Inference of Predictive Phospho-regulatory Networks from LC-MS/MS

Phosphoproteomics Data

Sebastian Vlaic

, Robert Altwasser

, Peter Kupfer

, Carol L. Nilsson

, Mark Emmett

Anke Meyer-Baese

and Reinhard Guthke

Leibniz Institute for Natural Product Research and Infection Biology, Hans-Kn¨oll-Institute,

Beutenbergstr. 11a,D-07745 Jena, Germany

Department of Pharmacology & Toxicology, The University of Texas Medical Branch,

Galveston, Texas 77555-1074, U.S.A.

Department of Scientiﬁc Computing, Florida State University, Tallahassee, Florida 32310-4120, U.S.A.

Keywords:

Phospho-regulatory networks, Network Inference, Glioblastoma Cancer Stem Cells.

Abstract:

In the ﬁeld of transcriptomics data the automated inference of predictive gene regulatory networks from high-

throughput data is a common approach for the identiﬁcation of novel genes with potential therapeutic value.

Sophisticated methods have been developed that extensively make use of diverse sources of prior-knowledge

to obtain biologically relevant hypotheses. Transferring such concepts to the ﬁeld of phosphoproteomics data

has the potential to reveal new insights into phosphorylation-related signaling mechanisms. In this study we

conceptually adapt the TILAR network inference algorithm for the inference of a phospho-regulatory network.

Therefore, we use published phosphoproteomics data of WP1193 treated and IL6-stimulated glioblastoma

stem cells under normoxic and hypoxic condition. Peptides corresponding to 21 differentially phosphorylated

proteins were used for network inference. Topological analysis of the phospho-regulatory network suggests

lamin B2 (LMNB2) and spectrin, beta, non-erythrocytic 1 (SPTBN1) as potential hub-proteins associated with

the alteration of phosphorylation under the observed conditions. Altogether, our results show that inference

of phospho-regulatory networks can aid in the understanding of complex molecular mechanisms and cellular

processes of biological systems.

1 INTRODUCTION

Network inference is a well established tool to un-

cover the complex underlying regulatory mechanisms

of biological systems. The immediate reaction of a

cell towards changing conditions or external perturba-

tions is often mediated by alteration of already exist-

ing proteins via post-translational modiﬁcations such

as phosphorylation, methylation, acetylation, sumoy-

lation or ubiquitination (Seet et al., 2006).

Changes in phosphorylation were found to have

an impact on almost all aspects of cell biology

(Jørgensen and Linding, 2008). Therefore, the analy-

sis of causal relations between changes in phosphory-

lation state of proteins is essential for understanding

regulatory mechanisms of the biological system stud-

ied. For the inference of gene regulatory networks

(GRNs) a variety of diverse approaches have been

proposed, which can be used to identify causal rela-

tions in the expression of genes (Hecker et al., 2009b).

The adaptation and application of concepts developed

for GRNs to data from other layers of regulation has

therefore the potential to provide new insights into

regulatory relations of cellular processes.

With analytical methods such as high resolution mass

spectrometry, the quality and quantity of phosphopro-

teomics data is continuously increasing. This offers

new possibilities but also challenges regarding inter-

pretation and computational analysis of large scale

data sets. (Terfve and Saez-Rodriguez, 2012) di-

vided the approaches that have been applied to high-

throughput phosphoproteomics data into two cate-

gories. On the one hand, there are descriptive meth-

ods such as mapping of measured data to pathways,

enrichment analysis, and supervised as well as unsu-

pervised learning methods. These approaches pro-

vide an overview of the data and often serve as a

starting point for a more detailed investigation. On

the other hand, methods such as difference and dif-

ferential equations, Bayesian, boolean and rule-based

Vlaic, S., Altwasser, R., Kupfer, P., Nilsson, C., Emmett, M., Meyer-Baese, A. and Guthke, R.

Inference of Predictive Phospho-regulatory Networks from LC-MS/MS Phosphoproteomics Data.

