Mass spectrometry‐based protein–protein interaction networks for the study of human diseases

Molecular Systems Biology, Journal Year: 2021, Volume and Issue: 17(1)

Published: Jan. 1, 2021

Review12 January 2021Open Access Mass spectrometry-based protein–protein interaction networks for the study of human diseases Alicia L Richards orcid.org/0000-0002-4869-2945 Quantitative Biosciences Institute (QBI), University California San Francisco, CA, USA J. David Gladstone Institutes, Department Cellular and Molecular Pharmacology, Search more papers by this author Manon Eckhardt orcid.org/0000-0001-8143-6129 Nevan J Krogan Corresponding Author [email protected] orcid.org/0000-0003-4902-337X Information Richards1,2,3, Eckhardt1,2,3 *,1,2,3 1Quantitative 2J. 3Department *Corresponding author. Tel: +1 415 476 2980; E-mail: Systems Biology (2021)17:e8792https://doi.org/10.15252/msb.20188792 PDFDownload PDF article text main figures. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract A better understanding molecular mechanisms underlying disease is key expediting development novel therapeutic interventions. Disease are often mediated interactions between proteins. Insights into physical rewiring in response mutations, pathological conditions, or pathogen infection can advance our etiology, progression, pathogenesis lead identification potential druggable targets. Advances quantitative mass spectrometry (MS)-based approaches have allowed unbiased mapping these disease-mediated changes on a global scale. Here, we review MS techniques that been instrumental at system-level, discuss challenges associated with methodologies as well advancements aim address challenges. An overview examples from diverse contexts illustrates MS-based revealing mechanisms, pinpointing new targets, eventually moving toward personalized applications. Introduction Identifying principal basis crucial successful prevention, diagnosis, treatment. In past two decades, scientists placed lot hope large genomic studies deciphering mechanisms. Nevertheless, despite wealth information gathered, mechanism most remains unknown. This be explained least part fact many complex do not follow classical genotype phenotype model. They may result multiple genetic changes, epigenetic modifications, pathogen. The fallacy expecting simple explain phenotypes has demonstrated especially case cancer, where distinct collection mutations exclusive given cancer type (Junttila de Sauvage, 2013; Leiserson et al, 2015). Additionally, single gene different diseases, corresponding proteins having several functions cellular (Nadeau, 2001). Consequently, extracting useful diagnostic prognostic genetics alone difficult. Considering context disrupted processes help overcome challenge. biology approaches, which provide comprehensive picture biological process quantifying all observable components their relationships, well-suited understand influence network interconnected pathways. Proteins networks. Often, individual perform any isolation but accomplish task through direct other As such, studying (PPI) become powerful tool identifying functional consequences variation. approach, disease-related mapped vital PPIs processes. Comparison states wild-type reference map—either introduction carrying exogenous expression proteins—promises reveal how change during (Krogan 2015; Willsey 2018). directly responsible adaptation changes. Because connectivity proteins, impact mutation restricted specific product. Instead, it affects entire accordingly activity whole subset Instead focusing genes loci implicated disease, PPI-based analyses parts pathway connections changed state, thus offering an alternative identify mutation's function. Interacting visualized using network-based nodes representing "bait" interest PPI study. Nodes connected edges interacting identified Affinity Purification Spectrometry (AP-MS), proximity labeling, Cross-Linking (XL-MS), types experiments. performed both diseased state non-diseased WT states, variations regulation monitored. perturbations networks, including complete loss interactions, partial gain (Fig 1). suggests small network, such particular gene, cause significant across system. Changes partners protein, either progression following infection, might contribute potentially linking phenotype. Applying approach clinical advantages. finding protein biochemical its also play role same processes, providing mechanistic explanations implications beyond protein. Figure 1. systems-level converting pathway-level