Download - LFQ Analysis: Two-group Analysis

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Differential Protein Expression Analysis by Mass Spectrometry as a ServiceClaudia Fortes, Jonas Grossmann, Paolo Nanni, Witold Wolski, Christian Panse, Laura Kunz, Christian Trachsel, Nathalie Selevsek, Can Turker, Ugur Gurel, Bernd Roschitzki, and Ralph Schlapbach

Functional Genomics Center Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland

AbstractProtein expression information from proteomics high throughput DDA, DIA andSRM experiments are among the most popular proteomics services provided bythe Functional Genomics Center Zurich (FGCZ). This quantitative protein expres-sion data is used to test existing hypotheses and also to generate new scientificinsights. To assure that generated hypotheses are statistically significant a for-malized, semi automated data management and analysis process was establishedthat is able to capture a variety of scientific questions and organisms. We presenthere the entire process including experimental design, which stresses the inclusionof control samples, sample annotation, descriptive and differential data analysismethods. The workflow was already used in practice for more than 30 projects.

1 FGCZ – Customer interactions forLabel Free Quantification Service

• Step 1 - Kick-off discussions:Understand the biologists needs, do expectation management and give priceestimationsAfter a preliminary contact we try to meet physically.

• Step 2 - LFQ QC workflow:1to1 experiment; no changes expected, discuss potential problems, flag withQC passed/failedOften, customers are not familiar with protein technologies. This should not be a big hurdle thoughbut before spending a lot of time/sample/money we usually need to make sure that the customercan reproducibly extract proteins in the setting they are going to do it later for the real experiment.

• Step 3: - LFQ analysis:Run the full LFQ experiment (two groups with minimum 4 biological replicates)and delivery of the resultsAt this stage, the customer proved to be reproducible in protein extraction and is already famil-iar with some of the outputs. We make sure that all processing steps are randomized as muchas possible. Sample collection, protein extraction, digestion, mass spectrometry all these stepscan potentially give batch effects in the analysis downstream. We do delivery the Scaffold file(http://www.proteomesoftware.com/products/scaffold), several PDF reports including QC plots aswell tab-separated files which can directly be used in spreadsheet applications.

• Step 4 - Discussion of results:Discuss the results, potential pitfalls, downstream analysisAfter the customer already had a look at the results, usually some question along the processingpipeline arise. We try to anticipate these questions already upfront but most of the time it is mostbeneficial to meet involved people face-to-face and explain and have a look at the results directlytogether. Also some potential pitfalls and some specific features of the software tools that wereused are often topic in these discussions. Moreover we usually give some hints on web-tools fordata interpretation, which are easy to use and give useful information in the context of the project.

1.1 LFQ QC workflowThe workflow for the QC experiment is depicted in panel A in Figure 1. This pilotexperiment is also valuable to get a first estimate about the number of proteins ex-pected, the coverage of the identified proteins in pathways of interest and can alsoshow some (unwanted) modifications which should be considered in the analysis.

A.Qualitycontrolstep

Trypsin

diges5on

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replicates

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Dataanalysis(reproducibility,unexpectedmodifica5ons,...)

B.LabelFreeProteinquan5fica5on

Trypsin

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analysis

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Sta5s5calanalysis

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Figure 1: Schematic overview for the workflows in the LFQ service at the FGCZ. Panel A depicts the quality control

step. The customer has to show reproducibility in protein extraction by starting with the exact same biological material

and perform the protein extraction four times. All downstream analysis is exactly the same as for the real LFQ experiment.

The two groups are ”mocked-up” and we are looking for differences between the two groups although no differences are

to be expected. All differences observed are false positives introduced with biochemistry in the wetlab or by the analysis

workflow. In panel B it is shown how the minimal experimental design looks for the real LFQ experiment.

There are a number of different parameters evaluated such as the percentagesof modified or fully tryptic peptides. Percentage of single hit proteins, normaliza-tion scaling factor, percentage of regulated proteins and correlation coefficient areevaluated as well. Based on these parameters we flag such an QC experiment aseither QC passed or QC failed.

