Biology subjects

Aebersold, R.

Publications and source records attributed to Aebersold, R..

At least 37 records · Page 2Linked to original sources

bioRxiv2021

Novel biochemical, structural and systems insights into inflammatory signaling revealed by contextual interaction proteomics

Protein-protein interactions (PPI) represent the main mode of the proteome organization in the cell. In the last decade, several large-scale representations of PPI networks have captured generic aspects of the functional organization of network components, but mostly lack the context of cellular states. However, the generation of contextual representations of PPI networks is essential for structural and systems-level modeling of biological processes and remains an unsolved challenge. In this study we describe an integrated experimental/computational strategy to achieve a contextualized modeling of PPI. This strategy defines the composition, stoichiometry, spatio-temporal organization and cellular requirements for the formation of target assemblies. We used this approach to generate an integrated model of the formation principles and architecture of a large signalosome, the TNF-receptor signaling complex (TNF-RSC). Overall, we show that the integration of systems- and structure-level information provides a generic, largely unexplored link between the modular proteome and cellular function. Significance StatementIn this work, we propose a critical shift in the way we analyze, and think the study of, protein-protein interactions (PPI), and present an experimental and computational framework to model them in the cellular context. We applied this framework to the signalosome tumor necrosis factor receptor signaling complex (TNF-RSC), and generated an integrated model of its formation and architecture that provides new insights and resolved controversies regarding its organization and regulation. To achieve a contextual modelling of PPIs, we first optimized and developed, and then combined, approaches to map the composition of a target complex, its absolute stoichiometry, its spatial organization and assembly/disassembly dynamics, its temporal dependence on signaling, and its reliance on cellular resources.

systems biology↗

bioRxiv2021

Molecular architecture of the human tRNA ligase complex

RtcB enzymes are RNA ligases that play essential roles in tRNA splicing, unfolded protein response, and RNA repair. In metazoa, RtcB functions as part of a five-subunit tRNA ligase complex (tRNA-LC) along with Ddx1, Cgi-99, Fam98B and Ashwin. The human tRNA-LC or its individual subunits have been implicated in additional cellular processes including microRNA maturation, viral replication, DNA double-strand break repair and mRNA transport. Here we present a biochemical analysis of the inter-subunit interactions within the human tRNA-LC along with crystal structures of the catalytic subunit RTCB and the N-terminal domain of CGI-99. We show that the core of the human tRNA-LC is assembled from RTCB and the C-terminal alpha-helical regions of DDX1, CGI-99, and FAM98B, all of which are required for complex integrity. The N-terminal domain of CGI-99 displays structural homology to calponin-homology domains, and CGI-99 and FAM98B associate via their N-terminal domains to form a stable subcomplex. The crystal structure of GMP-bound RTCB reveals divalent metal coordination geometry in the active site, providing insights into its catalytic mechanism. Collectively, these findings shed light on the molecular architecture and mechanism of the human tRNA ligase complex, and provide a structural framework for understanding its functions in cellular RNA metabolism.

biochemistry↗

bioRxiv2021

All driven by energy demand? Integrative comparison of metabolism of Enterococcus faecalis wildtype and a glutamine synthase mutant

Lactic acid bacteria (LAB) play a significant role in biotechnology, e.g. food industry, but also in human health. Many LAB genera have developed a multidrug resistance in the past few years, becoming a serious problem in controlling hospital germs all around the world. Enterococcus faecalis accounts for a large part of the human infections caused by LABs. Therefore, studying its adaptive metabolism under various environmental conditions is particularly important. In this study, we investigated the effect of glutamine auxotrophy ({Delta}glnA mutant) on metabolic and proteomic adaptations of E. faecalis in response to a changing pH in its environment. Changing pH values are part of its natural environment in the human body, but also play a role in food industry. We compared the results to those of the wildtype. Our integrative method, using a genome-scale metabolic model, constrained by metabolic and proteomic data allows us to understand the bigger picture of adaptation strategies in this bacterium. The study showed that energy demand is the decisive factor in adapting to a new environmental pH. The energy demand of the mutant was higher at all conditions. It has been reported that {Delta}glnA mutants of bacteria are energetically less effective. With the aid of our data and model we are able to explain this phenomenon as a consequence of a failure to regulate glutamine uptake and the costs for the import of glutamine and the export of ammonium. Methodologically, it became apparent that taking into account the non-specificity of amino acid transporters is important for reproducing metabolic changes with genome-scale models since it affects energy balance.