dataGenetic variants, occur rarely individuals used Comparisons introduced aid determining significance mutations. Similarly, pathogenic determine host pathways hijacked over course infection. Download figure PowerPoint current research disease. Throughout, will highlight field, advances some them. For detailed examination tools relying detection, refer reader reviews (e.g., Snider Beltran 2017). methods Liquid chromatography-MS (LC-MS) sensitive, accurate, selective method quantify (Richards Aebersold Mann, 2016). One major benefits nature proteomics. contrast PPIs, yeast-2-hybrid (Y2H), maps physical, binary predetermined set (Walhout Vidal, general workflow utilizing discovery develop outlined Box 1 illustrated Fig 2. Below, summarize variety that, when combined MS, allow proteome-level analysis systems. Overview techniques(A) Workflow bottom-up Preparing proteomic samples LC-MS/MS requires extraction, proteolysis, and, optionally, peptide-level fractionation. Online LC separation peptide mixtures introduces analytes spectrometer precursor fragment ion analysis. Tandem spectra matched theoretical generated silico garner sequences inference. (B) Label-free quantitation. Following digestion, each sample, equal amount peptides separately loaded column. Relative quantitation comparing extracted peak intensity runs dataset. (C) SILAC. During cell culture, "light" "heavy" versions amino acids metabolically incorporated samples. sample preparation, lysates mixed total ratios digested peptides. Intensities chromatograms MS1 scan relative abundances (D) Isobaric labeling. Each peptides, labeled unique isobaric label, ratios. MS/MS analysis, tag yields (E) Targeted MS. SRM, individually monitored quantified. first isolated, characteristic fragments Only masses selected user starts digesting mixture defined cleavage sites trypsin), separated liquid chromatography mass-to-charge (m/z) measured spectrometer. standard tandem experiments, sequence determined collecting second spectrum after induced fragmentation. Taken together, m/z data full then computationally search databases organism original 2A). To candidate interactors studies, "scored" accuracy interaction. oftentimes done combining parameters reproducibility, specificity, abundance detected scoring algorithms exists purpose, MiST, CompPASS, SAINT (Sowa 2009; Choi 2011; Teo 2014, 2016; Morris 2014; Verschueren methodology algorithm differs—for example, incorporates quality controls prey probability bait true positive, while CompPASS utilizes ultimately focus abundance, uniqueness, reproducibility distinguish contaminant background (Christianson 2011). output programs table filtered, scored imported visualization Cytoscape (Shannon 2003). addition computational assessing specificity appropriate controls, conditions 2B–E). allows unlimited number 2B). However, there limitations one them being comparison purposes, identical amounts should injected column When possible, normalization required. reduce bias, compared analyzed acquisition batch Randomization run order avoid systematic errors. Metabolic labeling Stable Isotope Labeling Amino Acids Cell Culture (SILAC) (TMT) labels multiplex increasing experimental throughput. SILAC stable heavy level 2C; Ong 2002; Szklarczyk 2019), tagging utilize NHS-activated molecules label free amines chemical tags vitro digestion 2D). All rely inclusion additional control added, so origin respective interactor traced (Ong Thompson 2003; 2014). Together, timepoints discriminate non-specific (Wiese 2007; Virreira Winter targeted strategies, parallel reaction monitoring (PRM) multiple/selective (MRM/SRM), validate greater consistency, sensitivity, (Lange 2008; Gallien 2012; Peterson 2012). Briefly, target assay development. These signature ions precise final experiment 2E). Among numerous contaminants copurified together interest. Therefore, necessary analyze way separates artifacts. done, part, careful design suitable controls. Importantly, unrelated tag, alone, need included (Jäger 2011b). GFP It unlikely form presumably false positives due epitope affinity capture (Morris contaminations. accessed via CRAPome database (Mellacheruvu 2013), public repository negative data, filtered out Contamination carryover overexpressed residual subsequent experiments actually present interactor. Strict wash steps required alleviate problem. purification (AP-MS) AP-MS 3A) tagging, short (for FLAG-, TAP-, Strep-Tag, c-myc (Chang, 2006)) fused interest—either construct under gene's endogenous promoter