Protein quantification without isotopic labels has been a long-standing interest in the proteomics field. However, accurate and robust proteome-wide quantification with label-free approaches remains a challenge. We developed a new intensity determination and normalization procedure called MaxLFQ that is fully compatible with any peptide or protein separation prior to LC-MS analysis. Protein abundance profiles are assembled using the maximum possible information from MS signals, given that the presence of quantifiable peptides varies from sample to sample. For a benchmark dataset with two proteomes mixed at known ratios, we accurately detected the mixing ratio over the entire protein expression range, with greater precision for abundant proteins. The significance of individual label-free quantifications was obtained via a t test approach. For a second benchmark dataset, we accurately quantify fold changes over several orders of magnitude, a task that is challenging with label-based methods. MaxLFQ is a generic label-free quantification technology that is readily applicable to many biological questions; it is compatible with standard statistical analysis workflows, and it has been validated in many and diverse biological projects. Our algorithms can handle very large experiments of 500+ samples in a manageable computing time. It is implemented in the freely available MaxQuant computational proteomics platform and works completely seamlessly at the click of a button. Protein quantification without isotopic labels has been a long-standing interest in the proteomics field. However, accurate and robust proteome-wide quantification with label-free approaches remains a challenge. We developed a new intensity determination and normalization procedure called MaxLFQ that is fully compatible with any peptide or protein separation prior to LC-MS analysis. Protein abundance profiles are assembled using the maximum possible information from MS signals, given that the presence of quantifiable peptides varies from sample to sample. For a benchmark dataset with two proteomes mixed at known ratios, we accurately detected the mixing ratio over the entire protein expression range, with greater precision for abundant proteins. The significance of individual label-free quantifications was obtained via a t test approach. For a second benchmark dataset, we accurately quantify fold changes over several orders of magnitude, a task that is challenging with label-based methods. MaxLFQ is a generic label-free quantification technology that is readily applicable to many biological questions; it is compatible with standard statistical analysis workflows, and it has been validated in many and diverse biological projects. Our algorithms can handle very large experiments of 500+ samples in a manageable computing time. It is implemented in the freely available MaxQuant computational proteomics platform and works completely seamlessly at the click of a button. Mass-spectrometry-based proteomics has become an increasingly powerful technology not only for the identification of large numbers of proteins, but also for their quantification (1.Aebersold R. Mann M. Mass spectrometry-based proteomics.Nature. 2003; 422: 198-207Crossref PubMed Scopus (5585) Google Scholar, 2.Ong S.E. Mann M. Mass spectrometry-based proteomics turns quantitative.Nat. Chem. Biol. 2005; 1: 252-262Crossref PubMed Scopus (1317) Google Scholar, 3.Bantscheff M. Schirle M. Sweetman G. Rick J. 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However, despite their success, they inherently entail extra preparation steps, whereas label-free quantification is by its nature the simplest and most economical approach. Label-free quantification is in principle applicable to any kind of sample, including materials that cannot be directly metabolically labeled (for instance, many clinical samples). In addition, there is no limit on the number of samples that can be compared, in contrast to the finite number of "plexes" available for label-based methods (7.Dephoure N. Gygi S.P. Hyperplexing: a method for higher-order multiplexed quantitative proteomics provides a map of the dynamic response to rapamycin in yeast.Sci. Signal. 2012; 5: rs2Crossref PubMed Scopus (121) Google Scholar). A vast literature on label-free quantification methods, reviewed in Ref. 3.Bantscheff M. Schirle M. Sweetman G. Rick J. Kuster B. Quantitative mass spectrometry in proteomics: a critical review.Anal. Bioanal. 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ProtQuant: a tool for the label-free quantification of MudPIT proteomics data.BMC Bioinformatics. 2007; 8: S24Crossref PubMed Scopus (46) Google Scholar, 31.Weisser H. Nahnsen S. Grossmann J. Nilse L. Quandt A. Brauer H. Sturm M. Kenar E. Kohlbacher O. Aebersold R. Malmstrom L. An automated pipeline for high-throughput label-free quantitative proteomics.J. Proteome Res. 2013; Crossref PubMed Scopus (125) Google Scholar) already exist. These computational methods include simple additive prescriptions to combine peptide intensities (32.Ning K. Fermin D. Nesvizhskii A.I. Comparative analysis of different label-free mass spectrometry based protein abundance estimates and their correlation with RNA-Seq gene expression data.J. Proteome Res. 2012; 11: 2261-2271Crossref PubMed Scopus (109) Google Scholar, 33.Cheng F.Y. Blackburn K. Lin Y.M. Goshe M.B. Williamson J.D. Absolute protein quantification by LC/MS(E) for global analysis of salicylic acid-induced plant protein secretion responses.J. 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However, major bottlenecks remain: Most methods require measurement of samples under uniform conditions with strict adherence to standard sample-handling procedures, with minimal fractionation and in tight temporal sequence. Also, many methods are tailored toward a specific biological question, such as the detection of protein interactions (37.Choi H. Glatter T. Gstaiger M. Nesvizhskii A.I. SAINT-MS1: protein-protein interaction scoring using label-free intensity data in affinity purification-mass spectrometry experiments.J. Proteome Res. 2012; 11: 2619-2624Crossref PubMed Scopus (46) Google Scholar), and are therefore not suitable as generic tools for quantification at a proteome scale. Finally, the modest accuracy of their quantitative readouts relative to those obtained with stable-isotope-based methods often prohibits their use for biological questions that require the detection of small changes, such as proteome changes upon stimulus. Metabolic labeling methods such as SILAC 1The abbreviations used are: SILAC, stable isotope labeling by amino acids in cell culture; MS, mass spectrometry; LC-MS, liquid chromatography–mass spectrometry; MS/MS, tandem mass spectrometry; XIC, extracted ion current; LFQ, label-free quantification; UPS, universal protein standard; FDR, false discovery rate. 1The abbreviations used are: SILAC, stable isotope labeling by amino acids in cell culture; MS, mass spectrometry; LC-MS, liquid chromatography–mass spectrometry; MS/MS, tandem mass spectrometry; XIC, extracted ion current; LFQ, label-free quantification; UPS, universal protein standard; FDR, false discovery rate. (38.Ong S.E. Blagoev B. Kratchmarova I. Kristensen D.B. Steen H. Pandey A. Mann M. