# Investigating perturbed pathway modules from gene expression data via structural equation models

- Daniele Pepe†
^{1}Email author and - Mario Grassi†
^{1}

**15**:132

https://doi.org/10.1186/1471-2105-15-132

© Pepe and Grassi; licensee BioMed Central Ltd. 2014

**Received: **26 August 2013

**Accepted: **25 April 2014

**Published: **6 May 2014

## Abstract

### Background

It is currently accepted that the perturbation of complex intracellular networks, rather than the dysregulation of a single gene, is the basis for phenotypical diversity. High-throughput gene expression data allow to investigate changes in gene expression profiles among different conditions. Recently, many efforts have been made to individuate which biological pathways are perturbed, given a list of differentially expressed genes (DEGs). In order to understand these mechanisms, it is necessary to unveil the variation of genes in relation to each other, considering the different phenotypes. In this paper, we illustrate a pipeline, based on Structural Equation Modeling (SEM) that allowed to investigate pathway modules, considering not only deregulated genes but also the connections between the perturbed ones.

### Results

The procedure was tested on microarray experiments relative to two neurological diseases: frontotemporal lobar degeneration with ubiquitinated inclusions (FTLD-U) and multiple sclerosis (MS). Starting from DEGs and dysregulated biological pathways, a model for each pathway was generated using databases information biological databases, in order to design how DEGs were connected in a causal structure. Successively, SEM analysis proved if pathways differ globally, between groups, and for specific path relationships. The results confirmed the importance of certain genes in the analyzed diseases, and unveiled which connections are modified among them.

### Conclusions

We propose a framework to perform differential gene expression analysis on microarray data based on SEM, which is able to: 1) find relevant genes and perturbed biological pathways, investigating putative sub-pathway models based on the concept of disease module; 2) test and improve the generated models; 3) detect a differential expression level of one gene, and differential connection between two genes. This could shed light, not only on the mechanisms affecting variations in gene expression, but also on the causes of gene-gene relationship modifications in diseased phenotypes.

### Keywords

Structural equation modeling (SEM) Model generation Pathway analysis Perturbed models Perturbed edges## Background

Most of known diseases are complex diseases. This means that they are caused by the combination of genetic and environmental factors. The introduction of the concept of network biology [1] allowed the application of network based approaches for studying this type of diseases. These approaches rely on the possibility to represent molecules, as proteins or genes, as interaction networks. Microarray experiments of gene expression represent a useful tool to examine the change of gene expression profile in diseases. Many efforts were performed to build, starting from gene expression, molecular networks. This activity is sometimes referred to as reverse engineering of gene regulatory networks [2]. One type of method applied for this goal, relies on the Structural Equation Modeling (SEM), a general methodology used to address questions about complex systems [3]. SEM finds a number of applications in biological networks, for example in the inference of causal phenotype networks (see [4] for a review), genome-wide association studies (GWAS) and gene-environment interactions [5, 6], as well as to measure effects of quantitative trait loci (QTLs) in linkage analyses [7–9]. The use of SEM in the analysis of microarray is not new. One of the first applications is shown by [10], who demonstrated that covariance structure analysis is a useful statistical method to find common transcriptional factors for a set of genes and to specify and evaluate hypothesized biological pathways. [11] applied SEM systematically for gene network reconstruction using gene expression data pre-processed with genetic algorithms. In most of the applications of SEM the aim is to infer networks starting from data. Thus, we can define these approaches as exploratory approaches. More recently, [12] described a confirmatory approach in microarray analysis.

In this paper we propose a SEM pipeline that, from initial and revised *a priori* network models, obtained by pathway analysis [13], is able to compare the path strengths between several groups and to determine the effect of factors analyzed on the paths. Our framework takes into consideration the generation of pathway models based on the principles of network theory such as the small network phenomena and the detection of modules [14]. Firstly considering how differentially expressed genes (DEGs) are connected by other genes in the microarray, we try to bring out which modifications in the gene network could be responsible of the differences observed between groups considered. Our approach relies on: 1) curated biological pathway databases, 2) the principles that characterize disease genes in biological networks, 3) grouping genes in Protein Information Resource (PIR) super-families [15] for facilitating the interpretation of the model. The proposed SEM pipeline is a combination of data-driven and knowledge-driven approaches. In fact, in order to generate perturbed pathway modules, we used curated biological pathways, representing the *a priori* biological knowledge about genes and their connections. The hardest part is to highlight which portion of the pathway is actually distinctive of the phenomenon being analyzed. We consider, as initial model, the one obtained from the shortest paths between every couple of DEGs. This process preserves the biological knowledge completely, as the connections in the new model are not inferred, but already present in the original pathway. The model is then fitted with SEM and improved by balancing between data-driven and knowledge-driven evidences obtained by the combination of SEM with the knowledge enclosed in public databases, relative to real and putative connections among genes. Finally, SEM with multiple group analyses supplies useful information to clinicians and biologists about experimental group differences, unveiling which connections and genes are statistically significant in the perturbed pathway models.