systems biology↗

bioRxiv2021

PHENOTYPIC CHARACTERIZATION OF TWO NOVEL CELL LINE MODELS OF CASTRATION RESISTANT PROSTATE CANCER.

BACKGROUNDResistance to androgen deprivation therapies is a major driver of mortality in advanced prostate cancer. Therefore, there is a need to develop new pre-clinical models that allow the investigation of resistance mechanisms and the assessment of drugs for the treatment of castration resistant prostate cancer. METHODSWe generated two novel cell line models (LAPC4-CR and VCaP-CR) which were derived by passaging LAPC4 and VCaP cells in vivo and in vitro under castrate conditions. We performed detailed transcriptomic (RNA-seq) and proteomic analyses (SWATH-MS) to delineate expression differences between castration-sensitive and castration-resistant cell lines. Furthermore, we characterized the in vivo and in vitro growth characteristics of the novel cell line models. RESULTSThe two cell line derivatives LAPC4-CR and VCaP-CR showed castration resistant growth in vitro and in vivo which was only minimally inhibited by AR antagonists, enzalutamide and bicalutamide. High-dose androgen treatment resulted in significant growth arrest of VCaP-CR but not in LAPC4-CR cells. Both cell lines maintained AR expression, but exhibited distinct expression changes on the mRNA and protein level. Integrated analyses including data from LNCaP and the previously described castration resistant LNCaP-abl cells revealed an expression signature of castration resistance. CONCLUSIONSThe two novel cell line models LAPC4-CR and VCaP-CR and their comprehensive characterization on the RNA and protein level represent important resources to study the molecular mechanisms of castration resistance.

cancer biology↗

bioRxiv2021

Principles of gene regulation quantitatively connect DNA to RNA and proteins in bacteria

Bacteria allocate their proteome to cellular functions differently in different growth conditions. It is largely unknown how such allocation arises from known mechanisms of gene regulation while constrained by limited translation capacity and fixed protein density. Here, we performed absolute transcriptomic and proteomic analysis for E. coli across many conditions, obtaining a plethora of results on promoters and mRNAs characteristics that clash with conventional expectations: the majority of mRNAs exhibit similar translational efficiencies, while the promoter strengths are vastly different across genes. These characteristics prescribe two principles of gene regulation guiding bacteria to attain the desired protein allocation under global constraints: Total transcriptional output is tightly coordinated with ribosomal activity, and the concentrations of individual proteins are largely set by transcription. These two principles lead to a quantitative formulation of Central Dogma which unravels the complex relationship between gene regulatory activities and mRNA/protein concentrations across conditions. The knowledge obtained will be invaluable for accurately inferring gene regulatory interactions from omics data, as well as for guiding the design of genetic circuits for synthetic biology applications in E. coli and other organisms.

systems biology↗

bioRxiv2021

Towards a systematic characterization of protein complex function: a natural language processing and machine-learning framework