editing technologies like CRISPR-Cas9. resulting probe interacting, "prey" eliminating antibodies interest, would lower throughput immunoprecipitation (IP) easily purified matrix recognizing epitope. After washing eliminate interactors, 3. networks(A) General AP-MS. Bait endogenously tagged expressed cells, followed lysis LC-MS/MS. processing (BOX), Identification proximal promiscuous ligase cells. biotin, within fusion protein's radius subsequently lysed captured matrix. Direct cross-linked XL-MS. cross-linking reagent, cells digested, enriched cross-linker. LC-MS/MS, interpretation build high-throughput enabled 1,000s complexes large-scale models healthy states. largest assembly BioPlex database, has, date, compiled 56,533 10,961 HEK293T (Huttlin 2015, Publicly available sets these, hu.MAP 2.0 (Drew 2017; preprint: Drew 2020), represent important resources biomedical efforts spurred multitude discoveries further below. limitation milder than those typically employed Membrane hard problems extraction (Sastry Pankow Weaker transient prone steps. (TAP) affixes separate (Rigaut 1999), endure harsher His-tag) increase recovery rate lost regular (Puig comes disadvantage laborious preparation purification, artifacts Irrespective employed, remain issues, requiring selection Another lysis-induced mixing compartments normally interact, positive identifications. Possible solutions deconvolute effects compartment currently explored discussed section New Methodology. possible introducing N- C-terminus disrupt normal function, making advantageous test termini. note does readily differentiate indirect interactors. On hand, offers advantages earlier strategies (e.g. Y2H), high sensitivity quantification time (non-binary). detecting post-translational modifications (PTMs) (Matsuura 2008). generation, label-free value comparative whether Proximity represents complementary strategy traditional (Han case, expressing enzyme 3B). molecule substrate, covalent 10–20 nm range, capturing surrounding environment, lysis, denatured solubilized, enrichment biotinylated commonly streptavidin binding, strong binding biotin streptavidin, permits efficient AP-MS, allowing weak methodologies. procedure includes use detergents intact purification. Various established. BioID BirA, rendering promiscuous. BirA catalyzes transformation reactive form, resultant cloud reacts primary vicinity, biotinylation (Roux Subcellular include nuclear envelope (Kim 2016b), centrosome (Antonicka nucleus (preprint: Go cytoplasm (Redwine 2017), Golgi apparatus (Liu 2018), ER (Hoffman endosome, lysosome, mitochondrial cell–cell junctions (Fredriksson 2015), flagella (Kelly efficiency limited 2018; 2019). Due slow kinetics, 18–24 h produce sufficient material off-target background, somewhat restricts amenable BioID. timescale, generation static maps. BioID, BioID2, was developed Aquifex aeolicus. significantly smaller decreases disruption improved targeting localization subcellular 2016a). still 16 improve speed, Branon al (2018) directed evolution resulted faster-acting enzymatic variations: TurboID 15 miniTurbo 13 deletion N-terminal domain. enzymes comparable ten minutes. class arose peroxidases, catalyzing redox reactions. Horseradish peroxidase (HRP) best-studied suffers poor reducing environments (Trinkle-Mulcahy, Engineered ascorbic acid (APEX) drawback, genetically (Rhee Hung timed H2O2, APEX oxidizes phenol derivatives biotin-phenoxyl radicals covalently react electron rich acids, kinetics minutes (Martell rapid capabilities offer speed make investigate dynamically changing interactions. environments, retains cytosol peroxide criticized harmful effect prevents living organisms. Newer iterations seek toxicity issues times. recently introduced, contact-specific SplitID divides separate, inactive (Cho 2020). recombine close proximity, suited organelle contact sites, organelle, subsequently, C-terminal split separated, joined promote Experimental carefully considered before undertaking experiment. With techniques, neighboring throughout colocalize period, simply diffusion region, difficult really reside immediate environment (Lobingier without attached expected presence arise natural 2018) attach enrichment. Similar insertion C- terminus alter Prior generating enzyme-expressing line, C-termini tested ensure no (Sears possibility non-labeled fall outside therefore detected. N-terminus advantageous. Cross-linking (XL-MS) Although complex, members contact. XL-MS fill gap 3C). provides structural proximat