## Methods

### Illustration of the proposed pipeline

### Structural equations models (SEM)

SEM is a statistical procedure for confirmatory causal inference originated from path analysis proposed in 1921 by the American geneticist Sewall Wright [21]. It is based on multivariate linear regression equations, where the response variable in one regression equation may appear as a predictor in another equation. Indeed, variables may influence one-another reciprocally, either directly or through other variables as intermediaries. Additionally, correlated or uncorrelated unmeasured variables may indicate the presence of unobserved factors that influences observed variables.

*V*to be the index set of the

*Y*observed variables, represented as the “parent” set {

*pa*(

*i*)|

*i*∈

*V*}, i.e. the explanatory variables of

*Y*

_{ i }, or as the “siblings” set {

*sib*(

*i*)|

*i*∈

*V*}, i.e. the unmeasured linked variables with

*Y*

_{ i }, respectively. These sets determine a system of linear equations:

The system of linear equations affirms that every node is characterized by the relationships with his parents, while the covariance structure describes the relationships between unobserved nodes.

They encode two distinct causal assumptions: (1) a “weak” assumption on the possible existence of (direct) casual influences of explanatory variables on *Y*_{
i
}, and (bi-directed) correlated unmeasured variables *U*_{
i
}, quantified by the regression (path) coefficients *β*_{
ij
}, and the covariances *ψ*_{
ij
}, respectively; and 2) a “strong” assumption based on the absence of (direct) causal influences or (bi-directed) correlations of any observed/unobserved variables neither in the “parents” set pa(i) nor in the “siblings” set sib(i). In other terms, a weak assumption excludes some values for a parameter (the null value zero), but permits a range of other values; while, strong assumptions assume that parameters take specific values (null value zero or a fixed *a priori* value). The linear equations and the covariance structure can be encoded and visualized in a “path diagram”, that is, a mixed graph *G* = (*V*,*E*) featuring both directed (→) and bi-directed (↔) edges. The vertex set *V* includes the genes and the edge set *E* represents relations or reactions among vertexes. The “activity” of a given gene is embedded in a path diagram: the actions performed by a gene on downstream molecules, and the signals that it receives from upstream regulators. “Directed edges” between two genes (j→i, if and only if j ∈ pa(i)), measured by path coefficients (ranging usually from -1 to 1, if genes are standardized), represent expected change in the activity of the downstream gene, given a unit of change in the upstream gene while the values of the other genes remain constant. Considering that paths reflect a direct influence of one gene on another, negative path coefficients indicate ensemble inhibition (negative control) and positive paths measure net activation (positive control). “Bi-directed edges” between two genes (j↔i if and only if j ∈ sib(i), or equivalently, if and only if i ∈ sib(j)) encode a hidden common cause that may be interpreted as latent or unobserved measurement of upstream regulators that could account for the observed covariances (correlations) between the two genes.

One important feature of SEM is that direct and indirect effects can be computed and compared. “Directed paths” between two genes are the sequence of all the directed edges (j → k_{1},… → …, k_{m} → i) from genes *Y*_{j} to *Y*_{i}. Each directed path is a channel along which information (gene’s activities) can flow, and so a “total effect” (TE) of gene *Y*_{j} on gene *Y*_{i} is defined as the total sum of the products of the sequence of arrows (edges) along all directed paths from *Y*_{j} to *Y*_{i}. Accordingly, a “indirect effect” (IE) of gene *Y*_{j} on gene *Y*_{i} represents the portion of the total effects not considering the directed edge effect (DE), i.e. TE = DE + IE.

The well-known SEM analysis consists of four steps [22]: a) definition and identification of an initial path model, b) estimation of parameters, c) evaluation of the fitting, and d) model modification.