DOI: 10.5220/0005743000850091

In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016) - Volume 3: BIOINFORMATICS, pages 85-91

ISBN: 978-989-758-170-0

networks are considered to be predictive since they

are able to process measured data and produce inter-

pretable models that predict the behavior of biological

systems.

In this study, we apply an inference algorithm that

combines these two concepts by using knowledge of

descriptive approaches to produce predictive models,

based on a system of linear equations. To this end,

we apply the previously published transcription fac-

tor binding site integrating LARS (TILAR) algorithm

(Hecker et al., 2009a), which uses a linear model to

infer GRNs from expression data based on least an-

gle regression (LARS) (Efron et al., 2004). One fea-

ture of TILAR is its ability to make use of several

sources of prior-knowledge. Initially, a network struc-

ture template is created, which is iteratively reﬁned to

explain the measured data in the best possible way,

while integrating as much prior-knowledge as possi-

ble. The concept of modeling is based on the idea that

the expression of genes is regulated by transcription

factors (TFs). This introduces the possibility of using

known transcription factor binding sites (TFBSs) as

knowledge base to group genes that are regulated by

the same TFs.

Analogous to the TFBS-based concept of TILAR, we

will show how prior-knowledge about phosphoryla-

tion regulating phosphatases and kinases (PKs) can

be used to group different phosphorylation sites creat-

ing a network template for the inference of phospho-

regulatory networks (PRNs). We will exemplify this

idea by applying the algorithm to published phospho-

proteomics data of glioblastoma stem cells (GSC11)

perturbed with the JAK2/STAT3 phosphorylation in-

hibitor WP1193, IL-6 stimulation and hypoxia.

2 METHODS

2.1 Data Pre-processing

Raw data from (Nilsson et al., 2010) was processed

using R 2.14 (R Core Team, 2014). Duplicated pep-

tide measurements were mean averaged and log10

transformed. Peptides containing no tyrosine, serine

or threonine amino acid were removed from the list of

measured peptides, since only those can be phospho-

rylated.

2.2 TILAR Concept of Modeling for the

Inference of GRNs

The basic idea of the TILAR algorithm as published

by (Hecker et al., 2009a) is to include knowledge

about the expression regulation of the modeled genes

by TFs. Accordingly, the resulting GRNs are com-

posed of relations between the regulating TFs and

their target genes (TF-to-gene relations) as well as re-

lations between the modeled genes and the regulating

TFs (gene-to-TF relations). The TF-to-gene relations

can be, e.g., derived from literature, TFBS-databases

or TFBS prediction tools and serve as a network struc-

ture template for the inference of the gene-to-TF re-

lations. The prior-knowledge structure template can

thus effectively assist the algorithm in the identiﬁ-

cation of biologically meaningful networks. Using

TILAR, the predicted expression value of a gene i (ˆx

)

(with i = 1. . . N) is the sum of the weighted expres-

sion values (w

) of all other genes j via the TF

k (with k = 1. . . F) if (1) the regulated gene i has a

TFBS for the TF k and (2) the gene j does not have a

TFBS for the TF k (equation 1).

ˆx

∑

k=1

∑

j=1

(1− b







1, if gene j possesses a binding

site for TF k

0, else

(1)

The result is a system of linear equations for each

gene that has a TFBS for at least one TF. The inter-

action weights w

corresponding to the gene-to-TF

relations can then be estimated by LARS regression

(see (Hecker et al., 2009a) for details).

2.3 Network Inference

Network inference of PRNs is performed similar to

the inference of GRNs using TILAR (see (Hecker

et al., 2009a) for details). The conceptually adapted

TILAR algorithm uses the PK binding knowledge as

a structure template. Variable selection and estima-

tion is performed using LARS. Initially, the model

that minimizes Mallows Cp statistic (Mallows, 1973)

is selected and the achieved residual sums of squares

(RSS) serves as the initial error. Subsequently, step-

wise backward elimination is performed to iteratively

remove PK-to-peptide edges from the template for

which inference with LARS results in a model with a

RSS lower than the one measured in the previous iter-

ation. The iterative algorithm stops when the removal

of a PK-to-peptide knowledge edge does not decrease

the RSS of the model. The ﬁnal model is then selected

based on the drop of the RSS compared to the amount

of model parameters. The model parameters are then

estimated using linear regression.