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics.Mol. Cell. 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High mass resolution and accuracy and high peptide identification rates have been key ingredients in the success of isotope-label-based methods. These factors contribute similarly to the quality of label-free quantification. An increased identification rate directly improves label-free quantification because it increases the number of data points and allows "pairing" of corresponding peptides across runs. Although high mass accuracy aids in the identification of peptides (42.Cox J. Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.Nat. Biotechnol. 2008; 26: 1367-1372Crossref PubMed Scopus (9150) Google Scholar), it is the high mass resolution that is crucial to accurate quantification. This is because the accurate determination of extracted ion currents (XICs) of peptides is critical for comparison between samples (43.Andersen J.S. Wilkinson C.J. Mayor T. Mortensen P. Nigg E.A. Mann M. Proteomic characterization of the human centrosome by protein correlation profiling.Nature. 2003; 426: 570-574Crossref PubMed Scopus (1051) Google Scholar). At low mass resolution, XICs of peptides are often contaminated by nearby peptide signals, preventing accurate intensity readouts. In the past, this has led many researchers to use counts of identified MS/MS spectra as a proxy for the ion intensity or protein abundance (44.Ishihama Y. Oda Y. Tabata T. Sato T. Nagasu T. Rappsilber J. Mann M. Exponentially modified protein abundance index (emPAI) for estimation of absolute protein amount in proteomics by the number of sequenced peptides per protein.Mol. Cell. Proteomics. 2005; 4: 1265-1272Abstract Full Text Full Text PDF PubMed Scopus (1635) Google Scholar). Although the abundance of proteins and the probability of their peptides being selected for MS/MS sequencing are correlated to some extent, XIC-based methods should clearly be superior to spectral counting given sufficient resolution and optimal algorithms. These advantages are most prominent for low-intensity protein/peptide species, for which a continuous intensity readout is more information-rich than discrete counts of spectra. Therefore, we here apply the term "label-free quantification" strictly to XIC-based approaches and not to spectral counting. In this manuscript, we describe the MaxLFQ algorithms, part of the MaxQuant software suite, that solve two of the main problems of label-free protein quantification. We introduce "delayed normalization," which makes label-free quantification fully compatible with any up-front separation. Furthermore, we implemented a novel approach to protein quantification that extracts the maximum ratio information from peptide signals in arbitrary numbers of samples to achieve the highest possible accuracy of quantification. MaxLFQ is a generic method for label-free quantification that can be combined with standard statistical tests of quantification accuracy for each of thousands of quantified proteins. MaxLFQ has been available as part of the MaxQuant software suite for some time and has already been successfully applied to a variety of biological questions by us and other groups. It has delivered excellent performance in benchmark comparisons with other software solutions (31.Weisser H. Nahnsen S. Grossmann J. Nilse L. Quandt A. Brauer H. Sturm M. Kenar E. Kohlbacher O. Aebersold R. Malmstrom L. An automated pipeline for high-throughput label-free quantitative proteomics.J. Proteome Res. 2013; Crossref PubMed Scopus (125) Google Scholar). An Escherichia coli K12 strain was grown in standard LB medium, harvested, washed in PBS, and lysed in BugBuster (Novagen Merck Chemicals, Schwalbach, Germany) according to the manufacturer's protocol. HeLa S3 cells were grown in standard RPMI 1640 medium supplemented with glutamine, antibiotics, and 10% FBS. After being washed with PBS, cells were lysed in cold modified RIPA buffer (50 mm Tris-HCl, pH 7.5, 1 mm EDTA, 150 mm NaCl, 1% N-octylglycoside, 0.1% sodium deoxycholate, complete protease inhibitor mixture (Roche)) and incubated for 15 min on ice. Lysates were cleared by centrifugation, and after precipitation with chloroform/methanol, proteins were resuspended in 6 m urea, 2 m thiourea, 10 mm HEPES, pH 8.0. Prior to in-solution digestion, 60-μg protein samples from HeLa S3 lysates were spiked with either 10 μg or 30 μg of E. coli K12 lysates based on protein amount (Bradford assay). Both batches were reduced with dithiothreitol and alkylated with iodoacetamide. Proteins were digested with LysC (Wako Chemicals, GmbH, Neuss, Germany) for 4 h and then trypsin digested overnight (Promega, GmbH, Mannheim, Germany). Digestion was stopped by the addition of 2% trifluroacetic acid. Peptides were separated by isoelectric focusing into 24 fractions on a 3100 OFFGEL Fractionator (Agilent, GmbH, Böblingen, Germany) as described in Ref. 45.Hubner N.C. Ren S. Mann M. Peptide separation with immobilized pI strips is an attractive alternative to in-gel protein digestion for proteome analysis.Proteomics. 2008; (Dec. 8): 4862-4872Crossref PubMed Scopus (147) Google Scholar. Each fraction was purified with C18 StageTips (46.Rappsilber J. Ishihama Y. Mann M. Stop and go extraction tips for matrix-assisted laser desorption/ionization, nanoelectrospray, and LC/MS sample pretreatment in proteomics.Anal. Chem. 2003; 75: 663-670Crossref PubMed Scopus (1795) Google Scholar) and analyzed via liquid chromatography combined with electrospray tandem mass spectrometry on an LTQ Orbitrap (Thermo Fisher) with lock mass calibration (47.Olsen J.V. de Godoy L.M. Li G. Macek B. Mortensen P. Pesch R. Makarov A. Lange O. Horning S. Mann M. Parts per million mass accuracy on an Orbitrap mass spectrometer via lock mass injection into a C-trap.Mol. Cell. Proteomics. 