### Initial model building

Specification of initial pathway models (step a) was obtained taking the perturbed pathways and converting them in directed graphs or gene networks. Generalizing, each pathway can be seen as a mixed graph. The idea is to understand how DEGs are connected in the perturbed pathways by other microarray genes. A natural way to solve this problem is to identify the shortest paths (geodesic distance) between DEGs. The geodesic distance d_{geo}(*y*_{i}, *y*_{j}) between two DEGs, *y*_{i} and *y*_{j}, is defined as the minimum distance between these two genes. The function *get.shortest.paths*( ) of the R package igraph was used to compute all the shortest paths [24]. Define the microarray genes, DEGs, and not DEGs in the following way: *MG =* {*mg*_{
1
}*, mg*_{
2
}*, …, mg*_{
m
}}; *DEG =* {*deg*_{
1
}*, deg*_{
2
}*,…, deg*_{
n
}} and *NDEG =* {*ndeg*_{
1
}*, ndeg*_{
2
}*,…,ndeg*_{
m-n
}}, where *MG = DEG* ∪ *NDEG* and *DEG* ∩ *NDEG =* {∅}. Each shortest path could be represented as a list of nodes Y_{
k
}*=* (*y*_{
i
}*, y*_{
i+1
}*,..., y*_{
j-1,
}*y*_{
j
}) and a list of the corresponding edges *E*_{k} = (e_{i(i+1)}, ..., e_{(j-1)j}) where (*y*_{
i
}*, y*_{
j
}) ∈ *DEG*; (*y*_{
i+1
}*,…, y*_{
j-1
}) ∈ (*DEG* ∨ *NDEG*); Y_{k} ⊆ Y and *E*_{k} ⊆ *E*. The shortest paths for each pathway constitute *k* (*k* = 1, …, *K*) subgraphs *G*_{k} = {*Y*_{k},*E*_{k}} of the original pathway, *G* = {*Y*, *E*}. Not all DEGs and NDEGs will be included in the shortest paths. Therefore, we define two new sets: *DEG*(*s*) and *NDEG*(*s*) respectively, the sets of DEGs and the set of not DEGs that include all genes in shortest paths, where *DEG*(*s*) ⊆ *DEG* and *NDEG*(*s*) ⊆ *NDEG.*

*NDEG*(

*s*) that connect

*DEG*(

*s*) are grouped in basis to their PIR superfamily (PIRSF). Based on the evolutionary relationships of whole proteins, this classification system allows annotation of both specific biological and generic biochemical functions. The PIRSF can be represented as

*SUPF =*{

*supf*

_{ 1 }

*, supf*

_{ 2 }

*,…, supf*

_{ g }}, where ∀

*supf*

_{ i }⊆

*NDEG*(

*s*) and ∀

_{i≠j}

*supf*

_{ i }∩

*supf*

_{ j }

*=*{∅}. Using this information, each original shorthest path

*G*

_{k}= {

*Y*

_{k},

*E*

_{k}} is transformed in a new shortest paths, G*

_{k}= {Y*

_{k}= (y*

_{i}, y*

_{i+1},…, y*

_{j-1,}y*

_{j}), E*

_{k}= (e*

_{i(i+1)},…, e*

_{(j-1)j}) }, where (y*

_{i+1},…, y*

_{j-1}) ∈ (

*DEG(s)*∨

*SUPF*∨

*NDEG(s)*) and E* ⊆ E. The function to obtain the final graph, G* = (Y*,E*), is described in the following pseudo-code:

The graph G* = (Y*,E*) is the fusion of all shortest paths found, where each node and each edge cannot be present more than once, the self-loops are not considered but the feed-backs and cycles were preserved. To ensure the identification of the initial models, the “block-recursive” criterion of Rigdon [25] and the “bow free” criterion of Brito and Pearl [26] were applied. The first affirms that reciprocal relationships, feedback loops, or covariances are segregated into groups, or *blocks*, with no more than two equations per block. The second affirms that a model is ensured if variables standing in direct causal relationships (directed edges) do not have correlated errors (bi-directed edges). So a new graph is attained in which the DEGs are connected by other DEGs, PIRSFs or NDEGs. In this way a model was created for each significant pathway found.

Successively, PIRSFs composite variables are defined considering not DEGs, present in shortest paths, as causal indicators of latent (hidden) constructs [27]. To generate the PIRSFs, a principal component analysis (PCA) was performed on genes belonging to a PIRSF and the principal component scores of the first principal component (PC1) were considered as the values that characterize the PIRSF. Only PIRSFs for which the PC1 represents 50% or more of the total variance are considered. At the end of process we have the initial SEM model.