Language: Английский

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Protein interaction landscapes revealed by advanced in vivo cross-linking–mass spectrometry

Proceedings of the National Academy of Sciences, Journal Year: 2021, Volume and Issue: 118(32)

Published: Aug. 4, 2021

Significance Blueprints of in-cell protein interaction landscapes are essential for our understanding cellular structures and functions, which have been challenging to study at the systems level. Cross-linking–mass spectrometry (XL-MS) represents a high-throughput method global profiling networks can determine identity connectivity native PPIs simultaneously without cell engineering. While in vivo XL-MS experiments feasible, in-depth analyses remain difficult due technical limitations on sample preparation. Here, we developed new Alkyne-A-DSBSO–based platform that enabled us obtain most comprehensive PPI maps cells. This approach be adopted proteome-wide studies any organisms origins, thus advancing interactome biology beyond proteome abundance.

Language: Английский

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Ten Years of Extracellular Matrix Proteomics: Accomplishments, Challenges, and Future Perspectives

Molecular & Cellular Proteomics, Journal Year: 2023, Volume and Issue: 22(4), P. 100528 - 100528

Published: March 12, 2023

•ECM alterations cause or accompany diseases and disorders of all organ systems.•Proteomics is a method choice to profile the composition ECM tissues.•ECM proteomics can identify novel prognostic diagnostic biomarkers.•ECM uncover proteins playing functional roles in disease etiology.•Further technical advances are needed capture diversity proteoforms The extracellular matrix (ECM) complex assembly hundreds forming architectural scaffold multicellular organisms. In addition its structural role, conveys signals orchestrating cellular phenotypes. Alterations composition, abundance, structure, mechanics have been linked affecting physiological systems, including fibrosis cancer. Deciphering protein how it changes pathophysiological contexts thus first step toward understanding health development therapeutic strategies correct disease-causing alterations. Potentially, also represents vast, yet untapped reservoir biomarkers. characterized by unique biochemical properties that hindered their study: they large, heavily uniquely posttranslationally modified, highly insoluble. Overcoming these challenges, we others devised mass-spectrometry–based proteomic approaches define "matrisome," tissues. This part this review provides historical overview research presents latest now allow profiling healthy diseased second highlights recent examples illustrating has emerged as powerful discovery pipeline cancer third discusses remaining challenges limiting our ability translate findings clinical application proposes overcome them. Lastly, introduces readers resources available facilitate interpretation datasets. was once thought be impenetrable. Mass spectrometry–based proven tool decode ECM. light progress made over past decade, there reasons believe in-depth exploration matrisome within reach may soon witness translational proteomics. organisms (1Hynes R.O. evolution metazoan matrix.J. Cell Biol. 2012; 196: 671-679Crossref PubMed Scopus (177) Google Scholar, 2Adams J.C. Extracellular evolution: an overview.in: Keeley F.W. Mecham R.P. Evolution Matrix. Springer, Berlin, Heidelberg2013: 1-25https://doi.org/10.1007/978-3-642-36002-2_1Crossref 3Karamanos N.K. Theocharis A.D. Piperigkou Z. Manou D. Passi A. Skandalis S.S. et al.A guide functions matrix.FEBS J. 2021; 288: 6850-6912Crossref (34) Scholar). As such, guides cell polarization serves substrate migration, organizes cells into tissues organs, confers mechanical roles, exerts signaling through mechanotransduction (4Humphrey J.D. Dufresne E.R. Schwartz M.A. Mechanotransduction homeostasis.Nat. Rev. Mol. 2014; 15: 802-812Crossref (1185) 5Dooling L.J. Saini K. Anlaş A.A. Discher D.E. Tissue coevolves with fibrillar matrisomes fibrotic tissues.Matrix 2022; 111: 153-188Crossref (0) It cues interpreted via cell-surface receptors (e.g., integrins (6Kanchanawong P. Calderwood D.A. Organization, dynamics mechanoregulation integrin-mediated cell–ECM adhesions.Nat. 24: 142-161Crossref (7) Scholar), syndecans, adhesion GPCRs (7Liebscher I. Cevheroğlu O. Hsiao C.C. Maia A.F. Schihada H. Scholz N. GPCR research.FEBS 289: 7610-7630Crossref (5) Scholar)) orchestrate most, if not all, functions, from proliferation survival stemness differentiation. plays critical during development, growth, other processes wound healing aging (8Yamada K.M. Collins J.W. Cruz Walma Doyle Morales S.G. Lu al.Extracellular invasion tissue morphogenesis.Int. Exp. Pathol. 