It is a general assumption of molecular biology that the ensemble of expressed molecules, their activities and interactions determine biological processes, cellular states and phenotypes. Quantitative abundance of transcripts, proteins and metabolites are now routinely measured with considerable depth via an array of "OMICS" technologies, and recently a number of methods have also been introduced for the parallel analysis of the abundance, subunit composition and cell state specific changes of protein complexes. In comparison to the measurement of the molecular entities in a cell, the determination of their function remains experimentally challenging and labor-intensive. This holds particularly true for determining the function of protein complexes, which constitute the core functional assemblies of the cell. Therefore, the tremendous progress in multi-layer molecular profiling has been slow to translate into increased functional understanding of biological processes, cellular states and phenotypes. In this study we describe PCfun, a computational framework for the systematic annotation of protein complex function using Gene Ontology (GO) terms. This work is built upon the use of word embedding-- natural language text embedded into continuous vector space that preserves semantic relationships-- generated from the machine reading of 1 million open access PubMed Central articles. PCfun leverages the embedding for rapid annotation of protein complex function by integrating two approaches: (1) an unsupervised approach that obtains the nearest neighbor (NN) GO term word vectors for a protein complex query vector, and (2) a supervised approach using Random Forest (RF) models trained specifically for recovering the GO terms of protein complex queries described in the CORUM protein complex database. PCfun consolidates both approaches by performing the statistical test for the enrichment of the top NN GO terms within the child terms of the predicted GO terms by RF models. Thus, PCfun amalgamates information learned from the gold-standard protein-complex database, CORUM, with the unbiased predictions obtained directly from the word embedding, thereby enabling PCfun to identify the potential functions of putative protein complexes. The documentation and examples of the PCfun package are available at https://github.com/sharmavaruns/PCfun. We anticipate that PCfun will serve as a useful tool and novel paradigm for the large-scale characterization of protein complex function.

bioinformatics↗

bioRxiv2020

Systematic detection of functional proteoform groups from bottom-up proteomic datasets

The cellular proteome, the ensemble of proteins derived from a genome, catalyzes and controls thousands of biochemical functions that are the basis of living cells. Whereas the protein coding regions of the genome of the human and many other species are well known, the complexity and composition of proteomes largely remains to be explored. This task is challenging because mechanisms including alternative splicing and post-translational modifications generally give rise to multiple distinct, but related proteins - proteoforms - per coding gene that expand the functional capacity of a cell. Bottom-up proteomics is a mass spectrometric method that infers the identity and quantity of proteins from the measurement of peptides derived from these proteins by proteolytic digestion. Whereas bottom-up proteomics has become the method of choice for the detection of translation products from essentially any gene, the inherent missing link between measured peptides and their parental proteins has so far precluded the systematic assessment of proteoforms and thus limited the resolution of proteome maps. Here we present a novel, data-driven strategy to assign peptides to unique functional proteoform groups based on peptide correlation patterns across large bottom-up proteomic datasets. Our strategy does not fully characterize specific proteoforms, as is achievable in top-down approaches. Rather, it clusters peptides into functional proteoform groups that are directly linked to the biological context of the study. This allows the detection of tens to hundreds of proteoform groups in an untargeted fashion from bottom-up proteomics experiments. We applied the strategy to two types of bottom-up proteomic datasets. The first is a protein complex co-fractionation dataset where native complexes across two different cell cycle stages were resolved and analyzed. Here, our approach enabled the systematic detection and evaluation of assembly specific proteoforms at an unprecedented scale. The second is a protein abundance vs. sample data matrix typical for bottom-up cohort studies consisting of tissue samples from the mouse BXD genetic reference panel. In either data type the method detected state-specific proteoform groups that could be linked to distinct molecular mechanisms including proteolytic cleavage, alternative splicing and phosphorylation. We envision that the presented approach lays the foundation for a systematic assessment of proteoforms and their functional implications directly from bottom-up proteomic datasets.

systems biology↗

bioRxiv2020

SWATH-MS co-expression profiles reveal paralogue interference in protein complex evolution

Understanding the conservation and evolution of protein complexes is of critical value to decode their function in physiological and pathological processes. One prominent proposal posits gene duplication as a potential mechanism for protein complex evolution. In this study we take advantage of large-scale proteome expression datasets to systematically investigate the role of paralogues, and specifically self-interacting paralogues, in shaping the evolutionary trajectories of protein complexes. First, we show that protein co-expression derived from quantitative proteomic matrices is a good indicator for complex membership and is conserved across species. Second, we suggest that paralogues are commonly strongly co-expressed and that for the subset of paralogues that show diverging co-expression patterns, the divergent co-expression patterns reflect both sequence and functional divergence. Finally, on this basis, we show that homomeric paralogues known to be part of protein complexes display a unique co-expression pattern distribution, with a subset of them being highly diverging. These findings support the idea that homomeric paralogues can avoid cross-interference by diversifying their expression patterns, and corroborates the role of this mechanism as a force shaping protein complex evolution and specialization.