The pathway graph conversion, the graph analysis, and the PC1 scores are obtained by graphite [28], igraph [24] and stats [29] R packages, respectively, while R functions for network analysis are implemented ad hoc, and are available Additional file 1.

### SEM fitting

*Y*=

*BY + U*, and Cov(

*U*) = Ψ. This specification induces a structure on the covariance matrix of the joint distribution of the genes Y as:

where *θ=*(*β*; *ψ*) is the list of the free parameters in the model of dimension *t.* The unknown parameters are estimated so that the implied covariance matrix Σ(*θ*) is close to the observed sample covariance matrix *S*.

_{0}= E(

*S*) to be the true population covariance matrix, and Σ(θ) the model-implied covariance matrix. The hypothesis to be tested is:

The chi-square test is then χ^{2} = -2logLRT = -2[logL(Σ(*θ*)) - logL(Σ_{0})] with *d* = *p*(*p* + 1)/2-*t* degree of freedom (d.f.). logL( ) represents the log-likelihood of the model, *p* the number of genes, *t* the number of parameters of the fitted model. Not-significant *P*-values (*P* >0.05) indicate that the model provides a good fit to the data. The *P*-values are derived by using the χ^{2}(*d*) distribution or a resampling bootstrap distribution [30].

*θ*

_{0}), which is approximately correct, is in the neighborhood of Σ

_{0}. So the null hypothesis of “exact fit” is replaced by the null hypothesis of “close fit”:

*ε*for the fitted model:

*P*-values for RMSEA are set up from the non-central χ^{2}(*λ*,*d*) distribution with non-centrality parameter, *λ* = (*n*-1) × *d* × 0.05^{2} or from a resampling bootstrap distribution. The null hypothesis of close fit is not rejected if *P* > 0.05.

*s*) values and the ones obtained from the model (σ) of the covariance matrix:

SRMR values <0.10 are assumed as an adequate fitting measure, whereas values <0.05 may be considered as a good fit [32].

Finally, the model refinement (step-d) is obtained adding new directed or bi-directed edges to the initial model. This modification was needed considering that the initial model is only a simplified representation of the whole pathway. The criteria used for the refinement are based on the combination of three elements. First, the modification indexes (MI), that is an estimate of the decrease in the χ^{2}-score statistic that would result by freeing each fixed (=0) parameter in the model; second, z-tests (=parameter estimate/standard error) of the MLE; and finally, biological evidences obtained by STRING database [33] and by the existence of a direct path between the nodes that MI proposes to connect. The following heuristic stepwise strategy was used:

**Heuristic stepwise procedure:**

Input: list of the fixed (=0) parameters (paths and/or covariances) in the model.

Output: new free parameters (paths and/or covariances) in the model.

- 1.
freeing just a single parameter (path coefficient or covariance) at a time, and these in turn are sorted in descending order of magnitude using MI;

- 2.
verify if the edge (path coefficient or covariance) to add is present in STRING or when the edge is a path coefficient, if it represents a direct path that connects the nodes in the pathway selected, and then add this new edge in the model;

- 3.
fit the model and if the new edge is statistically not significant (

*P*> 0.05, one-sided), using a z value (z<|1.64|), remove it and repeat step 1-2; - 4.
STOP the selection procedure if the model achieves a non significant LRT (

*P*> 0.05) or RMSEA (*P*of “close” fit > 0.05) or SRMR < 0.1, otherwise repeat step 1-3

### Multiple-group analysis

When data are observed from multiple subsamples, the representation of groups with “indicator variables”, considered as nodes, allows to recognize DEGs. Instead, “multiple-group analysis” allows to identify differentially regulated genes (DRGs) across groups.