2019; 100: 144-152Crossref (47) 9Dzamba B.J. DeSimone D.W. sculpting embryonic tissues.Curr. Top Dev. 2018; 130: 245-274Crossref (49) 10Karamanos Neill T. Iozzo R.V. 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Chou Remodelling disease.Nat. 786-801Crossref (2349) 16Theocharis Karamanos multitasking player disease.FEBS 286: 2830-2869Crossref (190) Scholar) musculoskeletal system Ehlers–Danlos syndrome (17Malfait Castori M. Francomano C.A. Giunta Kosho Byers P.H. Ehlers-Danlos syndromes.Nat. Dis. Primers. 6: 64Crossref (82) arthritis), skin scleroderma (18Schulz J.N. Plomann Sengle G. Gullberg Krieg Eckes B. New developments on - emanating control myofibroblasts.Matrix 68–69: 522-532Crossref (48) epidermolysis bullosa (19Bruckner-Tuderman L. Has Disorders cutaneous basement membrane zone--the paradigm bullosa.Matrix 33: 29-34Crossref Scholar)), cardiovascular Marfan (20Cook J.R. Carta Galatioto Ramirez Cardiovascular manifestations related diseases; multiple causing similar phenotypes.Clin. Genet. 2015; 87: 11-20Crossref (52) respiratory (lung (21Zhou Y. 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Standardized assessment tumor-stroma ratio cancer: interobserver validation reproducibility potential factor.Clin. 14https://doi.org/10.1177/2632010X21989686Crossref 29van Pelt G.W. Sandberg T.P. Morreau Gelderblom van Krieken J.H.J.M. Tollenaar R.A.E.M. al.The tumour-stroma colon role impact.Histopathology. 197-206Crossref Nine 70-gene MammaPrint panel used early breast diagnosis (30Cardoso van't Veer Bogaerts Slaets Viale Delaloge S. al.70-Gene signature aid treatment decisions early-stage cancer.N. Engl. Med. 2016; 375: 717-729Crossref genes. present advantage being readily accessible, outside cells. Consequently, targeted delivery imaging agents (31Jailkhani Ingram Rashidian Rickelt Tian Mak al.Noninvasive tumor progression, metastasis, using nanobody targeting matrix.Proc. Nat. Acad. Sci. U. 116: 14181-14190Crossref 32Santimaria Moscatelli G.L. Giovannoni Neri Viti al.Immunoscintigraphic detection ED-B domain fibronectin, marker angiogenesis, cancer.Clin. 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Yet, while some elastin) families collagens, tenascins) extensively studied, whole, remained, until recently, largely underexplored (43Wilson matrix: but important proteome.Expert Proteomics. 2010; 7: 803-806Crossref (14) uncharted (44Filipe E.C. Chitty J.L. Cox Charting unexplored cancer.Int. 99: 58-76Crossref very allowing assemble capable withstanding significant stress deformations study global core, tend average 1045 amino acids long. undergo extensive intracellular posttranslational modifications (PTMs), glycosylation, lysine proline hydroxylation collagens collagen-domain-containing contribute stabilization triple-helical structure (45Rappu Salo A.M. Myllyharju Heino Role prolyl collagens.Essays Biochem. 63: 325-335Crossref glycation. higher-order structures established hydrogen bonds (46Buehler Nature designs tough collagen: explaining nanostructure fibrils.Proc. Natl. 2006; 103: 12285-12290Crossref (593) 47Shoulders M.D. Raines R.T. Collagen stability.Annu. 2009; 78: 929-958Crossref (2243) disulfide fibronectin dimers (48Schwarzbauer J.E. Fibronectins, fibrillogenesis, vivo functions.Cold 2011 Jul 1; a005041Crossref (280) covalent cross-links elastin (49Ozsvar Cain S.A. Baldock Tarakanova Weiss A.S. Tropoelastin assembly.Front. Bioeng. Biotechnol. 9643110Crossref (35) (50Ricard-Blum family.Cold a004978Crossref (1080) Scholar)). These making insoluble and, hence, challenging like SDS-PAGE, immunoprecipitation pull-down assays mass spectrometry (MS). Because high insolubility, underrepresented Further contributing underrepresentation fact that, apart few exceptions, small fraction mass. challenge comprehensive characterization broad range terms abundance. comprised abundant components, which generate many peptides (for 121 trypsin cleavage sites alpha 1 chain I), smaller secreted factors, such ECM-remodeling enzymes, growth morphogens, much lower limitation ECM, instrumentations methods fractionate peptide samples, will discussed here, key complexity different subproteomes applied (see below). attempts at ECM-rich tissues, cartilage, following enrichment employed SDS-PAGE 2D gel electrophoresis separate subsets solubilized, followed liquid chromatography coupled tandem (LC-MS/MS). studies reported up dozen proteins. At time, no feat instrumental helping shape field (51Wilson Cartilage proteomics: solutions advances.Proteomics Clin. Appl. 2008; 2: 251-263Crossref 52Lammi Häyrinen Mahonen Proteomic analysis cartilage- bone-associated samples.Electrophoresis. 