evolutionary biology↗

bioRxiv2020

The Molecular Landscape of the Aging Mouse Liver

Systems biology approaches often use inferred networks of gene expression and metabolite data to identify regulatory factors and pathways connected with phenotypic variance. Generally, study-specific multi-layer "Omics" datasets are used to contextualize generic molecular networks. In this regard separating upstream causal mechanisms, downstream biomarkers, and incidental correlations remains a significant challenge, yet it is essential for designing mechanistic experiments. To address this, we designed a study following a population of 2157 individuals from 89 isogenic BXD mouse strains across their lifespan to identify molecular interactions among genotype, environment, age (GxExA) and metabolic fitness. Each strain was separated into two cohorts, one fed low fat (6% cal/fat) and the other high fat (60% cal/fat) diets. Tissues were collected for 662 individuals (309 cohorts) diverging across age (7, 12, 18, and 24 months), diet, sex, and strain. Transcriptome, proteome, and metabolome data were generated for liver. Of these we identified linear relations among these molecular data with lifespan for the same genomes of mice (Roy et al. 2020), and we defined [~]1100 novel protein-coding genes associated with longevity. We knocked down the ortholog of Ctsd in C. elegans. The treatment reduced longevity both in wildtype and in mutant long-lived strains, thus validating the prediction. Next, to assess the molecular impact of GxExA on gene expression, the multi-omics data was parsed into metabolic networks where connectivity varied due to the independent variables. Differences in edge strengths connecting nodes in these molecular networks according to each variable enabled causal inference by using stability selection, with roughly 21% of novel gene-pathway connections being causally affected by diet and/or age. For instance, Chchd2 is activated by aging and drives changes in the proteasome, oxidative phosphorylation, and mitochondrial translation transcriptional networks. Together, we have developed a large multi-omics resource for studying aging in the liver, and a resource for turning standard associations into causal networks.

systems biology↗

bioRxiv2020

Mapping specificity, entropy, allosteric changes and substrates in blood proteases by a high-throughput protease screen

Proteases are among the largest protein families in eukaryotic phylae with more than 500 genetically encoded proteases in humans. By cleaving a wide range of target proteins, proteases are critical regulators of a vast number of biochemical processes including apoptosis and blood coagulation. Over the last 20 years, knowledge of proteases has been drastically expanded by the development of proteomic approaches to identify and quantify proteases and their substrates. In spite of their merits, some of these methods are laborious, not scalable or incompatible with native environments. Consequentially, a large number of proteases remain poorly characterized. Here, we introduce a simple proteomic method to profile protease activity based on isolation of protease products from native lysates using a 96FASP filter and their analysis in a mass spectrometer. The method is significantly faster, cheaper, technically less demanding, easily multiplexed and produces accurate protease fingerprints in near-native conditions. By using the blood cascade proteases as a case study we obtained protease substrate profiles of unprecedented depth that can be reliably used to map specificity, entropy and allosteric changes of the protease and to design fluorescent probes and predict physiological substrates. The native protease characterization method is comparable in performance, but largely exceeds the throughput of current alternatives.

biochemistry↗

bioRxiv2020

Systematic protein complex profiling and differential analysis from co-fractionation mass spectrometry data