BIOINFORMATICS 2016 - 7th International Conference on Bioinformatics Models, Methods and Algorithms

3 RESULTS

3.1 Data Pre-processing

Peptides with an absolute log10 fold-change greater

0.3 between treatment and control were selected as

differentially phosphorylated peptides (DPPs). This

was checked for the WP1193 treated, IL6 stimulated

cells with respect to their corresponding WP1193

treated control under normoxic (NO) (IL6-NO) and

under hypoxic (HO) (IL6-HO) conditions separately.

11 DPPs were identiﬁed for the IL6-NO condition and

13 DPPs for the IL6-HO condition, each correspond-

ing to a unique protein (Table 1). The results show

that only 2 peptides overlap between these two sets of

DPPs, corresponding to septin 2 (SEPT2) and spec-

trin repeat containing, nuclear envelope 2 (SYNE2).

Furthermore, we identiﬁed 28 DPPs between the two

stimuli, i.e., IL6-NO and IL6-HO respectively (HO-

NO), to include the effect of the condition under

which the cells where cultured.

Table 1: Proteins (Name) corresponding to the DPPs iden-

tiﬁed in the different comparisons.

Comparison Name

IL6 - NO EIF3C, GTF2F1, LMNA, MSN, NES,

NUMA1, SEPT2, SLTM, SPTBN1,

SRSF1, SYNE2

IL6 - HO EIF4G3, FSCN1, HSP90B1, ILF3,

KRT1, LMNB2, MARCKS, NOLC1,

RBM8A, SEPT2, SYNE2, TUBB3,

YBX1

HO - NO CLASP1, EIF3C, EIF4G3, G3BP1,

GTF2F1, ILF3, KRT1, LMNB2,

MAP1B, MARCKS, MCM3, MSN,

MYH9, NES, NOLC1, NUMA1,

PI4KB, PLCB3, SEPT2, SEPT7,

SLTM, SRSF1, SRSF11, SYNE2,

TUBB3, VIM, YBX1

DAVID (Huang et al., 2009) was applied to identify

signiﬁcantly enriched Gene Ontology (GO)-terms us-

ing the union of all sets of DPPs. For a Benjamini-

Hochberg (BH)-corrected p-value < 0.01 the results

show mainly categories related to the cell cycle or

structure building processes (Table 2).

3.2 Network Inference

3.2.1 Application of the TILAR Concept to

Phosphoproteomic Data

The TILAR algorithm is primarily applied for the

inference of GRNs using gene expression data (see

methods). The basic idea of the adapted algorithm is

that a change in the phosphorylation state of a peptide

Table 2: List of signiﬁcantly enriched GO-terms identiﬁed

with DAVID. Shown are the GO-terms (GO-term), the cor-

responding id (GO-term ID), the number of DPPs associ-

ated with the term (Count) and the BH-corrected p-value

(Pvalue).

GO-term

GO-term Count Pvalue

GO:0005198 structural molecule

activity

12 1.16e-5

GO:0008092 cytoskeletal protein

binding

9 8.47e-4

GO:0000279 M phase 8 1.56e-3

GO:0003779 actin binding 7 3.35e-3

GO:0022403 cell cycle phase 8 3.51e-3

GO:0003723 RNA binding 9 5.15e-3

GO:0007049 cell cycle 9 7.77e-3

GO:0048285 organelle ﬁssion 6 8.23e-3

GO:0022402 cell cycle process 8 8.46e-3

GO:0000087 M phase of mitotic

cell cycle

6 9.26e-3

alters the activity of the corresponding protein. As a

result, this protein potentially changes the activity of

PKs and therefore, the phosphorylation state of an-

other peptide. Given our data the identiﬁed DPPs all

correspond to a unique protein and thus, the adaption

is straight forward and can be formulated as outlined

in equation 1 in the methods section. The predicted

change in phosphorylation state of the peptide i (x

, with i = 1. . . N) is the sum of the weighted (w

)