27: 2687-2701Crossref 53Hattar Maller McDaniel Hansen K.C. Hedman Lyons al.Tamoxifen induces pleiotrophic mammary stroma resulting suppresses transformed phenotypes.Breast R5Crossref (53) 54Wilson Diseberg Gordon Zivkovic Tatarczuch Mackie al.Comprehensive cartilage formation maturation sequential extraction label-free quantitative proteomics.Mol. 1296-1313Abstract (63) 55Belluoccio Wilson Thornton D.J. Wallis Gorman J.J. mouse plate cartilage.Proteomics. 6549-6553Crossref (30) 56Hansen Kiemele O'Brien Shankar Fornetti al.An in-solution ultrasonication-assisted digestion improved proteome coverage.Mol. 8: 1648-1657Abstract (85) Of note, sample preparation protocols tailored account posed (insolubility, glycosylation), separation 1D resulted identification nearly 100 distinct (57Didangelos Yin X. Mandal Baumert Jahangiri Mayr Proteomics space components human aorta.Mol. 2048-2062Abstract (214) 58Didangelos Saje Smith Xu Q. abdominal aortic aneurysms: approach.Mol. 10https://doi.org/10.1074/mcp.M111.008128Abstract (146) However, most studies, known proteins, expected detected those were identified. One then ask: ensure capturing tissues? And indeed, faced when attempting characterize, unbiased manner, lack defined parts systematically annotate experimental output. result, days proteomics, listed "ECM" involved adhesions incorporated Conversely, prior knowledge existed would fail annotated belonging represented any attempt aiming states. became obvious analytical decipher discuss enhancement purpose biomarker target focus Special Issue Clinical Proteomics, article highlight selected performed samples rodent models show promise. organisms, zebrafish (59Chen W.C.W. Wang Missinato Park Long Liu H.J. al.Decellularized cardiac mammalian heart regeneration.Sci. Adv. 2e1600844Crossref (83) 60Garcia-Puig Mosquera Jiménez-Delgado García-Pastor Jorba Navajas al.Proteomics regeneration.Mol. 1745-1755Abstract 61Kessels M.Y. Huitema L.F.A. Boeren Kranenbarg Schulte-Merker Leeuwen JL skeletal matrix.PLoS One. 9e90568Crossref (32) drosophila (62Sessions A.O. Kaushik Parker Raedschelders Bodmer Eyk downregulation Drosophila preserves contractile function improves lifespan.Matrix 2017; 62: 15-27Crossref (15) planarians (63Sonpho E. Mann F.G. Levy Ross Guerrero-Hernández Florens al.Decellularization Enables planarian 20100137Abstract produced culture. advance fundamental disease. bottom-up MS-based but, worth noting modalities facets glycosylation patterns glycomics (64Raghunathan Sethi M.K. Klein Zaia glycomics, glycoproteomics molecules.Mol. 2138-2148Abstract (29) 65de Haan Pučić-Baković Novokmet Falck Lageveen-Kammeijer Razdorov al.Developments perspectives high-throughput glycomics: enabling thousands samples.Glycobiology. 32: 651-663Crossref 66Kellman B.P. Lewis N.E. Big-data tools connect glycan biosynthesis communication.Trends 46: 284-300Abstract (23) 67Riley N.M. Bertozzi C.R. Pitteri S.J. A pragmatic spectrometry-based glycoproteomics.Mol. 20100029Abstract fragments degradomics (68Haack Overall C.M. auf dem Keller Degradomics technologies exploration.Matrix 114: 1-17Crossref localization distribution MS (69Angel P.M. Comte-Walters Ball L.E. Talbot Brockbank K.G.M. al.Mapping formalin-fixed, paraffin-embedded MALDI spectrometry.J. Proteome 635-646Crossref (51) 70Clift C.L. Drake R.R. Angel Multiplexed serial enzyme digests formalin-fixed sections.Anal. Bioanal. Chem. 413: 2709-2719Crossref (8) 2012, published journal describing two-pronged (71Naba Clauser K.R. Hoersch Carr Hynes matrisome: silico definition normal matrices.Mol. 11https://doi.org/10.1074/mcp.M111.014647Abstract (668) While had attempted limitations described above decellularizing extracting guanidine hydrochloride), set tackle them all. brief, took differential solubility deplete non-ECM incubations extraction, decellularization, buffers concomitantly enriching Observing incubation 8 M urea mM DTT did fully solubilize ECM-enriched suspecting found material, processed "crude" M-urea-resuspended samples. We hypothesized deglycosylating enhance accessibility treated Peptide-N-glycosidase F (PNGaseF). further preincubated deglycosylated suspension LysC, protease digesting tightly folded tryptic digestion. To fractionated off-gel electrophoresis. Last, quantification stipulated ECM-specific PTMs hydroxylations variable database search. Indeed, 19% acid sequence I positions X Y X-Y-Gly repeats often hydroxylated parallel, developed robust nomenclature classify characteristic domain-based organization (72Hohenester Eng