2005; 4: 2010-2021Abstract Full Text Full Text PDF PubMed Scopus (1241) Google Scholar). All raw files were searched against the human and E. coli complete proteome sequences obtained from UniProt (version from January 2013) and a set of commonly observed contaminants. MS/MS spectra were filtered to contain at most eight peaks per 100 mass unit intervals. The initial MS mass tolerance was 20 ppm, and MS/MS fragment ions could deviate by up to 0.5 Da (48.Cox J. Hubner N.C. Mann M. How much peptide sequence information is contained in ion trap tandem mass spectra?.J. Am. Soc. Mass Spectrom. 2008; 19: 1813-1820Crossref PubMed Scopus (25) Google Scholar). For quantification, intensities can be determined alternatively as the full peak volume or as the intensity maximum over the retention time profile, and the latter method was used here as the default. Intensities of different isotopic peaks in an isotope pattern are always summed up for further analysis. MaxQuant offers a choice of the degree of uniqueness required in order for peptides to be included for quantification: "all peptides," "only unique peptides," and "unique plus razor peptides" (42.Cox J. Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.Nat. Biotechnol. 2008; 26: 1367-1372Crossref PubMed Scopus (9150) Google Scholar). Here we chose the latter, because it is a good compromise between the two competing interests of using only peptides that undoubtedly belong to a protein and using as many peptide signals as possible. The distribution of peptide ions over fractions and samples is shown in supplemental Fig. S1. The E. coli K12 strain was grown in standard LB medium, harvested, washed in PBS, and lysed in 4% SDS, 100 mm Tris, pH 8.5. Lysates were briefly boiled and DNA sheared using a Sonifier (Branson Model 250). Lysates were cleared by centrifugation at 15,000 × g for 15 min and precipitated with acetone. Proteins were resuspended in 8 m urea, 25 mm Tris, pH 8.5, 10 mm DTT. After 30 min of incubation, 20 mm iodoacetamide was added for alkylation. The sample was then diluted 1:3 with 50 mm ammonium bicarbonate buffer, and the protein concentration was estimated via tryptophan fluorescence emission assay. After 5 h of digestion with LysC (Wako Chemicals) at room temperature, the sample was further diluted 1:3 with ammonium bicarbonate buffer, and trypsin (Promega) digestion was performed overnight (protein-to-enzyme ratio of 60:1 in each case). E. coli peptides were then purified by using a C18 Sep Pak cartridge (Waters, Milford, MA) according to the manufacturer's instructions. UPS1 and UPS2 standards (Sigma-Aldrich) were resuspended in 30 μl of 8 m urea, 25 mm Tris, pH 8.5, 10 mm DTT and reduced, alkylated, and digested in an analogous manner, but with a lower protein-to-enzyme ratio (12:1 for UPS1 and 10:1 for UPS2, both LysC and trypsin). UPS peptides were then purified using C18 StageTips. E. coli and UPS peptides were quantified based on absorbance at 280 nm using a NanoDrop spectrophotometer (Fisher Scientific). For each run, 2 μg of E. coli peptides were then spiked with 0.15 μg of either UPS1 or UPS2 peptides, and about 1.6 μg of the mix was then analyzed via liquid chromatography combined with mass spectrometry on a Q Exactive (Thermo Fisher). Data were analyzed with MaxQuant as described above for the proteome dataset. All files were searched against the E. coli complete proteome sequences plus those of the UPS proteins and common contaminants. To increase the number of peptides that can be used for quantification beyond those that have been sequenced and identified by an MS/MS database search engine, one can transfer peptide identifications to unsequenced or unidentified peptides by matching their mass and retention times ("match-between-runs" feature in MaxQuant). A prerequisite for this is that retention times between different LC-MS runs be made comparable via alignment. The order in which LC-MS runs are aligned is determined by hierarchical clustering, which allows one to avoid reliance on a single master run. The terminal branches of the tree from the hierarchical clustering typically connect LC-MS runs of the same or neighboring fractions or replicate runs, as they are the most similar. These cases are aligned first. Moving along the tree structure, increasingly dissimilar runs are integrated. The calibration functions that are needed to completely align LC-MS runs are usually time-dependent in a nonlinear way. Every pair-wise alignment step is performed via two-dimensional Gaussian kernel smoothing of the mass matches between the two runs. Following the ridge of the highest density region determines the recalibration function. At each tree node the resulting recalibration function is applied to one of the two subtrees, and the other is left unaltered. Unidentified LC-MS features are then assigned to peptide identifications in other runs that match based on their accurate masses and aligned retention times. In complex proteomes, the high mass accuracy on current Orbitrap instruments is still insufficient for an unequivocal peptide identification based on the peptide mass alone. However, when comparing peptides in similar LC-MS runs, the information contained in peptide mass and recalibrated retention time is enough to transfer identifications with a sufficiently low FDR (in the range of 1%), which one can estimate by comparing the density of matches inside the match time window to the density outside this window (49.Geiger T. Wehner A. Schaab C. Cox J. Mann M. Comparative proteomic analysis of eleven common cell lines reveals ubiquitous but varying expression of most proteins.Mol. Cell. Proteomics. 2012; 11Abstract Full Text Full Text PDF Scopus (577) Google Scholar). The matching procedure takes into account the up-front separation, in this case isoelectric focusing of peptides into 24 fractions. Identifications are only transferred into adjacent fractions. If, for instance, for a given peptide sequenced in fraction 7, isotope patterns are found to match by mass and retention time in fractions 6, 8, and 17, the matches in fraction 17 are discarded because they have a much greater probability of being false. The same strategy can be applied to any other up-front peptide or protein separation (e.g. one-dimensional gel electrophoresis). All matches with retention time differences of less than 0.5 min after recalibration are accepted. Further details on the alignment and matching algorithms, including how to control the FDR of matching, will be described in a future manuscript. The label-free software MaxLFQ is completely integrated into the MaxQuant software (42.Cox J. Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.Nat. Biotechnol. 2008; 26: 1367-1372Crossref PubMed Scopus (9150) Google Scholar) and can be activated by one additional click. It is freely available to academic and commercial users as part of MaxQuant and can be downloaded via the Internet. MaxQuant runs on Windows desktop computers with Vista or newer operating systems, preferably the 64-bit versions. There is a lar