1.2 LFQ Analysis - Two group AnalysisA schematic overview is shown in panel B in Figure 1. At the LFQ service, weemphasise from the very beginning on, the samples should not be collected beforediscussing and starting the real experiment with us. Doing so, we can stress thefact that randomization at every step in sample processing can potentially have abatch effect. We carefully discuss the experimental design with our customer andalso internally, each step is tracked and randomized.

1.3 Proteins Used for Quantitation

The input matrix has the following structure.

Figure 1: Heatmap for quantifyable proteins (asinh transformed)

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untreated SignalDistributions (asinh)

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Intensity.GE1Intensity.GE2Intensity.GE3Intensity.GE4Intensity.Glu1Intensity.Glu2Intensity.Glu3Intensity.Glu4

Figure 2: Density plot for quantifyable proteins (asinh transformed)

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1.4 Imputation and Normalization

To overcome the problem of zero values in the matrix we use impuation (R-package: missForest)to estimate a value instead of loosing the value (or the line) completely. Due to the vast majorityof small values in the full dataset the imputed value is usually near back-ground level.

Shown in Figure ?? are the un-normalized but imputed quantitative values while in Figure ??the median normalization is applied.

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Figure 3: Density plot of the quant values with imputation in asinh transformation

The correlation of the vectors with the quantitative values show very well how similar the MSexperiments are. Here one should see the grouping based on the clustering. In Figure ?? themedian normalized values are used to calculate the correlation.

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Figure 4: Density plot for normalized values based on imputed matrix (asinh)

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Normalize

Figure 2: Data preprocessing/normalization pipeline used for protein quantification. A feature of the MaxQuant tool

that is used for the primary analysis is the ”match-between-run” that step sometimes is not successful.. This results in a

zero as protein intensity and should be seen as NA. We are handling these NAs by imputing a value for all cells where we

find NA (using the missForest R-package). After imputing, we are normalising the samples using a median-normalization.

This simple preprocessing proofed to be relatively robust and overfitting was not observed.

We are confident that the way we analyse the sample is robust and delivers in-teresting findings. We do use the Maxquant software as primary analysis tool[Cox et al., 2014] at the moment but we are also looking in other tools which canprovide quantitative protein estimates [Weisser et al., 2013]. We handle sparse-ness of data (missing values or NAs) by imputing using the missForest R-package[Stekhoven and Buhlmann, 2012]. Nevertheless there are some steps which wecarefully watched and re-evaluated for each project. Especially the imputation stepis delicate in cases where there are many NAs in some of the samples or in aknock-out condition where indeed the signal is not to be expected in one conditionwhile it might be present (with low signals) in the other condition.

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LFQ Analysis: Two-group Analysis

•  Yeast: grown on 1) EtOH/Glycerol vs 2) Glucose

Intensity.GE3

Intensity.GE1

Intensity.GE4

Intensity.GE2

Intensity.Glu4

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Figure 5: Correlation plot for normalized values based on imputed matrix (asinh)

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1.5 Heatmaps and Clustering for Proteins

