (2006) A role for Yin Yang-1 (YY1) in assembly of snRNA transcription complexes. Gene, Aug 1;377:96-108. Epub 2006 May 20. Abstract

21. Naryshkina T, Liu J, Florens L, Swanson SK, Pavlov AR, Pavlova NV, Inman R, Minakhin L, Kozyavkin SA, Washburn M, Mushegian A, Severinov K. (2006) Thermus thermophilus bacteriophage  YS40 genome and proteomic characterization of virions. J. Mol. Biol., Dec 8;364(4):667-77. Epub 2006 Sep 6. Abstract

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20. Correlation of Relative Abundance Ratios Derived from Peptide Ion Chromatograms and Spectrum Counting for Quantitative Proteomic Analysis using Stable Isotope Labeling

Zybailov B, Coleman MK, Florens L, Washburn MP. (2005) Anal. Chem., Oct 1;77(19):6218-24.

In this study, S. cerevisiae crude membrane fractions were prepared using the acid-labile detergent RapiGest from cells grown under rich and minimal media conditions using 14N and 15N ammonium sulfate as the sole nitrogen source. Four independent MudPIT analyses of 1:1 mixtures of sample were prepared and analyzed via quantitative multidimensional protein identification technology on a two-dimensional ion trap mass spectrometer. Using the method described in this study, low-abundance integral membrane proteins with up to 14 transmembrane domains were identified and their protein expression determined when sufficient spectrum counting and ion chromatogram information was generated. We demonstrate that spectrum counting and mass spectrometry derived ion chromatograms strongly correlate for determining quantitative changes in protein expression. Spectrum counting proved more reproducible and has a wider dynamic range contributing to the deviation of the two quantitative approaches from a perfect positive correlation.


19. The continuing evolution of shotgun proteomics

Swanson SK, Washburn MP. (2005) Drug Discov. Today, May 15;10(10):719-25. Review.

Shotgun proteomics has emerged as a powerful approach for the analysis of complex protein mixtures, including biofluids, tissues, cells, organelles or protein complexes. Having evolved from the integration of chromatography and mass spectrometry, innovations in sample preparation, multidimensional chromatography, mass spectrometry and proteomic informatics continually facilitate, enable and challenge shotgun proteomics. As a result, shotgun proteomics continues to evolve and enable new areas of biological research, and is beginning to impact human disease diagnosis and therapeutic intervention.


18. Jin J, Cai Y, Yao T, Gottschalk AJ, Florens L, Swanson SK, Gutiérrez JL, Coleman MK, Workman JL, Mushegian A, Washburn MP, Conaway RC, Conaway JW. (2005) A Mammalian Chromatin Remodeling Complex with Similarities to the Yeast INO80 Complex. J. Biol. Chem., Dec 16;280(50):41207-12. Epub 2005 Oct 17. Abstract

17. Cai Y, Jin J, Florens L, Swanson SK, Kusch T, Li B, Workman JL, Washburn MP, Conaway RC, Conaway JW. (2005) The mammalian YL1 protein is a shared subunit of the TRRAP/TIP60 histone acetyltransferese and SRCAP complexes. J. Biol. Chem., Apr 8;280(14):13665-70. Epub 2005 Jan 11. Abstract

16. Conaway JW, Florens L, Sato S, Tomomori-Sato C, Parmely TJ, Yao T, Swanson SK, Banks CA, Washburn MP, Conaway RC. (2005) The mammalian Mediator complex. FEBS Lett., Feb 7;579(4):904-8. Review. Abstract

15. Schneider J, Wood A, Lee JS, Schuster R, Dueker J, Maguire C, Swanson SK, Florens L, Washburn MP, Shilatifard A. (2005) Molecular Regulation of Histone H3 Trimethylation by COMPASS and the Regulation of Gene Expression. Mol. Cell, Sep 16;19(6):849-56. Abstract

14. Carrozza MJ, Li B, Florens L, Suganuma T, Swanson SK, Lee KK, Shia WJ, Anderson S, Yates JR 3rd, Washburn MP, Workman JL. (2005) Histone H3 methylation by Set2 directs deacetylation of coding regions by Rpd3S to suppress spurious intragenic transcription. Cell, Nov 18;123(4):581-92. Abstract

13. Carrozza MJ, Florens L, Swanson SK, Shia WJ, Anderson S, Yates JR 3rd, Washburn MP, Workman JL. (2005) Stable incorporation of sequence specific repressors Ash1 and Ume6 into the Rpd3L complex. Biochim. Biophys. Acta, Nov 10;1731(2):77-87; Epub 2005 Oct 24. Abstract

12. Prochasson P, Florens L, Swanson SK, Washburn MP, Workman JL. (2005) The HIR corepressor complex binds to nucleosomes generating a distinct protein/DNA complex resistant to remodeling by SWI/SNIF. Genes Dev., Nov 1;19(21):2534-9. Abstract