Language: Английский

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A deep learning model of tumor cell architecture elucidates response and resistance to CDK4/6 inhibitors

Nature Cancer, Journal Year: 2024, Volume and Issue: 5(7), P. 996 - 1009

Published: March 5, 2024

Abstract Cyclin-dependent kinase 4 and 6 inhibitors (CDK4/6is) have revolutionized breast cancer therapy. However, <50% of patients an objective response, nearly all develop resistance during To elucidate the underlying mechanisms, we constructed interpretable deep learning model response to palbociclib, a CDK4/6i, based on reference map multiprotein assemblies in cancer. The identifies eight core that integrate rare common alterations across 90 genes stratify palbociclib-sensitive versus palbociclib-resistant cell lines. Predictions translate patient-derived xenografts, whereas single-gene biomarkers do not. Most predictive can be shown by CRISPR–Cas9 genetic disruption regulate CDK4/6i response. Validated relate cell-cycle control, growth factor signaling histone regulatory complex show promotes S-phase entry through activation modifiers KAT6A TBL1XR1 transcription RUNX1. This study enables integrated assessment how tumor’s profile modulates resistance.

Language: Английский

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Pathogenesis and virulence of flavivirus infections

Virulence, Journal Year: 2021, Volume and Issue: 12(1), P. 2814 - 2838

Published: Oct. 26, 2021

The Flavivirus genus consists of >70 members including several that are considered significant human pathogens. Flaviviruses display a broad spectrum diseases can be roughly categorised into two phenotypes - systemic disease involving haemorrhage exemplified by dengue and yellow Fever virus, neurological complications associated with the likes West Nile Zika viruses. Attempts to develop vaccines have been variably successful against some. Besides, mosquito-borne flaviviruses vertically transmitted in arthropods, enabling long term persistence possibility re-emergence. Therefore, developing strategies combat is imperative even if become available. cellular interactions their hosts key establishing viral lifecycle on one hand, activation host immunity other. latter should ideally eradicate infection, but often leads immunopathological consequences. In this review, we use Dengue viruses discuss what learned about molecular determinants accompanying immunopathology, while highlighting current knowledge gaps which need addressed future studies.

Language: Английский

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A Practical Guide to Small Protein Discovery and Characterization Using Mass Spectrometry

Journal of Bacteriology, Journal Year: 2021, Volume and Issue: 204(1)

Published: Nov. 8, 2021

Small proteins of up to ∼50 amino acids play important physiological roles across all domains life. Mass spectrometry is an ideal approach detect and characterize small proteins, but many aspects standard mass workflows are biased against due their size. Here, we highlight applications study emphasizing modifications optimize the detection proteins.