Protein complexes, macro-molecular assemblies of two or more proteins, play vital roles in numerous cellular activities and collectively determine the cellular state. Despite the availability of a range of methods for analysing protein complexes, systematic analysis of complexes under multiple conditions has remained challenging. Approaches based on biochemical fractionation of intact, native complexes and correlation of protein profiles have shown promise, for instance in the combination of size exclusion chromatography (SEC) with accurate protein quantification by SWATH/DIA-MS. However, most approaches for interpreting co-fractionation datasets to yield complex composition, abundance and rearrangements between samples depend heavily on prior evidence. We introduce PCprophet, a computational framework to identify novel protein complexes from SEC-SWATH-MS data and to characterize their changes across different experimental conditions. We demonstrate accurate prediction of protein complexes (AUC >0.99 and accuracy around 97%) via five-fold cross-validation on SEC-SWATH-MS data, show improved performance over state-of-the-art approaches on multiple annotated co-fractionation datasets, and describe a Bayesian approach to analyse altered protein-protein interactions across conditions. PCprophet is a generic computational tool consisting of modules for data pre-processing, hypothesis generation, machine-learning prediction, post-prediction processing, and differential analysis. It can be applied to any co-fractionation MS dataset, independent of separation or quantitative LC-MS workflow employed, and to support the detection and quantitative tracking of novel protein complexes and their physiological dynamics.

systems biology↗

bioRxiv2020

JARID2 and AEBP2 regulate PRC2 activity in the presence of H2A ubiquitination or other histone modifications

The Polycomb repressive complexes PRC1 and PRC2 functionally interact to coordinate cell type identity by the epigenetic regulation of gene expression. It has been proposed that PRC2 is recruited to genomic loci via the recognition of PRC1-mediated mono-ubiquitination of histone H2A at lysine 119 (H2AK119ub1), but the mechanism of this process remains poorly understood. Here, we report the cryo-EM structure of human PRC2 with cofactors JARID2 and AEBP2 bound to a nucleosome substrate containing H2AK119ub1. We find that JARID2 and AEBP2 each interact with one of the two ubiquitin molecules in the nucleosome. A ubiquitin-interaction motif (UIM) in JARID2 is sandwiched between ubiquitin and the histone H2A-H2B acidic patch. Simultaneously, the tandem zinc-fingers of AEBP2 interact with the second ubiquitin and the histone H2A-H2B surface on the opposite side of the nucleosome. JARID2 plays a dual role in the H2AK119ub1 dependent stimulation of PRC2 through interactions with both EED via its K116 trimethylation and with the H2AK119-ubiquitin. AEBP2, on the other hand, appears to primarily serve as a scaffold contributing to the interaction between PRC2 and the H2AK119ub1 nucleosome. Our structure also provides a detailed visualization of the EZH2-nucleosome interface, revealing a segment of EZH2 (named "bridge helix") that is stabilized as it bridges the EZH2(SET) domain, the H3 tail and the nucleosomal DNA. In addition to the role played by AEBP2 and JARID2 in PRC2 regulation by H2AK119ub1 recognition, we also observe that the presence of these cofactors partially overcomes the inhibitory effect that H3K4- and H3K36-trimethylation have on core PRC2. Together, our results reveal the central role played by cofactors JARID2 and AEBP2 in orchestrating the crosstalk between histone post-translational modifications and PRC2 methyltransferase activity.

biophysics↗

bioRxiv2020

Proteogenomic heterogeneity of localized human prostate cancer progression

BackgroundTumor-specific genomic aberrations are routinely determined by high throughput genomic measurements. It remains unclear though, how complex genome alterations affect molecular networks through changing protein levels, and consequently biochemical states of tumor tissues. ResultsHere, we investigated the propagation of genomic effects along the axis of gene expression during prostate cancer progression. For that, we quantified genomic, transcriptomic and proteomic alterations based on 105 prostate samples, consisting of benign prostatic hyperplasia regions and malignant tumors, from 39 prostate cancer patients. Our analysis revealed convergent effects of distinct copy number alterations impacting on common downstream proteins, which are important for establishing the tumor phenotype. We devised a network-based approach that integrates perturbations across different molecular layers, which identified a sub-network consisting of nine genes whose joint activity positively correlated with increasingly aggressive tumor phenotypes and was predictive of recurrence-free survival. Further, our data revealed a wide spectrum of intra-patient network effects, ranging from similar to very distinct alterations on different molecular layers. ConclusionsThis study uncovered molecular networks with remarkably convergent alterations across tumor sites and patients, but it also exposed a diversity of network effects: we could not identify a single sub-network that was perturbed in all high-grade tumor regions.