_{1}(θ) and Σ

_{1}(θ) as the model-implied mean vector and covariance matrix of group 1 (experimental group) respectively and μ

_{2}(θ) and Σ

_{2}(θ) as the corresponding moments of group 2 (control group). For each models, two omnibus tests are performed considering the two experimental conditions (groups), one for the differential expression genes (nodes) and the other for the strength of the edges. In the first case, the hypothesis to be tested is:

In the “null” model (*H*_{0}), the mean or covariance estimates are constrained to be equal across groups; in the “alternative” model (*H*_{1}), they are allowed to differ across groups. The statistical significance is determined by comparison of LRT chi-square (χ^{2}diff) values at a given degree of freedom (d.f. diff). If there is a significant difference (*P* < 0.05) in the chi-squared goodness-of-fit index, the groups differ significantly for one or more specific gene expression (nodes) and/or gene-relationships (edges). Finally, three path-coefficient differences are screened: 1) “up/down” expression (gene nodes), testing the “zero value” for the group indicator variable (C = experimental =1, and C = control = 0) path coefficients; 2) “up/down” regulation (gene edges), testing the “zero value” for the differences of path coefficients across groups; 3) “on/off” regulation (gene edges) with respect to *a priori* KEGG gene regulation target, testing the “zero value” of the edge coefficients across groups.

*c*

_{ i }be an element of the matrix C. Let B

_{1}and B

_{2}to be the corresponding path coefficient matrices in the experimental and control groups; D = B

_{1}– B

_{2}and

*d*

_{ ij }be an element of the matrix D. We consider the test statistics:

where *SE*( ) is the estimated standard error of the parameters. The statistic *t*_{C} can be used to test the conditional “up/down” expression level difference of one gene between groups, given the parents of the gene in the network. Similarly, *t*_{
D
} checks the conditional “up/down” expression regulatory differences of one gene on another between groups. Moreover, *t*_{1} and *t*_{2} check the “on/off” regulatory differences compared to a priori KEGG pathway. The *P*-values of these statistics (two-sided, for *t*_{C} and *t*_{D} and one-sided, for *t*_{1} and *t*_{2}) are derived either asymptotically from the *N*(0,1)-distribution or empirically from the nonparametric-based or using model-based bootstrap distribution with B bootstrap samples (usually, B = 100, or 1000).

Note that the marginal bivariate test of DEGs with SAM approach can be regarded as the special case of the conditional test with *t*_{C,} when the pathway graph is G = (Y, ∅), so pa(y) = ∅ for all genes ∈Y.

We use the implementation provided by the lavaan [34] R package for estimation, evaluation, and modification of SEM data analysis, and R codes is available in Additional file 1.

## Results

The above described method was applied to two gene expression microarrays datasets, one from a study on FTLD-U and the other on MS.

### FTLD-U analysis

**Perturbed pathways obtained by SPIA on FTLD-U data**

Name pathway | pSize | NDE | pNDE | tA | pPERT | pGFdr | Status |
---|---|---|---|---|---|---|---|

Glutamatergic synapse | 77 | 11 | 0.000 | -6.557 | 0.064 | 0.006 | Inhibited |

GABAergic synapse | 60 | 10 | 0.000 | 0.632 | 0.804 | 0.017 | Activated |

Calcium signaling pathway | 166 | 17 | 0.000 | 0.072 | 0.993 | 0.021 | Activated |

Amphetamine addiction | 55 | 8 | 0.001 | -2.685 | 0.457 | 0.047 | Inhibited |

Gap junction | 85 | 10 | 0.001 | 5.216 | 0.454 | 0.047 | Activated |

MAPK signaling pathway | 235 | 18 | 0.001 | -5.802 | 0.253 | 0.047 | Inhibited |

ECM-receptor interaction | 82 | 7 | 0.022 | 6.150 | 0.015 | 0.047 | Activated |

The most of the dysregulated pathways, as the MPAK signalling pathway, the calcium signalling pathway, the gap junction and the ECM-receptor interaction, confirm the analysis of [35]. The dysregulated pathways with a significant p-PERT were the glutamatergic synapse and GABAergic synapse. The role of the glutamate in the acute and neurodegenerative processes were well described in literature [35–38]. Meldrum [36] illustrated three different pathological mechanisms of action of the glutamate in the neurodegeneration. Glutamate can be neurotoxic through an agonist effect on the N-methyl-D-aspartate (NMDA), α-amino-hydroxy-5-methyl-4-isoaxaleproprionicacid (AMPA), kainate or Group I metabotropic receptors. The relative contribution of these different classes of receptor*s* vary according to the neurons involved and a variety of other circumstances. Selective neuronal death subsequent to the epileptics status appears to be highly dependent on NMDA receptor activation. Acute neuronal degeneration after transient global or focal cerebral ischemia seems to be dependent on both NMDA and AMPA receptors. Regarding the GABAergic pathway, a loss of glutamatergic pyramidal cells and calbindin-D28k-immunoreactive GABAergic neurons in the frontal and temporal cortices of patients with FTLD [39] and FTLD with motor neuron disease [40] was reported.