Language: Английский

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Recent Advances in Deep Learning for Protein-Protein Interaction Analysis: A Comprehensive Review

Molecules, Journal Year: 2023, Volume and Issue: 28(13), P. 5169 - 5169

Published: July 2, 2023

Deep learning, a potent branch of artificial intelligence, is steadily leaving its transformative imprint across multiple disciplines. Within computational biology, it expediting progress in the understanding Protein–Protein Interactions (PPIs), key components governing wide array biological functionalities. Hence, an in-depth exploration PPIs crucial for decoding intricate system dynamics and unveiling potential avenues therapeutic interventions. As deployment deep learning techniques PPI analysis proliferates at accelerated pace, there exists immediate demand exhaustive review that encapsulates critically assesses these novel developments. Addressing this requirement, offers detailed literature from 2021 to 2023, highlighting cutting-edge methodologies harnessed analysis. Thus, stands as reference researchers discipline, presenting overview recent studies field. This consolidation helps elucidate dynamic paradigm analysis, evolution techniques, their interdependent dynamics. scrutiny expected serve vital aid researchers, both well-established newcomers, assisting them maneuvering rapidly shifting terrain applications

Language: Английский

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Discovery and significance of protein-protein interactions in health and disease

Cell, Journal Year: 2024, Volume and Issue: 187(23), P. 6501 - 6517

Published: Nov. 1, 2024

The identification of individual protein-protein interactions (PPIs) began more than 40 years ago, using protein affinity chromatography and antibody co-immunoprecipitation. As new technologies emerged, analysis PPIs increased to a genome-wide scale with the introduction intracellular tagging methods, purification (AP) followed by mass spectrometry (MS), co-fractionation MS (CF-MS). Now, combining resulting catalogs complementary including crosslinking (XL-MS) cryogenic electron microscopy (cryo-EM), helps distinguish direct from indirect ones within same or between different complexes. These powerful approaches promise artificial intelligence applications like AlphaFold herald future where complexes, energy-driven machines, will be understood in exquisite detail, unlocking insights contexts both basic biology disease.

Language: Английский

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Mapping protein–protein interactions by mass spectrometry

Mass Spectrometry Reviews, Journal Year: 2024, Volume and Issue: unknown

Published: May 14, 2024

Abstract Protein–protein interactions (PPIs) are essential for numerous biological activities, including signal transduction, transcription control, and metabolism. They play a pivotal role in the organization function of proteome, their perturbation is associated with various diseases, such as cancer, neurodegeneration, infectious diseases. Recent advances mass spectrometry (MS)‐based protein interactomics have significantly expanded our understanding PPIs cells, techniques that continue to improve terms sensitivity, specificity providing new opportunities study diverse systems. These differ depending on type interaction being studied, each approach having its set advantages, disadvantages, applicability. This review highlights recent enrichment methodologies interactomes before MS analysis compares unique features specifications. It emphasizes prospects further improvement potential applications advancing knowledge contexts.

Language: Английский

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Pathogenic mutations of human phosphorylation sites affect protein–protein interactions

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: April 11, 2024

Abstract Despite their lack of a defined 3D structure, intrinsically disordered regions (IDRs) proteins play important biological roles. Many IDRs contain short linear motifs (SLiMs) that mediate protein-protein interactions (PPIs), which can be regulated by post-translational modifications like phosphorylation. 20% pathogenic missense mutations are found in IDRs, and understanding how such affect PPIs is essential for unraveling disease mechanisms. Here, we employ peptide-based interaction proteomics to investigate 36 disease-associated affecting phosphorylation sites. Our results unveil significant differences interactomes between phosphorylated non-phosphorylated peptides, often due disrupted phosphorylation-dependent SLiMs. We focused on mutation serine site the transcription factor GATAD1, causes dilated cardiomyopathy. find this mediates with 14-3-3 family proteins. Follow-up experiments reveal structural basis suggest binding affects GATAD1 nucleocytoplasmic transport masking nuclear localisation signal. demonstrate human sites significantly impact interactions, offering insights into potential molecular mechanisms underlying pathogenesis.

Language: Английский

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