change in activity of the protein j (w

) via the PK

k (with k = 1. . . F ) if (1) the change in the phosphory-

lation state of peptide i is mediated by the PK k , and

(2) the phosphorylation state of any peptide of protein

j is not.

3.3 Extraction of Prior-knowledge

According to the adapted concept there are two types

of prior-knowledge that can be integrated into the in-

ference process, i.e., knowledge about PKs that are

known to alter the phosphorylationof any of the DPPs

(PK-to-peptide) and knowledge about relations be-

tween the corresponding proteins and the regulating

PKs (protein-to-PK). Accordingly, PhosphoSitePlus

(Hornbeck et al., 2012) was used to ﬁnd PKs known

to alter the phosphorylation state of the DPPs. Using

this approach, regulators for ﬁve DPPs were extracted

correspondingto the proteins phospholipaseC, beta 3,

phosphatidylinositol-speciﬁc (PLCB3), minichromo-

some maintenance complex component 3 (MCM3),

myosin (MSN), heavy chain 9, non-muscle (MYH9),

SEPT2 and eukaryotic translation initiation factor

4 gamma, 3 (EIF4G3). Additionally, NetworKIN

(Linding et al., 2008) as well as GPS 2.0 (Xue et al.,

2008) were used to predict binding PKs for the re-

Inference of Predictive Phospho-regulatory Networks from LC-MS/MS Phosphoproteomics Data

maining DPPs. The obtained prior-knowledge net-

work was used as a template for the network infer-

ence with TILAR. We also used PathwayStudio 9

(Nikitin et al., 2003) to ﬁnd known regulatory interac-

tions between the proteins of the corresponding pep-

tides and the identiﬁed PKs (protein-to-PK) as well

as known regulatory interactions between the proteins

themselves. However, we did not ﬁnd any supporting

known relation.

3.4 Network Inference

DPPs with corresponding proteins associated to

the GO-terms ’cell cycle’ (9 DPPs) and ’structural

molecule activity’ (12 DPPs) were selected to in-

vestigate the effect of IL6 stimulation in WP1193

treated GSC11 cells with respect to the different oxi-

dation environments. Four DPPs were found to over-

lap between both groups leading to a total of 17

DPPs. While ’structural molecule activity’ is the

term with the smallest p-value we selected ’cell cy-

cle’ as a second category since it is the most gen-

eral term among the other cell cycle related GO-

terms. Moreover, all DPPs annotated with one of the

cell-cycle associated GO-terms are contained in GO-

term ’cell cycle’. Additionally, we included myristoy-

lated alanine-rich protein kinase C substrate (MAR-

CKS), GTPase activating protein SH3 domain Bind-

ing Protein 1 (G3BP1), general transcription factor

IIF, polypeptide 1, 74kDa (GTF2F1) and Y box bind-

ing protein 1 (YBX1), as the corresponding peptides

share common PK regulators with the 17 selected

peptides. Altogether, 21 DPPs were selected for the

inference. After reﬁnement of the initial structure

template and LARS regression (see methods) the ﬁ-

nal model was manually selected. Figure 1 shows that

an increase in the number of parameters (green line)

in the model beyond the selected cutoff of 41 (black

dotted line) does not drastically decrease the RSS (red

line).

The ﬁnal PRN (Figure 2) is composed of 18 inferred

edges, 36 prior-knowledge edges, 19 PK-nodes and

0 20 40 60 80 100 120

0.0

0.2

0.4

0.6

0.8

1.0

model

RSS scaled to maximum

RSS

size

number of parameters

Figure 1: Drop of the RSS with increasing model size.