DNA interstrand cross-links (ICLs) block replication fork progression by inhibiting DNA strand separation. Repair of ICLs requires sequential incisions, translesion DNA synthesis, and homologous recombination, but the full set of factors involved in these transactions remains unknown. We devised a technique called chromatin mass spectrometry (CHROMASS) to study protein recruitment dynamics during perturbed DNA replication in Xenopus egg extracts. Using CHROMASS, we systematically monitored protein assembly and disassembly on ICL-containing chromatin. Among numerous prospective DNA repair factors, we identified SLF1 and SLF2, which form a complex with RAD18 and together define a pathway that suppresses genome instability by recruiting the SMC5/6 cohesion complex to DNA lesions. Our study provides a global analysis of an entire DNA repair pathway and reveals the mechanism of SMC5/6 relocalization to damaged DNA in vertebrate cells.
Abstract The number of publications in the field of chemical cross-linking combined with mass spectrometry (XL-MS) to derive constraints for protein three-dimensional structure modeling and to probe protein-protein interactions has largely increased during the last years. As the technique is now becoming routine for in vitro and in vivo applications in proteomics and structural biology there is a pressing need to define protocols as well as data analysis and reporting formats that are generally accepted in the field and that have shown to lead to high-quality results. This first, community-based harmonization study on XL-MS is based on the results of 32 groups participating worldwide. The aim of this paper is to summarize the status quo of XL-MS and to compare and evaluate existing cross-linking strategies. From the results obtained, common protocols will be established. Our study serves as basis for establishing best practice guidelines in the field for conducting cross-linking experiments, performing data analysis, and reporting formats with the ultimate goal of assisting scientists to generate accurate and reproducible XL-MS results.
Reduced activity of the insulin/IGF signalling network increases health during ageing in multiple species. Diverse and tissue-specific mechanisms drive the health improvement. Here, we performed tissue-specific transcriptional and proteomic profiling of long-lived Drosophila dilp2-3,5 mutants, and identified tissue-specific regulation of >3600 transcripts and >3700 proteins. Most expression changes were regulated post-transcriptionally in the fat body, and only in mutants infected with the endosymbiotic bacteria, Wolbachia pipientis , which increases their lifespan. Bioinformatic analysis identified reduced co-translational ER targeting of secreted and membrane-associated proteins and increased DNA damage/repair response proteins. Accordingly, age-related DNA damage and genome instability were lower in fat body of the mutant, and overexpression of a minichromosome maintenance protein subunit extended lifespan. Proteins involved in carbohydrate metabolism showed altered expression in the mutant intestine, and gut-specific overexpression of a lysosomal mannosidase increased autophagy, gut homeostasis, and lifespan. These processes are candidates for combatting ageing-related decline in other organisms.
Article Figures and data Abstract Introduction Results Discussion Materials and methods Appendix 1 Appendix 2 Appendix 3 Data availability References Decision letter Author response Article and author information Metrics Abstract Reduced activity of the insulin/IGF signalling network increases health during ageing in multiple species. Diverse and tissue-specific mechanisms drive the health improvement. Here, we performed tissue-specific transcriptional and proteomic profiling of long-lived Drosophila dilp2-3,5 mutants, and identified tissue-specific regulation of >3600 transcripts and >3700 proteins. Most expression changes were regulated post-transcriptionally in the fat body, and only in mutants infected with the endosymbiotic bacteria, Wolbachia pipientis, which increases their lifespan. Bioinformatic analysis identified reduced co-translational ER targeting of secreted and membrane-associated proteins and increased DNA damage/repair response proteins. Accordingly, age-related DNA damage and genome instability were lower in fat body of the mutant, and overexpression of a minichromosome maintenance protein subunit extended lifespan. Proteins involved in carbohydrate metabolism showed altered expression in the mutant intestine, and gut-specific overexpression of a lysosomal mannosidase increased autophagy, gut homeostasis, and lifespan. These processes are candidates for combatting ageing-related decline in other organisms. Introduction Human life expectancy is increasing (Oeppen and Demography, 2002) and is predicted to continue to do so (Kontis et al., 2017). However, healthspan, the period of life spent in good health and free from the chronic diseases and disorders of ageing, is not keeping up with lifespan and there is therefore a growing period of functional decline and ill health at the end of life (Crimmins, 2015; Niccoli and Partridge, 2012; Partridge et al., 2018). Lowered activity of the insulin and IGF-1-like signalling (IIS) network can extend lifespan in laboratory model organisms (Kenyon, 2011; Partridge et al., 2011), and possibly humans through specific mutations (Flachsbart et al., 2009; Study of Osteoporotic Fractures et al., 2009), and can reduce the incidence of age-related impairments and diseases (Mannick et al., 2014; Mannick et al., 2018). Identifying the molecular mechanisms and understanding exactly how reducing IIS activity prolongs longevity may hence lead to interventions that ameliorate the effects of ageing and prevent age-related pathology. Gene expression profiling in whole organisms has identified genes and molecular mechanisms that ameliorate ageing in IIS mutants in C. elegans (Ewald et al., 2015; Halaschek-Wiener et al., 2005; Kaletsky et al., 2016; McElwee et al., 2007; Murphy et al., 2003; Oh et al., 2006) and Drosophila (Alic et al., 2011; Teleman et al., 2008). Recent transcriptomic analysis in mice (Page et al., 2018), and proteomic analysis in Drosophila (Tain et al., 2017) showed that the responses to lowered IIS are highly tissues-specific. How these tissue-specific responses are regulated is less clear, and