systems biology↗

bioRxiv2020

Network analysis identifies regulators of lineage-specific phenotypes in Mycobacterium tuberculosis

The Mycobacterium tuberculosis (Mtb) complex includes ten phylogenetically distinct human-adapted lineages with varying geographical distributions and pathogenicity. Lineage 1 (L1) is associated with low virulence, while Lineage 2 (L2) is linked to hyper-virulence, increased transmission, and drug resistance. We performed multi-layer comparative analyses integrating whole-genome sequencing with quantitative transcriptomic and proteomic profiling of a panel of L1 and L2 clinical strains, each grown under two in vitro conditions. Our data revealed variable correlations between transcript and protein levels across strains and gene categories, indicating lineage-specific post-translational regulation. Transcriptional and translational differences scaled with phylogenetic distance, with one in three SNPs on average leading to gene expression changes. A newly developed genome-scale transcriptional regulatory model identified master transcription factors--closely linked to the sigma factor network--whose targets were differentially expressed between L1 and L2. Notably, DosR proteins showed higher basal levels and a stronger nitric oxide (NO) response in L2. Time-course validation using an Rv1985c induction and wild-type H37Rv under hypoxia and reaeration confirmed the role of Rv1985c in the differentially expressed genes between L1 and L2. These findings suggest that limited genetic variation can translate into significant phenotypic differences via differential regulation of key transcriptional networks. HighlightsO_LIProteomic and transcriptomic characterization of fully sequenced diverse L1 and L2 clinical strains of Mtb. C_LIO_LIPost translational control mechanisms for regulatory and virulence genes are mitigated in Mtb L2. C_LIO_LIA genome-scale transcriptional framework identifies DosR, Rv1985c, Lsr2 and Rv0691c as master transcription factors responsible for differential target gene expression in L2 compared to L1 strains. C_LIO_LIL1 and L2 DosR proteins respond differently to nitric oxide stress, thus determining a relevant phenotype. C_LIO_LITime-course phenotypic and transcriptomic data under hypoxia and reaeration validate Rv1985c as a key transcription factor differentiating L2 and L1 strains. C_LI

systems biology↗

bioRxiv2019

Mitochondrial translation and dynamics synergistically extend lifespan in C. elegans through HLH-30

Mitochondrial form and function, such as translation, are closely interlinked in homeostasis and aging. Inhibiting mitochondrial translation is known to increase lifespan in C. elegans, which is accompanied by a fragmented mitochondrial network. However, the causality between mitochondrial translation and morphology in longevity remains uncharacterized. Here, we show in C. elegans that disrupting mitochondrial network homeostasis by either blocking fission or fusion synergizes with the reduced mitochondrial translation to substantially prolong lifespan and stimulate stress response such as the mitochondrial unfolded protein response, UPRMT. Conversely, immobilizing the mitochondrial network through a simultaneous abrogation of fission and fusion reverses the lifespan increase induced by mitochondrial translation inhibition. Furthermore, we find that the synergistic effect of inhibiting both mitochondrial translation and dynamics on lifespan, despite stimulating UPRMT, does not require it. Instead, this lifespan-extending synergy is exclusively dependent on the lysosome biogenesis and autophagy transcription factor HLH-30/TFEB. Altogether, our study reveals the mechanistic connections between mitochondrial translation and dynamics in regulating longevity. SUMMARYMitochondrial form and function are intimately intertwined. Liu et al. find the synergistic effect of inhibiting both mitochondrial translation and dynamics on lifespan. This synergy is dependent on the induction of lysosome biogenesis through the nuclear localization of HLH-30.

cell biology↗

bioRxiv2019

SECAT: Quantifying differential protein-protein interaction states by network-centric analysis