### SEM analysis of glutamatergic synapse KEGG pathway

^{2}(df) = 600.1 (25),

*P*< 0.001, RMSEA (P-close) =0.320 (<0.001), SRMR = 0.450). This is likely because the existing pathway databases do not even contain all pathway information presented in the public literature [41], and because the model generated was a simplification of the real connections between nodes. Twenty-two directed edges were added, six using STRING database and sixteen using graph information. The final pathway model was an adequate approximation of the observed covariance matrix, as demonstrated by the SRMR index (0.092). Two-group analysis of the final pathway model revealed a significant global mean (χ

^{2}diff(df) = 48.5 (19),

*P*< 0.001 of H

_{0}: μ

_{1}= μ

_{2}subject to Σ

_{1}= Σ

_{2}) and covariance differences (χ

^{2}diff (df) = 110.9 (51),

*P*< 0.001 of H

_{0}: Σ

_{1}= Σ

_{2}subject to μ

_{1}≠ μ

_{2}). The specific tests that consider the effect of the progranulin mutation on every gene and every edge are summarized in Tables 2 and 3.

**Single node and edge differences found between FTLD-U with progranulin mutation and control groups**

Path | Type | FTLD-U Progranulin (P) | Control (C) | Difference (95% CI) | P-value | up/down |
---|---|---|---|---|---|---|

1742 ← group | Group on node | -6.024 | -5.540 | -0.484(-0.87; -0.10) | 0.014 | P down-expressed |

5532 ← group | Group on node | 9.606 | 10.706 | -1.101 (-1.64; -0.56) | 0.000 | P down-expressed |

2785 ← group | Group on node | 8.453 | 9.734 | -1.281 (-1.95; -0.62) | 0.000 | P down-expressed |

5534 ← group | Group on node | 7.233 | 8.308 | -1.075 (-1.65; -0.51) | 0.000 | P down-expressed |

gtp_bind ← group | Group on node | 2.374 | 2.626 | -0.252 (-0.44; -0.06) | 0.000 | P down-expressed |

2911 < - > 9454 | Binding/association | 0.178 | 0.813 | -0.635 (-1.27; -0.00) | 0.049 | P down-regulated |

3708 < -9456 | Binding/association | 0.457 | -0.205 | 0.662 (0.34; 0.98) | 0.000 | P up-regulated |

ade_cycl < -gtp_bind | activation | -2.044 | -0.840 | -1.204 (-2.28; -0.13) | 0.028 | P down-regulated |

5613 < -107 | Indirect | -0.012 | -0.324 | 0.312 (0.02; 0.60) | 0.034 | P up-regulated |

5613 < -ade_cycl | Indirect | 0.446 | -0.413 | 0.858(0.43; 1.20) | 0.000 | P up-regulated |

5579 < -plc_b | Indirect | 3.677 | 1.110 | 2.567(0.86; 4.28) | 0.003 | P up-regulated |

22941 < -1742 | String | 1.306 | 0.570 | 0.736(0.12; 1.35) | 0.002 | P up-regulated |

plc_b < -9229 | Directed path | 0.262 | 0.558 | -0.297(-0.56; -0.03) | 0.028 | P down-regulated |

ade_cycl < -5532 | Directed path | -0.463 | 0.208 | -0.671(-1.16; -0.18) | 0.007 | P down-regulated |

ade_cycl < -5534 | Directed path | -0.713 | -0.168 | -0.545(-1.05 -0.05) | 0.032 | P down-regulated |

5613 < -22941 | Directed path | -0.581 | -0.039 | -0.542(-0.95; -0.13) | 0.010 | P down-regulated |

22941 < -glutam_recp | Directed path | 0.044 | -0.278 | 0.322(0.09; 0.55) | 0.006 | P up-regulated |

9455 < -glutam_recp | Directed path | -0.128 | -0.462 | 0.334(0.04; 0.92) | 0.024 | P up-regulated |