tissue-specific profiling of both the transcriptome and the proteome can give a more informative picture, not only of the molecular changes mediating tissue-specific functional responses to mutations that increase healthspan, but also of how gene expression itself is regulated to achieve those responses (Barrett et al., 2012). IIS affects not only lifespan, but also other processes including development, growth, and reproduction (Bartke, 2011). Isolating the potentially causal changes in gene expression that specifically modulate longevity in IIS mutants is therefore challenging. In the fruit fly Drosophila, IIS is activated through insulin-like peptides (DILPs) (Grönke et al., 2010). Genetic ablation of the median secretory neurons (mNSC), which produce DILPs, or null mutation of 3 dilp genes (dilp2-3,5) that are expressed in the mNSC neurons, systemically lowers IIS, resulting in extended lifespan, reduced body size, reduced female fertility, and delayed development (Grönke et al., 2010; Broughton et al., 2005). However, the extent to which these traits change is greater in dilp2-3,5 mutants than mNSC-ablated flies, perhaps because IIS activity is reduced throughout development in dilp2-3,5 mutants, while lowered IIS commences only later in larval life in mNSC-ablated flies (Grönke et al., 2010; Broughton et al., 2005). A naturally occurring endosymbiotic bacterium, Wolbachia pipientis, present in many insect species (Werren and Windsor, 2000), interacts with IIS (Ikeya et al., 2009), and increases the longevity of dilp2-3,5 mutants (Grönke et al., 2010). Wolbachia also increases the resistance of dilp2-3,5 mutants to xenobiotics, but does not affect other phenotypes associated with reduced IIS (Grönke et al., 2010). Changes in gene expression that require Wolbachia in IIS mutants are therefore potentially causal for longevity, and identifying them could thus aid in isolating the specific mechanisms and processes that mediate IIS mutant longevity. Here, we have simultaneously profiled tissue-specific changes in gene expression at both the transcriptomic and proteomic level in the gut, brain, thorax, and fat body of dilp2-3,5 mutant flies. Combining our proteomic analysis with transcriptome profiling, we have examined the role of transcriptional and proteomic responses in remodelling of the tissue-specific proteomes in response to lowered IIS. To pinpoint whether these changes were causal for longevity, we quantified whether these changes in expression were altered in the presence of Wolbachia. Surprisingly, we found that, unlike in the gut, brain, and thorax, the majority of fat-body-specific gene expression changes in response to reduced IIS were regulated post-transcriptionally, and that this regulation was entirely dependent upon the presence of Wolbachia. To increase the specificity and robustness of our analysis, we performed a novel meta-analysis of the proteomic responses to those in a previously studied mutant, mNSC-ablated flies (Tain et al., 2017). Importantly, our tissue-specific gene expression analysis and cross model meta-analysis allowed the identification of both conserved, and model-specific, responses to reduced IIS, which may contribute to IIS-mediated longevity. We identified novel functional signatures of reduced endoplasmic reticulum (ER)-protein targeting and an increased DNA damage/repair response that require Wolbachia and that were specific to the fat body of dilp2-3,5 mutants. We then showed that DNA damage is reduced, and genome stability increased, in the fat body of dilp2-3,5 mutant flies and, importantly, that these effects require Wolbachia. Furthermore, we showed that increased expression of one subunit of the minichromosome maintenance complex was sufficient to reduce DNA damage in the fat body, and extend lifespan. Finally, we identified a gut-specific upregulation of lysosomal alpha-mannosidases in response to lowered IIS that occurred only in the presence of Wolbachia. Furthermore, we showed that gut-specific overexpression of one lysosomal alpha-mannosidase was sufficient to maintain gut homeostasis and extend lifespan. Results Tissue-specific remodelling of gene expression in response to reduced IIS Reducing IIS activity can remodel gene expression via downstream transcription factors (Partridge et al., 2011; Kenyon, 2010; Fontana et al., 2010). To determine the effect of reduced IIS on tissue-specific gene expression, we compared transcript and protein expression levels in dilp2-3,5 mutants to those of wild-type controls (wDah). We reproducibly identified a total of 11331 transcripts and 7234 proteins (Appendix 1A-C), of which 3683 transcripts and 3738 proteins showed significantly altered expression in at least one tissue of dilp2-3,5 mutant flies (adj. p-value<=0.1) (Appendix 1D, Supplementary file 1–2). In total, the gut, fat body, brain, and thorax of dilp2-3,5 mutant flies showed 563, 1004, 365, and 2535 differentially expressed transcripts, respectively (Appendix 1D). In contrast, we detected a total of 1824, 1678, and 1473 differentially expressed proteins in the gut, fat body, and brain of dilp2-3,5 mutant flies, respectively, but only 339 were changed in the thorax (Appendix 1D). Overall both the proteomic and transcriptomic responses to reduced IIS were highly tissue-specific, only 22 proteins and 17 transcripts showed altered expression in all four tissues (Appendix 1D). Reduced IIS post-transcriptionally remodels the fat-body-specific proteome Under steady state conditions, protein abundance is primarily determined by mRNA abundance (Liu et al., 2016). To determine if the correlation between mRNA and protein abundance was perturbed in response to lowered IIS activity, we compared tissue-specific transcripts and corresponding proteins in dilp2-3,5 mutants. On average across the four tissues, two thirds of the significant tissue-specific changes in transcript levels in dilp2-3,5 mutants were mirrored by changes in expression of the encoded proteins (Figure 1A, Supplementary file 3). One third, however, were regulated in opposite directions, possibly through post-transcriptional regulation (Figure 1A, Supplementary file 3). Figure 1 Download asset Open asset Reducing IIS modulates both the tissue-specific transcriptomic and proteomic landscapes. Plots show the proportion of protein/transcript pairs that are regulated in the same (both up [orange] or both down [blue]) or opposite (grey) directions in response to reduced IIS (dilp2-3,5 vs. wDah). Correlations were calculated between the protein and transcript log-fold changes of significantly regulated protein/transcript pairs in each plot. (A) All protein/transcript