In Figure XXX we show

Intensity.GE3

Intensity.GE1

Intensity.GE4

Intensity.GE2

Intensity.Glu4

Intensity.Glu1

Intensity.Glu3

Intensity.Glu2

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REV__YDR429CYGR235CYBL069WREV__YDR049WYER105CREV__YMR286WYML065WYJR034WREV__zz|ZZ_FGCZCont0155|;REV__zz|ZZ_FGCZCont0039|;REV__zz|ZZ_FGCZCont0013|;REV__zz|ZZ_FGCZCont0188|;REV__CON__P08779;REV__CON__Q61782;REV__zz|ZZ_FGCZCont0111|;REV__zz|ZZ_FGCZCont0088|;REV__zz|ZZ_FGCZCont0200|;REV__zz|ZZ_FGCZCont0151|;REV__CON__P35900;REV__CON__Q9D312;REV__zz|ZZ_FGCZCont0083|;REV__CON__Q04695;REV__CON__Q9QWL7;REV__CON__Q6IFX2;REV__CON__Q9Z2K1;REV__zz|ZZ_FGCZCont0187|;REV__CON__P02533;REV__zz|ZZ_FGCZCont0162|;REV__zz|ZZ_FGCZCont0046|;REV__CON__Q3ZAW8YHL004WYNR051CYNL166CYHR138CYHR132W−AYJL158CYDL065CYDR177WYLR324WYLR025WYBR279WYMR008CYGL122CYHR158CYDR508CYLR083CYMR188CYCR011CYGR199WYMR089CYNL194CYIL065CYGL129CYBR120CYBR006WREV__YPL107WYKL204WYGL028CYBR046CYEL066WYNR038WYKR082WYER134CYGL208WYML025CYDR452WYAL039CYOL088CYNL330Czz|ZZ_FGCZCont0013|YFL028CYIL064WYJL196CYBR061CYHR012WYIL126WYBL036CYLR231CYPL243WYKR070WYNL084CYHR028CYML035CYKR065CYPR019WYOR370CYEL036CYOL057WYOR014WYMR054WYBR053CYJL068CYKR026CYKL027WYNL002CYDR322WYGL027CYLR163CYMR264WYML036WYDR227WYCL033CYNL180CYOL021CYPL012WYFL034C−AYBR251WYHL021CYLR067CYBR170CYLR059CYBR034CYDR494W

14 18 22Value

03000


Count

Figure 6: Heatmap for normalized values based on imputed matrix (asinh transformed)

7

EtO

H

EtO

H

EtO

H

EtO

H

Glu

c

Glu

c

Glu

c

Glu

c

EtO

H

EtO

H

EtO

H

EtO

H

Glu

c

Glu

c

Glu

c

Glu

c

Gluc

Gluc

Gluc

Gluc

EtOH

EtOH

EtOH

EtOH

Figure 3: A straight forward yet unbiased way

to look at the clustering and the distances of the

samples is to calculate pairwise correlations between

each sample. The figure shows a visualization of

these correlation coefficients. At this point, usually

samples should be clustered together.

• 12 • • •



Intensity.GE3

Intensity.GE1

Intensity.GE4

Intensity.GE2

Intensity.Glu4

Intensity.Glu1

Intensity.Glu3

Intensity.Glu2

Intensity.GE3

Intensity.GE1

Intensity.GE4

Intensity.GE2

Intensity.Glu4

Intensity.Glu1

Intensity.Glu3

Intensity.Glu2

0.8 0.9 1Value

04

8


Count

Figure 5: Correlation plot for normalized values based on imputed matrix (asinh)