Protein-protein interactions (PPIs) play critical functional and regulatory roles in virtually all cellular processes. They are essential for the formation of macromolecular complexes, which in turn constitute the basis for extended protein interaction networks that determine the functional state of a cell. We and others have previously shown that chromatographic fractionation of native protein complexes in combination with bottom-up mass spectrometric analysis of consecutive fractions supports the multiplexed characterization and detection of state-specific changes of protein complexes. In this study, we describe a computational approach that extends the analysis of data from the co-fractionation / mass spectrometric analysis of native complexes to the level of PPI networks, thus enabling a qualitative and quantitative comparison of the proteome organization between samples and states. The Size-Exclusion Chromatography Algorithmic Toolkit (SECAT) implements a novel, network-centric strategy for the scalable and robust differential analysis of PPI networks. SECAT and its underlying statistical framework elucidate differential quantitative abundance and stoichiometry attributes of proteins in the context of their PPIs. We validate algorithm predictions using publicly available datasets and demonstrate that SECAT represents a more scalable and effective methodology to assess protein-network state and that our approach thus obviates the need to explicitly infer individual protein complexes. Further, by differential analysis of PPI networks of HeLa cells in interphase and mitotic state, respectively, we demonstrate the ability of the algorithm to detect PPI network differences and to thus suggest molecular mechanisms that differentiate cellular states.

systems biology↗

bioRxiv2019

Orthogonal proteomic platforms and their implications for the stable classification of high-grade serous ovarian cancer subtypes

The National Cancer Institute (NCI) Clinical Proteomic Tumor Analysis Consortium (CPTAC) has established a two-dimensional liquid chromatography-tandem mass spectrometry (2DLC-MS/MS) workflow using isobaric tagging to compare protein abundance across samples. The workflow has been used for large-scale clinical proteomic studies with deep proteomic coverage within and outside of CPTAC. SWATH-MS, an instance of data-independent acquisition (DIA) proteomic methods, was recently developed as an alternate proteomic approach. In this study, we analyzed remaining aliquots of peptides using SWATH-MS from the original retrospective TCGA samples generated for the CPTAC ovarian cancer proteogenomic study (Zhang et al., 2016). The SWATH-MS results indicated that both methods confidently identified differentially expressed proteins in enriched pathways associated with the robust Mesenchymal subtype of high-grade serous ovarian cancer (HGSOC) and the homologous recombination deficient tumors also present in the original study. The results demonstrated that SWATH/DIA-MS presents a promising complementary or orthogonal alternative to the CPTAC harmonized proteomic method, with the advantages of simpler, faster, and cheaper workflows, as well as lower sample consumption. However, the SWATH/DIA-MS workflow resulted in shallower proteome coverage. Overall, we concluded that both analytical methods are suitable to characterize clinical samples such as in the high-grade serous ovarian cancer study, providing proteomic workflow alternatives for cancer researchers depending on the specific goals and context of the studies.

cancer biology↗

bioRxiv2019

Chemical genetics of AGC-kinases reveals shared targets of Ypk1, Protein Kinase A and Sch9

Protein phosphorylation cascades play a central role in the regulation of cell growth and protein kinases PKA, Sch9 and Ypk1 take centre stage in regulating this process in S. cerevisiae. To understand how these kinases co-ordinately regulate cellular functions we compared the phospho-proteome of exponentially growing cells without and with acute chemical inhibition of PKA, Sch9 and Ypk1. Sites hypo-phosphorylated upon PKA and Sch9 inhibition were preferentially located in RRxS/T-motifs suggesting that many are directly phosphorylated by these enzymes. Interestingly, when inhibiting Ypk1 we not only detected several hypo-phosphorylated sites in the previously reported RxRxxS/T-, but also in an RRxS/T-motif. Validation experiments revealed that neutral trehalase Nth1, a known PKA target, is additionally phosphorylated and activated downstream of Ypk1. Signalling through Ypk1 is therefore more closely related to PKA- and Sch9-signalling than previously appreciated and may perform functions previously only attributed to the latter kinases.

cell biology↗