pairs in the respective tissue where the transcript is significantly regulated (adj. p-value≤0.1) in response to reduced IIS, irrespective of if the associated protein is significantly regulated (Supplementary file 3–4). (B) All protein/transcript pairs in the respective tissue where the protein is significantly regulated (adj. p-value≤0.1) in response to reduced IIS, irrespective of if the associated transcript was significantly regulated (Supplementary file 3–4). Correlations (cor.), number of protein/transcript pairs (n) shown above each plot. Rounded percentages of protein/transcript pairs within a specific quadrant of the plots are shown within the respective quadrants (may not total 100%). To more precisely examine the effect of post-transcriptional regulation on the proteome in dilp2-3,5 mutants, we compared the expression of proteins whose level was significantly regulated in response to reduced IIS, to their associated transcripts. This comparison was made irrespective of whether those transcripts were significantly regulated (Figure 1B, Supplementary file 4). Our analysis revealed that gene expression changes in response to reduced IIS changes in the gut, brain, and thorax were mainly driven by changes in transcription (Figure 1B). However, many gene expression changes in response to lowered IIS in the fat body were post-transcriptional (Figure 1B). Over 50% of the proteins changed in the fat body of dilp2-3,5 mutants were oppositely regulated from their associated transcripts (Figure 1B). In total, 1569 proteins were differentially expressed in the fat body of dilp2-3,5 mutant flies (Figure 1B). Of those, 729 proteins changed expression in the same direction as their transcripts and were enriched for functions associated with proteostasis, amino acid metabolism, and mitochondria (Figure 1B shown in blue and orange, Supplementary file 5). The remaining 840 proteins were regulated in the opposite direction to that of their transcripts. This included 601 significantly downregulated proteins, which were enriched for functions relating to translation/peptide generation, endoplasmic reticulum (ER), and lipids. The remaining 239 proteins were significantly upregulated and enriched for functions relating to DNA replication/repair, and chromatin remodelling (Figure 1B, Supplementary file 5). Thus, gene expression changes in response to lowered IIS in the brain, gut, and thorax, were driven by transcription; however, in the fat body those changes were primarily post-transcriptional. Together, these findings highlight the importance of quantifying post-transcriptional gene expression when analysing IIS mutants, as many gene expression changes would have been missed if examined solely at the mRNA level. Identifying tissue-specific, differential gene expression potentially causal in longevity To further narrow down changes in gene expression that may have a causal role in IIS mutant longevity, we examined both transcriptomic and proteomic changes in the presence and absence of the intracellular, bacterial symbiont Wolbachia pipientis. The presence of Wolbachia is required for lifespan extension of dilp2-3,5 mutants, but not for other IIS-related phenotypes such as growth and fecundity (Grönke et al., 2010). We identified both transcripts and proteins whose expression changed in the dilp2-3,5 mutant, but only in the Wolbachia-positive background (Appendix 2A), and found both Wolbachia-independent and Wolbachia-dependent changes (Appendix 2B, Supplementary file 1–2). The majority of Wolbachia-dependent changes were differentially regulated in the fat body (235 proteins and 249 transcripts) of dilp2-3,5 mutants, with relatively few changes occurring in the gut, brain, and thorax (Appendix 2B). Of those genes whose Wolbachia-dependent expression was altered in the fat body, only 26 were regulated, and regulated in the same direction, on both the transcript and protein level, suggesting considerable post-transcriptional regulation in response to reduced IIS, specifically in this tissue (Figure 1B). Surprisingly, we found the previously described post-transcriptional pattern of oppositely regulated proteins and transcripts (Figure 1B) was dependent on Wolbachia in the fat body of dilp2-3,5 mutants (Appendix 2D-E). To identify functional signatures associated to Wolbachia-dependent gene expression changes in response to reduced IIS, we performed GO enrichment analysis. Transcripts whose regulation was Wolbachia-dependent in fat body were enriched for glucosidase and peptidase enzyme families (Appendix 2C, Supplementary file 6). Proteins whose regulation was Wolbachia-dependent were enriched for proteins associated with DNA replication and damage/repair responses in fat body, and mannose metabolism in the gut (Appendix 2C, Supplementary file 6). Thus, examining tissue-specific, Wolbachia-dependent, changes in gene expression in response to reduced IIS has identified DNA damage/repair responses in fat body and mannose metabolism in the gut as possible regulators of longevity in dilp2-3,5 mutants. Furthermore, the discrepancy between regulation at the transcript and protein in the fat body suggest increased levels of post-transcriptional regulation in response to reduced IIS. Proteomic responses to reduced IIS in two independent Drosophila models Several genetic interventions in Drosophila that reduce IIS result in increased lifespan. To identify robust and conserved changes in protein expression in response to reduced IIS, we examined the overlap in differentially expressed proteins between dilp2-3,5 mutants and the previously published tissue-specific proteomes of mNSC-ablated flies (Tain et al., 2017). There was a significant correlation between the tissue-specific changes in each IIS mutant (Appendix 3A-B). However, we also detected 1810 differentially regulated proteins whose expression was not changed in mNSC-ablated flies (Appendix 3B) (Tain et al., 2017). Thus, mutant-specific changes occurred, but a significant proportion of the differential expression was also conserved between the two mutants. Both to increase the power of our analysis and detect shared functional signatures between the two IIS mutants, we performed an undirected network propagation analysis (Vanunu et al., 2010), which incorporates protein-protein interaction information. We clustered the resulting network propagation scores (Supplementary file 7) that were either shared or detected in only one mutant, and identified functional categories within the clusters with GO enrichment analysis (Figure 2, Supplementary file 8). Figure 2 Download asset Open asset Hierarchical clustering and