6

1.5 Heatmaps and Clustering for Proteins

In Figure XXX we show

Intensity.GE3

Intensity.GE1

Intensity.GE4

Intensity.GE2

Intensity.Glu4

Intensity.Glu1

Intensity.Glu3

Intensity.Glu2

YGR192CYAL005CYCR012WYHR174WYLR044CYAL038Wzz|ZZ_FGCZCont0179|;CON__P00761YMR186WYDL229WYOL086CYPR080W;YBR118WYKL152CYOR133W;YDR385WYKL060CYFL014WYLR259CYJR045CYER177Wzz|ZZ_FGCZCont0260|YDR155CYJR104CYNL055CYJR121WYBL099WYLL026WYJL052WYGR254WYNR016CYLR304CYER091CYGL008CYLR249WYPL061WYDR050CYKL182WYBR196CYPL231WYFL039CYMR142CYJL136CYDL185WYOL121C;YNL302CYLR048WYGL076CYOR369CYEL054C;YDR418WYAL003WYGL253WYDR502CYPL106CYOR063WYML063WYJR145C;YHR203CYMR116CYER043CYBR031WYNL178WYLL045CYKR097WREV__YKR054CYER065CYBR072WYKL085WYPR191WYNR001CYOR374Wzz|ZZ_FGCZCont0099|;CON__P35527;zz|ZZ_FGCZCont0219|;zz|ZZ_FGCZCont0195|zz|ZZ_FGCZCont0178|;zz|ZZ_FGCZCont0025|;CON__P04264;zz|ZZ_FGCZCont0077|YFR053CYOR230WYLR153CYBL030CYGL062WYCL040WYIL125WYGR234WYJL153CYBL045CYJR077CYNL037CYOR136WYNL134CYOR142WYKL067WYMR105CYLR377CYER024WYDR380WYAR035WQ0250YLR038CYFR033CYBR169CYDR343CYJR148WYKR016WYDR377WYLR295CYEL011WYGR088WYNL015WYFL018CYDR258CYHR008CYBR039WYDR148CYPL078CYPL262WYKL142WYDR533CYBR026CYER103WYCL035CREV__YMR132CYHR051WYDL181WREV__YDR356WYPR160WYGL187CYGR244CYKL016CYNL117WYKL148CYIL136WYDL215CYMR303CYBL015WYAL054CYIL155CYLL041CYOL126CYLR174WYDR529CYML120CYCR005CYOR065WYJR048WYDR298CYDR322C−AYMR145CYKL150WYBL064CYHR137WREV__YPR095CYKR057WYDL081CYPR132W;YGR118WYIL053WYGL189CYOL120C;YNL301CYGL123WREV__YLR394WYGR209CYCR088WYNL079CYOR332WYDL130WYPL037CYBR029CYMR255WYMR307WYIL078WYLR167WYOR198CYJL080CYPL160WYPR163CYHR193CYJL167WYHR039C−AYGL105WYDR381WYNL071WYLR061WYBR078WYNL049CYOL109WYNL135CYDR233CYDR099WYMR173WYDR032CYHL034CYGR282CYOR020CYDR513WYML078WYOR187WYIL051CYMR083WYDL066WYJR109CYDR127WYPL240CYER178WYNL104CYIL041WYML100WYKL210WYJR009CYLR355CYPR074CYBR025CYOR375CYJR139CYGL245WYGR180CYLL018CYDR023WYKL035WYGR094WYGL026CYOR335CYCL030CYDR158WYLR109WYDR025W;YBR048WYLR029CYIL133CYHR183WYGL031CYHR010W;YDR471WYIL073CYGR061CYLR333C;YGR027CYOL127WYER117W;YBL087CYBL092WYGL103WYOR096WYHL001WYDR064WYJL130CYKL081WYGL147CYDL191W;YDL136WYBR191WYPL249C−AYPR102C;YGR085CYLL024CYLR150WYHL015WYBR084C−A;YBL027WYIL018W;YFR031C−AYOR312C;YMR242CYBR189WYCR031CYER102W;YBL072CYDL075WYKL180WYPL220W;YGL135WYMR143W;YDL083CYGR034WYJR123WYIL069C;YER074WYML024W;YDR447CYPL090C;YBR181CYDL126CYER138C;YPL257W−B;YLR157C−B;YDR261C−DYDR226WYML028WYLR058CYPL131WYKL056CYGR155WYKR059W;YJL138CYLR340WYGR240CYBR127CYML026C;YDR450WYOL040CYLR075WYOL039WYCL043CYEL032WYOR007CYOR027WYPL004CYBL002WYJL034WYLR448WYKL156W;YHR021CYGR086CYFL045CYBR121CYJR105WYBR011CYEL034WYHR064CYER165WYMR205CYLR354CYDL055CYML055WYGL119WREV__YDR159WYLR301WYJR016CYGL009CYEL071WYGL030WYNL069CYPL143WYER081WYLR388WYDR002WYHR025WYHR068WYLR342WYDL145CYML008CYPL226WYPR033CYKL080WYER025WREV__YLL003WYGR264CYJL177WYDR091CYPR028WYDR212WYBL039CYPR043W;YJR094W−AYBL106CYDL014WYPL006WYGR162WYOR341WYMR260CYDL061CYOR182C;YLR287C−AYLR262C−AYLR185WYGL106WREV__YBL076CYOR232WYJL159WYDR510WYPR052CYOR298C−AREV__YJL073WYCR028C−AYKL192CYCR004CYMR031CYJL020CYGR037CYDR033WYPR041WYDR454CYBR286WYMR146CYDR432WYNL031C;YBR010WYBR079CYCL050CYLR325CYOR285WYPL154CYNL121CYNL007CYKR001CYKR048CYBL047CYGR253CYGR130CYLL050CYOR128CYER009WYPL225WYIL075CYDR071CYDR429CYOR117WYKL145WYDR172WYDL100CYMR309CYML042WYKL104CYDL171CYPR145WYLR438WYBR126CYGL037CYMR072WYPR183WYLR216CYAR015WYNL220WYMR226CYJL026WYIL142WYOR323CYPL048WYML070WYDR353WYPL028WYOR310CYML073CYLR197WYHR208WYER036CYKL216WYNL096CYHR179WYOR168WYML126CYOR270CYPR069CYPR181CYNL287WYAL035WYJL008CYNL010WYFL030WYML074CYHL033CYBR149WYKL157WYOL058WYPR036WYLR359WYBR143CYGL137WYHR128WYLR060WYMR318CYGR185CYLR367W;YJL190CYNL030W;YBR009CYLR180WYPR035WYGR285CYNL209WYMR217WYGR204WYHR019CYLR441CYDR037WYOR293WYGL202WYGR124WYBL076CYLR432WYCR053WYER110CYHR020WYAL012WYOR185CYIL052C;YER056C−AYOR204WYOR167CYDL195WYFL022CYMR120CYDL131WYMR108WYFL037WYDR539WYDL192WYMR012WYBR249CYNL064CYGL234WYDR129CYOL139CYER003CYOR317WYDR225W;YBL003CYDL084WYDR341CYFR044CYLR027CYDL124WYDL022WYBR221CYGL206CYDL085C−AREV__YKL176CYMR278WYBR111W−AYGL256WYHR196WREV__YDR257CYOR153WYHR191CYML056CYDR497CYKL127WYHR163WYER006WYNL014WREV__YDR523CYGR148CYDR101CYHR164CYOR371CYNL310CYDR296WYBR003WYPR051WYJL207CYLR372WYMR222CYMR312WYLR364WYFL001WYJR042WYJR101WYER029CYLR386WYPR170W−BYHR056CYJR058CYLR305CYHR202WYDL115CYNL081CYFL034C−BYKR023WYPR162CYGR013WYAL025CYDR162CYDR156WYOR078WREV__YIR024CYNL272CYJR006WYIL154CYDL088CYNL075WYDR283CYHR059WYKR083CYOL100WYPL156CYKR085CYNL215WYBL004WYJR003CYBL035CYLR241WYLR357WYPL016WCON__P02668;zz|ZZ_FGCZCont0117|YMR247CYML127WYER163CYMR124WYLL036CYGR247WYOR018WYBL105CYBR288CYNR030WYOL018CYGR143WYNL222WYNR033WYLR319CYBL057CYOR229WYOR288CYJL154CYCR061WYER142CYKR076WYER053CCON__Q86YZ3YDR194CYEL007WREV__YJL042WYIL157CYIL007CYDR289CYNL252CYOR124CYAL021CYJR008WYER010CYDL019CYJL112WYGR256WYDR068WYMR272CYNL125CYIL068CYCR035CYPL223CYER168CYIL145CYKL173WYCR054CYER164WYJL082WYHR147CYDR165WYPL196WYPL249CYKL091CYKL170WYAL049CYAL017WYER141WYOR195WYKL100CYGR140WYGR002Czz|ZZ_FGCZCont0107|YLR022CYPL116WYHR011WYDR245WYGL130WYDR515WYPR173CYIL162WYDR428CYDR182WYOR125CYLR129WYGR215WYBR171WYDL116WYKL003CYBR151WYHR099WYPL036WYO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REV__YDR429CYGR235CYBL069WREV__YDR049WYER105CREV__YMR286WYML065WYJR034WREV__zz|ZZ_FGCZCont0155|;REV__zz|ZZ_FGCZCont0039|;REV__zz|ZZ_FGCZCont0013|;REV__zz|ZZ_FGCZCont0188|;REV__CON__P08779;REV__CON__Q61782;REV__zz|ZZ_FGCZCont0111|;REV__zz|ZZ_FGCZCont0088|;REV__zz|ZZ_FGCZCont0200|;REV__zz|ZZ_FGCZCont0151|;REV__CON__P35900;REV__CON__Q9D312;REV__zz|ZZ_FGCZCont0083|;REV__CON__Q04695;REV__CON__Q9QWL7;REV__CON__Q6IFX2;REV__CON__Q9Z2K1;REV__zz|ZZ_FGCZCont0187|;REV__CON__P02533;REV__zz|ZZ_FGCZCont0162|;REV__zz|ZZ_FGCZCont0046|;REV__CON__Q3ZAW8YHL004WYNR051CYNL166CYHR138CYHR132W−AYJL158CYDL065CYDR177WYLR324WYLR025WYBR279WYMR008CYGL122CYHR158CYDR508CYLR083CYMR188CYCR011CYGR199WYMR089CYNL194CYIL065CYGL129CYBR120CYBR006WREV__YPL107WYKL204WYGL028CYBR046CYEL066WYNR038WYKR082WYER134CYGL208WYML025CYDR452WYAL039CYOL088CYNL330Czz|ZZ_FGCZCont0013|YFL028CYIL064WYJL196CYBR061CYHR012WYIL126WYBL036CYLR231CYPL243WYKR070WYNL084CYHR028CYML035CYKR065CYPR019WYOR370CYEL036CYOL057WYOR014WYMR054WYBR053CYJL068CYKR026CYKL027WYNL002CYDR322WYGL027CYLR163CYMR264WYML036WYDR227WYCL033CYNL180CYOL021CYPL012WYFL034C−AYBR251WYHL021CYLR067CYBR170CYLR059CYBR034CYDR494W