11. Shia WJ, Osada S, Florens L, Swanson SK, Washburn MP, Workman JL. (2005) Characterization of the yeast trimeric-SAS acetyltransferase complex. J. Biol. Chem., Mar 25;280(12):11987-94. Epub 2005 Jan 18. Abstract

10. Lee KK, Florens L, Swanson SK, Washburn MP, Workman JL. (2005) The deubiquitylation activity of Ubp8 is dependent upon Sgf11 and its association with the SAGA complex. Mol. Cell. Biol., Feb;25(3):1173-82. Abstract

9. Zybailov B and Washburn MP. (2005) Proteome Analysis: Mass Spectrometry Based Methods Of in the Encyclopedia of Molecular Cell Biology and Molecular Medicine (Meyers, R.A., ed. Wiley-VCH, Weinheim, Germany) 8, 1-43.  Also published in (2007) in Proteins: From Analytics to Structural Genomics (Meyers, R.A., ed. Wiley-VCH, Weinheim Germany).

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8. Principles and applications of multidimensional protein identification technology

Paoletti AC, Zybailov B, Washburn MP. (2004) Expert Rev. Proteomics, 1(3):275-82.

Multidimensional chromatography coupled to tandem mass spectrometry is an emerging technique for the analysis of proteomes and is rapidly being implemented by many researchers for proteomic analysis. In this technology profile, a particular proteomic approach known as multidimensional protein identification technology (MudPIT) is discussed. In MudPIT, a biphasic microcapillary column is packed with high-performance liquid chromatography grade reversed phase and strong cation exchange packing materials, loaded with a complex peptide mixture and placed in line with quaternary high-performance liquid chromatography and a tandem mass spectrometer. MudPIT has the capability to analyze highly complex proteomic mixtures such as whole proteomes, organelles and protein complexes.


7. Utilisation of proteomics datasets generated via multidimensional protein identification technology (MudPIT)

Washburn MP. (2004) Brief Funct. Genomic Proteomic, 3(3):280-6.

Technological developments in proteomics have had a dramatic impact on biology in recent years. One of these developments--named multidimensional protein identification technology (MudPIT)--couples two-dimensional chromatography of peptides in mass spectrometry-compatible solutions directly to tandem mass spectrometry, allowing for the identification of proteins from highly complex mixtures. Since the initial descriptions of MudPIT, this approach has been implemented in the analysis of whole proteomes, organelles and protein complexes. Key aspects of many of the analyses are the validation of MudPIT datasets with alternate strategies and the integration of MudPIT datasets with other biochemical, cell biology or molecular biology approaches. This paper presents strategies for validating MudPIT datasets and incorporating these datasets into biologically driven experimental design.


6. Analysis of protein composition using multidimensional chromatography and mass spectrometry

Link AJ, Jennings JL, Washburn MP. (2004) Curr. Protoc. Protein. Sci. Chapter 23:Unit 23.1.

Multidimensional liquid chromatography of peptides produced by protease digestion of complex protein mixtures followed by tandem mass spectrometry can be coupled with automated database searching to identify large numbers of proteins in complex samples. These methods avoid the limitations of gel electrophoresis and in-gel digestions by directly identifying protein mixtures in solution.


5. Sato S, Tomomori-Sato C, Parmely TJ, Florens L, Zybailov B, Swanson SK, Banks CA, Jin J, Cai Y, Washburn MP, Conaway JW, Conaway RC. (2004) A Set of Consensus Mammalian Mediator Subunits Identified by Multidimensional Protein Identification Technology. Mol. Cell, Jun 4;14(5):685-91. Abstract

4. Tomomori-Sato C, Sato S, Parmely TJ, Banks CA, Sorokina I, Florens L, Zybailov B, Washburn MP, Brower CS, Conaway RC, Conaway JW. (2004) A mammalian mediator subunit that shares properties with Saccharomyces cerevisiae mediator subunit Cse2. J. Biol. Chem., Feb 13;279(7):5846-51. Epub 2003 Nov 24. Abstract

3. Kusch T, Florens L, Macdonald WH, Swanson SK, Glaser RL, Yates JR 3rd, Abmayr SM, Washburn MP, Workman JL. (2004) Acetylation by Tip60 Is Required for Selective Histone Variant Exchange at DNA Lesions. Science, Dec 17;306(5704):2084-7. Epub 2004 Nov 4. Abstract

2. Lee KK, Prochasson P, Florens L, Swanson SK, Washburn MP, Workman JL. (2004) Proteomic analysis of chromatin-modifying complexes in Saccharomyces cerevisiae identifies novel subunits. Biochem. Soc. Trans., 32(Pt 6):899-903. Abstract

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1. Soft landing for protein chips

Washburn MP (2003) Nat. Biotechnol., 21(10):1156-7.

Protein arrays can now be generated by mass spectrometry.


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