GO enrichment analysis of significantly regulated proteins in two independent models of reduced IIS activity. Tissue-specific heatmap of network propagation scores (Supplementary file 7) based on the comparisons of tissue-specific, long-lived IIS mutant proteomes vs. wild-type controls in two independent model systems of reduced IIS: dilp2-3,5 vs. wDah (dilp2-3,5) and InsP3‐Gal4/UAS‐rpr vs. wDah (mNSC-abl.). For every protein and tissue, the minimum of both scores in that tissue was calculated to show conserved changes between the models (Integrated). Clusters denoted by colour and for each cluster. GO enrichment analysis and selected terms shown in grey boxes (see Supplementary file 8 for all significant terms). Proteins whose tissue-specific expression pattern was shared between both dilp2-3,5 mutants and mNSC-ablated flies represent robust and conserved proteomic changes in response to lowered IIS. Those proteins were associated with translation and ribosomal biogenesis, membrane fusion, mitochondrial electron transport, proteostasis, proteasome assembly, and ER protein targeting (clusters 1–8, Figure 2). Some of these processes have been directly or indirectly associated with the longevity of IIS mutants (Tain et al., 2017; Essers et al., 2016; Augustin et al., 2017). However, the link between lifespan and fat body-specific ER protein targeting (cluster 8, Figure 2) in response to reduced IIS has so far not been explored. Proteins whose tissue-specific expression pattern was detected in the dilp2-3,5 mutants, but not mNSC-ablated flies, represent possible mutant-specific proteomic changes in response to lowered IIS. Importantly, as dilp2-3,5 mutant flies are considerably longer lived than mNSC-ablated flies (Grönke et al., 2010; Broughton et al., 2005) any dilp2-3,5-specific proteomic changes may provide insight into additional mechanisms regulating longevity. DNA damage and repair response was one such functional signature present only in fat body only of dilp2-3,5 mutants, and may thus represent such an additional pro-longevity response to reduced IIS (Cluster 9, Figure 2). Combining our tissue-specific transcriptomic, proteomic, Wolbachia-dependent regulation, and cross model proteomic analyses, of gene expression remodelling in response to reduced IIS led us to examine three main findings that were functional candidates for relevance for longevity. Firstly, we investigated the targeting and translation of proteins to the ER in fat body in more detail. These functional signatures were enriched in the fat body of dilp2-3,5 mutants and linked to proteins whose levels decreased in response to reduced IIS despite increased expression of the associated transcripts (Figure 1B, Supplementary file 5). Furthermore, translational and ER-targeting functional signatures were conserved between dilp2-3,5 mutants and mNSC-ablated flies (Figure 2, clusters 7 and 8). Second, we analysed the importance of fat body-specific DNA damage and repair responses, whose functional signatures were identified as both Wolbachia-dependent changes in response to reduced IIS and only present in dilp2-3,5 mutants. Finally, we analysed gut-specific mannose metabolism, which we identified as both gut-specific Wolbachia-dependent changes in response to reduced IIS, and conserved between two models of reduced IIS. Lowered IIS reduces expression of ER-specific co-translational targeting machinery in the fat body The correct transport and trafficking of newly formed polypeptides within a cell is essential for the creation and maintenance of the distinct subcellular environments required for cellular function. Our analysis identified a fat-body-specific enrichment for proteins associated to the ER and involved in targeting proteins to the ER in dilp2-3,5 mutants (Figure 2, Supplementary file 8). To determine if the response to reduced IIS was both tissue- and ER-specific, we calculated average log-fold changes of proteins associated with several cellular compartment terms (Figure 3A). ER and Golgi associated proteins were consistently downregulated in the fat body of both mNSC-ablated and dilp2-3,5 mutant flies (Figure 3A). Importantly, this regulation did not appear in other tissues or in the absence of Wolbachia, suggesting that reduced ER-targeting of proteins is specific to the fat body and may be causal for the longevity of IIS mutants (Figure 3A). Secreted proteins (extracellular space) and intrinsic membrane components, which are processed in the ER, were also downregulated in the fat body in both models of reduced IIS, and only in the presence of Wolbachia (Figure 3A). Figure 3 Download asset Open asset Tissue-specific regulation of ER-associated cellular compartments and the ER co-translational targeting machinery in two independent models of reduced IIS. (A) Heatmap of mean log-fold changes in proteins annotated with selected GO cellular compartment terms, in the contrasts dilp2-3,5 vs. wDah (dilp2-3,5), dilp2-3,5T vs. wDahT (dilp2-3,5T (T denotes Wolbachia minus genotypes, see Methods section) ), and InsP3‐Gal4/UAS‐rpr vs. wDah (mNSC-abl.) flies (Tain et al., 2017). Significance of difference vs. zero was calculated using a two-sided Student's t-test (*p<0.05,**p<0.01,***p≤0.001). (B) Changes in protein expression of SRP, SRP-receptor (SRP-R) sub-units, TRAM and translocon components. Asterisks indicate Benjamini-Hochberg-corrected significance of the limma moderated t-test (*p≤0.1, **p≤0.01, ***p≤0.001). One key mechanism for delivering proteins to the ER is co-translational targeting, the process of importing newly synthesised proteins directly into the ER (Cross et al., 2009). Nascent polypeptides with signal peptides are recognized by the signal recognition particle (SRP) in the cytosol. SRP-bound nascent peptides are then transported to the SRP-receptor on the ER membrane and passed into the ER lumen through the ER translocon channel, with the aid of translocating proteins (Saraogi and Shan, 2011). There, translation is resumed by co-translational targeting through ER-bound ribosomes (Saraogi and Shan, 2011). Regulation of the SRP, SRP-receptor, and translocon complex thus determines ER import and co-translational targeting capacity. Tellingly, several components of the ER import and co-translational targeting machinery were down-regulated in the fat bodies of dilp2-3,5 mutants, and showed a similar trend in mNSC-ablated flies (Figure 3B). This included four of the seven SRP subunits (Srp68, Srp54k, Srp14, and Srp72), both SRP-receptor subunits (Gtp-bp and SRPRβ), two of the three translocon core subunits (Sec61gamma and Sec61alpha), and the translocating chain-associating