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Figure 6: Heatmap for normalized values based on imputed matrix (asinh transformed)

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Figure 4: Shown is the heatmap with its hierar-

chical clustering (arcsinh(protein intensities)) for pro-

teins (rows) and all samples (columns) Also here we

should get the samples clustered.

The hierarchical clustering on the correlation matrix (see Figure 3) or on the fullprotein intensities (see Figure 4) should show if the groups cluster together and ifthere is structure in the data. Also it helps to judge on potential batch effects whichmight have played a role in sample processing.

2 Application ScenarioTo demonstrate the usefulness of our workflow, we used yeast cells grown at twodifferent nutrient sources. All the steps and tools used in this scenario are exactlythe ones as if this would be a customer project.

Setup

• Sample: Yeast cells, grown on A) EtOH/Glycerol vs B) Glucose

• Protein extraction and digestion: Shock-freeze and in solution high intensityfocused ultrasound (HIFU)

• Mass spectrometer: Thermo Q-Exactive

• Primary analysis: MaxQuant v1.4.1.2

Results

• Proteingroups quantified (min 2 peptides): 2615 (protFDR = 5%)

• Differentially expressed proteins:

– FoldChange bigger than 1.5 and pValue smaller than 0.05: 647 (24.7%)– FoldChange bigger than 2 and pValue smaller than 0.01: 267 (10.2%)

• 13 • • •



2 FGCZ - Two Group Analysis (normalized and imputed)

2.1 Stringent Thresholds

Here we show for the imputed normalized and imputed non-normalized matrices the result of atwo group analysis. Significant calls are made with pValue smaller 0.01 and fold changes above2. We output first the length of the list (the list is also written to a text file with group means,pValue, FDR, FoldChange). Additionally we output graphically the Volcano plot.

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Figure 7: VolcanoPlot for normalized and imputed proteins (asinh) with stringent thresholds.

Working with the following parameters:FoldChangThreshold: 2SignificanceThreshold (pvalue): 0.01Total number of Proteins in list: 2615Total number of significant Proteins: 275

That is a percentage: 10.52

8

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Up in Glucose

Figure 5: The volcano plot depicts the differential expression for the comparison of the two groups. The log2-fold-

change is visualized on the x-axis while the significance (p-Value) is plotted on the y-axis.

Network and Enrichments

The enrichment for proteins being up-regulated in the glucose conditions point tohappy living yeast cells that are growing and dividing. Looking at the protein up-regulated in the EtOH/Glycerol condition we can observe that several categorieswith energy delivery are enriched.