18 proteins, representing the corresponding DPPs.

The proteins keratin 1, type II (KRT1), MSN and

YBX1 are not included in the network since no

protein-to-PK edge was inferred connecting the cor-

responding DPPs to any PK in the network. Lamin

B2 (LMNB2) and spectrin, beta, non-erythrocytic 1

(SPTBN1) are the two nodes with the highest out-

degree indicating a signal distributing function in the

PRN. Stimulating the cells with IL6, the peptide that

denotes for LMNB2 is differentially phosphorylated

under HO condition and not affected under NO con-

dition, whereas SPTBN1 is dephosphorylated under

NO conditions and not affected under HO conditions.

Notably, the PRN itself shows a clustering of proteins

associated with either of the two GO terms. Inter-

leukin enhancer binding factor 3, 90kDa (ILF3), nu-

cleolar and coiled-body phosphoprotein 1 (NOLC1),

septin 7 (SEPT7), SEPT2, MARCKS and CLASP1

form a cluster that is mainly regulated by LMNB2

and tubulin, beta 3 class III (TUBB3). These pro-

teins are mostly associated with the mitotic phase of

the cell cycle. Interestingly, all DPPs beside SEPT2

are affected by IL6 treatment in HO, but not NO. In

contrast, GTF2F1, lamin A/C (LMNA), nestin (NES)

and nuclear mitotic apparatus protein 1 (NUMA1) are

mainly regulated by SPTBN1. All of the associated

DPPs are affected by IL6 treatment in NO but not HO.

4 DISCUSSION

In our data set we identiﬁed only DPPs correspond-

ing to unique proteins. However, it is well known

that multiple phosphorylation sites can belong to the

same protein and can be phosphorylated indepen-

dently producing many phosphoisoforms leading to

a potentially distinct activity of the protein (Yang,

2005). In cases of multiple DPPs for one pro-

tein, these biological properties are important and

will have to be considered in future studies. Our

results highlight two structurally connected groups

of proteins in the inferred PRN, which are either

mostly differentially phosphorylated in HO (ILF3,

TUBB3, NOLC1, SEPT2, CLASP1, LMNB2) or in

NO (SPTBN1, NES, LMNA, GTF2F1, NUMA1).

Within the PRN the de-phosphorylation of ILF3 and

NOLC1 is mainly due to the negative relation to the

phosphorylated MARCKS. This is in agreement with

literature as ILF3 and NOLC1 are known to be phos-

phorylated during the mitotic phase of the cell cy-

cle (Smith and Miskimins, 2011; Pai et al., 1995).

Moreover, Rombouts et al. hypothesized based on

their data that phosphorylation of MARCKS could in-

hibit the cell cycle (Rombouts et al., 2012). In our

BIOINFORMATICS 2016 - 7th International Conference on Bioinformatics Models, Methods and Algorithms

Figure 2: The inferred PRN. Rectangles denote for PKs and circles represent Proteins of the corresponding DPPs. GO-terms

are outlined as octagons and associated proteins are connected with dotted lines. Blue edges correspond to the extracted

PK-to-peptide prior-knowledge while red and green edges represent the inferred inhibiting and activating edges, respectively.

Colored annotation boxes denote for direction of regulation (green: up; red: down; yellow: none) for the comparisons (left:)

IL6-NO; (middle:) IL6-HO; (right:) HO-NO.