membrane protein (TRAM) (Figure 3B). However, although the down-regulation of TRAM and SRP subunits was mostly Wolbachia-specific, regulation of the SRP-receptor subunits did not depend on Wolbachia (Figure 3B). Together, these results suggest that reduced IIS regulates the ER co-translational targeting and protein import machinery, specifically in the fat body of long-lived IIS mutant flies. Furthermore, since much of this regulation was dependent on Wolbachia, it may be important for the longevity of IIS mutants. DNA damage response and genome stability is increased in the fat body of long-lived IIS mutant flies A prevalent theory of ageing is that accumulation of molecular damage, including damage to DNA, progressively diminishes cellular function over time and leads to the functional deterioration associated with advancing age (Maynard et al., 2015). Several DNA damage and DNA repair pathways exist to prevent and counteract this damage, maintain genomic stability, and in turn maintain cellular and organismal functionality. Our bioinformatic analysis identified a post-transcriptionally increased abundance of proteins associated with DNA repair and DNA damage responses, in the fat body of dilp2-3,5 mutants but not in mNSC-ablated flies (Figure 2, Supplementary file 8). Surprisingly, very few of these proteins were detected as significantly regulated in the fat body of mNSC-ablated flies. This suggests that the increased quality and greater depth of proteomic coverage may have uncovered previously undetected, tissue-specific regulation of these proteins in response to reduced IIS. Most (78%) of the 50 regulated proteins within the GO terms DNA replication and DNA damage/repair responses were significantly and co-ordinately up-regulated in the fat body of long lived dilp2-3,5 mutants (Figure 4A, Supplementary file 9). Furthermore, the regulation of these proteins in dilp2-3,5 mutants required the presence of Wolbachia (Figure 4A). Thus, DNA damage/repair responses and genome stability may be increased specifically in the fat body of dilp2-3,5 mutants, and that increase may be associated with longevity. Figure 4 with 1 supplement see all Download asset Open asset Regulation of DNA damage responses and genome stability in response to reduced IIS. (A) log2-fold change of DNA replication/DNA damage response proteins, dilp2-3,5 vs. wDah (Supplementary file 9). Significantly regulated proteins (black dots), Wolbachia-dependent (red dots regulation (adj. p-value≤0.1)), and unregulated (grey dots). (B) Relative p-His2Av foci per fat body nuclei in aged (60d) of dilp2-3,5 mutants compared to controls (wDah) in the presence (dilp2-3,5) and absence (wDahT and dilp2-3,5T) of Wolbachia (averaged foci/nucleus from independent samples n > 14, scale bar shows 5 μM). (C) Regulation MCM complex proteins, dilp2-3,5 vs. wDah, in the presence and absence (dilp2-3,5T) of Wolbachia. Significance of difference vs. zero was calculated using a two-sided Student's t-test (*p<0.05, **p<0.01, ***p≤0.001) exact p values shown in Supplementary file 2. (D) Relative p-His2Av foci per fat body nuclei in aged (60d) of flies overexpressing MCM6 specifically in the fat body (FB-Gal4>UAS-MCM6, n = 15) compared to genetic controls (FB-Gal4/+, n = 16 and UAS-MCM6/+, n = 13). Averaged foci/nucleus from independent samples. (E) Survival analysis of flies fat-body-specifically overexpressing MCM6 (FB-Gal4;UAS-MCM6) compared to the UAS-MCM6/+ and FB-Gal4/+ genetic controls. Statistical significance between survival curves was determined by log-rank test (n = 150). (F) Differential expression of transposons, dilp2-3,5 and wDah, in each tissue (fat, gut, brain, thorax) (Supplementary file 10). Significantly changed transposons (black dots), regulated and expression is Wolbachia-dependent (red dots) (adj. p-value≤0.1) and not significantly regulated (grey dots). In response to DNA damage, including double strand breaks (DSBs), H2AX (His2Av in Drosophila) is phosphorylated (Serine 139) (Rogakou and Sekeri-Pataryas, 1999). His2Av phosphorylation (p-His2Av) thus serves as an early marker of DNA damage (Mah et al., 2010), and can be used to measure DNA damage associated with ageing in model organisms (Park et al., 2012; Wang et al., 2009) and humans (Sedelnikova et al., 2008). To determine if the fat-body-specific upregulation of DNA damage/repair response proteins in IIS mutant flies was sufficient to protect against DNA damage, we quantified the number of p-His2Av foci per nucleus in the fat body of aged (60d) flies (Figure 4B). The number of p-His2Av foci was significantly lower in the fat body of aged dilp2-3,5 mutants compared to similarly aged control flies (wDah) (Figure 4B), and only in the presence of Wolbachia (Figure 4B). Thus, our analysis suggests that DNA damage is reduced in the fat body of IIS mutant flies, and that the reduction in damage may be linked to the longevity. To determine which proteins may play a role in protecting the fat body from DNA damage, we assessed histone and chromatin proteins. However, with the exception of H2A and H2B in the fat bodies of dilp2-3,5 mutants we detected no consistent regulation of histones (Supplementary file 1–2). In agreement with our previous bioinformatic analysis, proteins associated with the GO-terms chromatin-remodelling, -silencing, -binding, and -organisation were up-regulated in fat body of dilp2-3,5 mutants (Supplementary file 9). For example, the subunits of chromatin-remodelling complexes Tip60 (MRG15, pont, and rept) (Kusch et al., 2004), ISWI, Chrac-14, and the INO80 complex were up-regulated in the fat body of IIS mutants flies (Supplementary file 2). These complexes each play a role in chromatin remodelling and DNA damage, and may act to maintain genome stability (Clapier and Cairns, 2009; Conaway and Conaway, 2009). In addition, all six subunits of the replicative helicase minichromosome maintenance complex (MCM2-7) (Bell and Dutta, 2002), which is required for both DNA repair and genome stability (Bailis and Forsburg, 2004), were up-regulated in response to reduced IIS, and only in the presence of Wolbachia (Figure 4C). We therefore, tested if tissue-specific increased expression of the minichromosome maintenance complex can decrease the level of DNA damage in the ageing fat body. Of the six up-regulated MCM subunits in the dilp2-3,5 mutant fat body, MCM6 was up-regulated to the greatest extent (12-fold) (Supplementary file 2). Overexpression of MCM6 using the constitutive, fat body-specific, Gal4 driver Fat body (FB), significantly reduced the number of p-His2Av foci in aged fat body compared to controls (Figure 4D). Importa