**Not significant (null edge) in FTLD-U with progranulin mutation and in control groups**

Progranulin (P) | Control (C) | |||||
---|---|---|---|---|---|---|

Paths | Type | Estimate | P-value | Estimate | P-value | P/C |

2911 < -5534 | Activation | -0.024 | 0.238 | 0.232 | 0.927 | OFF/OFF |

gtp_bind < -2911 | Activation | -0.049 | 0.167 | -0.138 | 0.500 | OFF/OFF |

1742 < -glutam_recp | Binding/association | -0.192 | 0.008 | -0.376 | 0.200 | ON/OFF |

50944 < -9229 | Binding/association | 0.131 | 0.138 | 0.160 | 0.282 | OFF/OFF |

22941 < -9229 | Binding/association | -0.245 | 0.811 | -0.034 | 0.276 | OFF/OFF |

9456 < -22941 | Binding/association | -0.114 | 0.632 | 0.130 | 0.553 | OFF/OFF |

9456 < -50944 | Binding/association | 0.742 | 0.133 | 0.931 | 0.098 | OFF/OFF |

9455 < -22941 | Binding/association | 0.142 | 0.224 | -0.407 | 0.042 | OFF/ON |

9454 < -50944 | Binding/association | 0.519 | 0.466 | 0.226 | 0.358 | OFF/OFF |

3708 < -9455 | Binding/association | -0.322 | 0.067 | -0.137 | 0.432 | OFF/OFF |

3708 < -9454 | Binding/association | 0.462 | 0.055 | 0.216 | 0.001 | OFF/ON |

107 < -gtp_bind | Activation | 0.198 | 0.525 | 0.136 | 0.743 | OFF/OFF |

plc_b < -gtp_bind | Activation | -0.275 | 0.225 | -0.234 | 0.127 | OFF/OFF |

5613 < -107 | Indirect | -0.012 | 0.000 | -0.324 | 0.926 | ON/OFF |

1742 < -50944 | String | 0.971 | 0.013 | 1.166 | 0.062 | ON/OFF |

3708 < -2911 | String | -0.068 | 0.054 | 0.143 | 0.501 | OFF/OFF |

9229 < -50944 | String | -0.490 | 0.116 | -1.755 | 0.502 | OFF/OFF |

ade_cycl < -5532 | Directed path | -0.463 | 0.169 | 0.208 | 0.022 | OFF/ON |

ade_cycl < -5534 | Directed path | -0.713 | 0.107 | -0.168 | 0.003 | OFF/ON |

glutam_recp < -5532 | Directed path | -0.531 | 0.000 | -1.017 | 0.061 | ON/OFF |

5579 < -9456 | Directed path | 0.228 | 0.000 | 0.654 | 0.470 | ON/OFF |

5579 < -5534 | Directed path | 0.295 | 0.000 | 0.986 | 0.388 | ON/OFF |

5613 < -22941 | Directed path | -0.581 | 0.741 | -0.039 | 0.001 | OFF/ON |

22941 < -glutam_recp | Directed path | 0.044 | 0.000 | -0.278 | 0.696 | ON/OFF |

3708 < -22941 | Directed path | 0.264 | 0.000 | 0.443 | 0.072 | ON/OFF |

1742 < -9229 | String | -0.626 | 0.027 | -1.399 | 0.126 | ON/OFF |

Four genes and one PIRSF of the glutamatergic model resulted influenced by the group: genes 1742 or PSD-95, 5532 or PPP3CB, 2785 or GNG3, 5534 or Ppp3r1 and the PIRSF of the glutamate receptor. An important role could be played by PSD-95 gene that is believed to be involved in the synapse maturation, in the induction of a network of neurotransmitter receptors, scaffolding proteins and ionotropic glutamate receptors [42]. The gene PPP3CB and the gene GNG3 are associated to the Wnt signalling correlated to the dysregulation in the case of progranulin deficiency [43]. To note that four of the nodes influenced by the group were involved in the perturbed edges described in the Table 1. Giving a look to the significant edges found, the relationships 22941 <-1742 and 22941 <-PIRSF “glutamate receptors” are well note in the literature. In fact, the gene 229141 or SHANK2 plays a critical role both in the integration of the various postsynaptic membrane proteins, cell-adhesion molecules, signal components, scaffolding proteins, and actin-based cytoskeleton, part of the PSD protein network (activated by the PSD-95) [44], and in the organization of the glutamate receptors [45]. The edges 3708 <-9456 that involves the gene Itpr1 and the gene HOMER1 were very interesting. The relationships are involved in the spinocerebellar ataxia in human as described by [46]. Also other links that include the PIRSF adenylate cyclase, GTP-binding regulatory protein and the PLC-beta could be useful to interpret the role of mutation in the progranulin gene group.