• 15 • • •

•  String-network and Enrichment: up in Glucose (n=141) vs all proteins identified




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8

Up in Glucose

LFQ Analysis: Over-representation Analysis

cytoplasmic translation

ribosome biogenesis

ribonucleoprotein complex biogenesis

rRNA processing

rRNA metabolic process

ribosome assembly

ncRNA processing

regulation of translational fidelity

ribosomal large subunit biogenesis

ribonucleoprotein complex assembly

Enrichment for GO Processes Upregulated in Glucose

−log10(p.adjust(pValue))

0 5 10 15 20 25 30 35

Figure 6: The figure shows the STRING network and the enriched GO processes for the proteins that are upreg-

ulated if the yeast cells are grown in glucose media (n=141). Cytoplasmic translation is highly enriched and the strong

network cluster is associated to ribosomal proteins

• 14 • • •

•  String-network and Enrichment: up in EtOH (n=126) vs all proteins identified




−6 −4 −2 0 2 4 6

02

46

Volcano Plot

log2(grp1/grp2)

−log10(pValue)

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8

Up in EtOH/Glycerol

LFQ Analysis: Over-representation Analysis

energy derivation by oxidation of organic compounds

cellular respiration

generation of precursor metabolites and energy

aerobic respiration

oxidative phosphorylation

purine ribonucleoside triphosphate metabolic process

ribonucleoside triphosphate metabolic process

purine nucleoside triphosphate metabolic process

ATP metabolic process

purine nucleotide metabolic process

Enrichment for GO Processes Upregulated in EtOH/Glycerol

−log10(p.adjust(pValue))

0 5 10 15

Figure 7: The figure shows the STRING network and the enriched GO processes for the proteins that are upregu-

lated if the yeast cells are grown in EtOH/Glycerol media (n=126). Here, (among others) cellular respiration and energy

delivery categories are enriched.

3 OutlookCurrently, we are working on the generalisation of this workflow so that we canplug different inputs e.g. peptide centric quantification (quantified elution groups)and not directly working with protein intensities and moreover we extended theanalysis to handle multigroup analysis (AOV) and we are implementing additionaldownstream analysis options such as automatic enrichment analysis and networkanalysis (e.g. using GSEA-packages in R such as TopGO [Alexa and Rahnen-fuhrer, 2010] or SetRank [Petrovic et al., 2015] and the RString interface [Szklar-czyk et al., 2015]).

ReferencesA. Alexa and J. Rahnenfuhrer. topGO: topGO: Enrichment analysis for Gene Ontology, 2010. R package version 2.22.0.

J. Cox, MY. Hein, CA. Luber, I. Paron, N. Nagaraj, and M. Mann. Accurate proteome-wide label-free quantification bydelayed normalization and maximal peptide ratio extraction, termed maxlfq. Mol Cell Proteomics, 13(9):2513–2526,2014.

M. Petrovic, C. Simillion, P. Kruzliak, J. Sabo, and M. Heller. Doxorubicin affects expression of proteins of neuronalpathways in mcf-7 breast cancer cells. Cancer Genomics Proteomics, 12(6):347–358, 2015.

DJ. Stekhoven and P. Buhlmann. Missforest–non-parametric missing value imputation for mixed-type data. Bioinformat-ics, 28(1):112–118, 2012.

D. Szklarczyk, A. Franceschini, S. Wyder, K. Forslund, D. Heller, J. Huerta-Cepas, M. Simonovic, A. Roth, A. Santos, KP.Tsafou, M. Kuhn, P. Bork, LJ. Jensen, and C von Mering. String v10: protein-protein interaction networks, integratedover the tree of life. Nucleic Acids Res, 43:447–452, 2015.

H. Weisser, S. Nahnsen, J. Grossmann, L. Nilse, A. Quandt, H. Brauer, M. Sturm, E. Kenar, O. Kohlbacher, R. Aebersold,and L. Malmstrom. An automated pipeline for high-throughput label-free quantitative proteomics. J Proteome Res,12(4):1628–1644, 2013.

Contact: Jonas Grossmann, Functional Genomics Center Zurich, Y32 H94, Winterthurerstr. 190, CH-8057 Zurich, SWITZERLAND, Phone: +41 44 635 39 10, Fax: +41 44 635 39 22, EMail: [email protected], URL: www.fgcz.ethz.ch – For questions: [email protected]