PRN, the increase in phosphorylated MARCKS fol-

lows an increase in LMNB2 phosphorylation. Lamins

are known to be phosphorylated during mitosis to me-

diate matrix disassembly (Dechat et al., 2008). This

indicates that the mitotic phase might be initiated un-

der the HO condition, but not the NO condition, with

MARCKS preventing the phosphorylation of mitosis

important proteins such as ILF3 and NOLC1 as well

as SEPT2 and SEPT7. Both proteins are associated

with the coordination of cytokinesis (Spiliotis et al.,

2005). According to the inferred PRN the second

hub-protein is SPTBN1. In erythrocytes, the phos-

phorylation of this protein was found to be linked

to mechanical stability of the membrane. Manno et

al. demonstrated that increased phosphorylation of

SPTBN1 destabilizes the membrane while reduced

phosphorylation has the adverse effect (Manno et al.,

1995). In the PRN, SPTBN1-phosphorylationis posi-

tively associated with SEPT7 and SEPT2 phosphory-

lation and negatively related to MARCKS phospho-

rylation. This indicates that destabilization of the

membrane by SPTBN1 could be directly linked to

mitosis. The data shows that SPTBN1 phosphory-

lation decreases upon IL6 stimulation in NO while

no effect is seen in HO. Other proteins connected to

SPTBN1 are NUMA1, NES, GTF2F1 and LMNA, all

of which are negatively related to SPTBN1 phospho-

rylation. NUMA1 was reported an important mitotic

component by contributing to the initiation and main-

tenance of the focused spindle poles (Sparks et al.,

1995). Interestingly, Sparks et al. observed multi-

ple mitotic phosphorylation events occuring in simi-

lar timing of lamin B phosphorylation (Sparks et al.,

1995). Thus, our results show that phosphorylation

of SPTBN1 might negatively control the phosphory-

lation of NUMA1 acting in an antagonistic fashion

to the effect of LMNB2 phosphorylation. Similar to

NUMA1 the phosphorylation of NES is also posi-

tively related to the phosphorylation of LMNB2 and

negatively related to the phosphorylation of SPTBN1.

NES phosphorylation was shown to be an important

regulator for its organizationand dynamics during mi-

tosis in a neuronal progenitor cell line (Sahlgren et al.,

2001). Similar to NES the data reports LMNA phos-

phorylation to be lower in IL6 treated cells in NO,

but not in HO. Like LMNB2, LMNA phosphorylation

is associated with matrix disassembly in mitosis. Fi-

nally, phosphorylation of GTF2F1 is associated with

the stimulation of transcription (Solow et al., 2001).

The observed de-phosphorylation in IL6 treated cells

in NO therefore might correspond to a decreased tran-

scription rate.

Inference of Predictive Phospho-regulatory Networks from LC-MS/MS Phosphoproteomics Data

5 CONCLUSION

In this study, we used phosphoproteomics data de-

rived from LC-MS/MS to illustrate the inference of

phospho-regulatory networks based on the modeling

concept of the published TILAR algorithm. The ad-

vantage of this approach is that knowledge about reg-

ulating phosphatases and kinases serves as a knowl-

edge base to create a network structure template that

guides the inference. This way, prior-knowledge

can be automatically integrated to create biologi-

cally meaningful, intuitive network models represent-

ing the experimentally measured data. For the in-

ference of the PRN we used published data measur-

ing the changes in phosphorylation of proteins from

JAK2/STAT3 phosphorylation inhibitor WP1193 per-

turbed GSC11 cells treated with IL6 under NO and

HO conditions. In total, 21 DPPs were selected for

the inference with most of them related to the GO-

terms ’structural molecule activity’ and ’cell cycle’.

Our results suggest that the oxygen concentration has

an impact on IL6 induced changes in protein phos-

phorylation. While phosphorylation of most of the

selected DPPs does not change upon IL6 treatment in

NO, there is an increased phosphorylation in mitosis-

associated proteins such as LMNB2 and MARCKS in

HO. This exempliﬁes how PRNs can aid in the inter-

pretation of phosphoproteomics data. However, due

to the shortage of experimental data the derived hy-

potheses will have to be veriﬁed using additional ex-

perimental data.

ACKNOWLEDGEMENTS

This work was supported by the BMBF (Virtual Liver

Network, FKZ: 0315736) and the excellence gradu-

ate school Jena School for Microbial Communication

(JSMC).

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Inference of Predictive Phospho-regulatory Networks from LC-MS/MS Phosphoproteomics Data