### Multiple sclerosis

**Perturbed pathways obtained by SPIA on MS data**

Name pathway | pSize | NDE | pNDE | tA | pPERT | pGFdr | Status |
---|---|---|---|---|---|---|---|

B cell receptor signaling pathway | 73 | 8 | 0.000 | 6.822 | 0.372 | 0.000 | Activated |

Fc gamma R-mediated phagocytosis | 89 | 4 | 0.015 | -13.66 | 0.006 | 0.036 | Inhibited |

Salmonella infection | 71 | 5 | 0.001 | -4.901 | 0.174 | 0.041 | Inhibited |

The first, B cell receptor (BCR) signaling pathway, is an important component of adaptive immunity. B cells produce and secrete millions of different antibody molecules, each of which recognizes a different antigen. This signalling ultimately results in the expression of immediate early genes that further activate the expression of other genes involved in B cell proliferation, differentiation and immunoglobulin (Ig) production as well as other processes. The role of B cells is well known in MS [47, 48]. The second one, Fc gamma R-mediated phagocytosis pathway, includes specialized cell types as macrophages, neutrophils, and monocytes that take part in host-defence mechanisms. Expression of the inhibitory Fc gamma receptor IIB (FcγRIIB) plays an important role during peripheral B cell development, which prevents memory B cells with low affinity or self-reactive receptors from entering the germinal center and becoming IgG positive plasma cells [49]. Furthermore, the decreased expression of Fc gamma RIIB or non-functional Fc gamma RIIB variants are consistently associated with the development of autoimmune tissue inflammation [49]–[51]. Considering the connection between the Fc gamma R-mediated phagocytosis and B cells, and the association of this pathway with autoimmune inflammation, we could conclude that also this pathway could be implicated in the MS phenotype.

Salmonella infection, the third pathway, may appear less interesting, nevertheless it is connected with response to infections that, as showed for the two previous pathways, plays an important role in MS.

### SEM analysis of Fc gamma R-mediated phagocytosis KEGG

As an example, we analyzed the Fc gamma R-mediated phagocytosis pathway. The model was obtained starting from the KEGG pathway and finding the shortest paths between DEGs (no PIRSF reduction was performed). The graph reduction of the model specification was from KEGG pathway (mean degree = 10.404, number nodes(edges) = 94 (489)) to DEGs shortest paths model (mean degree = 3.647, number nodes(edges) = 17(31)). The initial model had a poor fit (χ^{2} (df) = 339.410 (105), *P* < 0.001, RMSEA (*P*-close) =0.288 (<0.001), SRMR = 0.321) and the final model was adequate considering the SRMR index (0.098) and RMSEA (*P*-close) 0.111 (0.059). To reach the final model, twenty edges were added: fifteen using as driving criteria the presence of a directed path between the nodes in the original pathway, and five using STRING database information.

Two-group analysis of the final pathway-model revealed a significant global mean (χ^{2} diff(df) = 40.3 (16), *P* < 0.001 of H_{0}: μ_{1} = μ_{2} subject to Σ_{1} = Σ_{2}) and covariance differences (χ^{2} diff (df) = 124.4 (48), *P* < 0.001 of H_{0}: Σ_{1} = Σ_{2} subject to μ_{1}≠ μ_{2}).

**Single node and edge differences found between MS and control groups**

Path | Type | Multiple sclerosis ( MS ) | Control (C) | Difference (95% CI) | P-value | Up/down |
---|---|---|---|---|---|---|

382 ← group | Group on node | -9.002 | -7.963 | -1.04 (-1.65; -0.43) | 0.001 | MS down-expressed |

10093 ← group | Group on node | -0.452 | 0.032 | -0.48 (-0.93; -0.04) | 0.033 | MS down-expressed |

5580 ← group | Group on node | -3.054 | -2.648 | -0.41 (-0.70; -0.11) | 0.007 | MS down-expressed |

5321 ← group | Group on node | 0.694 | 1.240 | -0.55 (-1.00;-0.09) | 0.020 | MS down-expressed |

1399 ← group | Group on node | 4.688 | 6.126 | -1.44 (-1.82;-1.05) | 0.000 | MS down-expressed |

8613 | Indirect | 0.001 | -0.002 | 0.003 (0.001;0.004) | 0.038 | MS up-associated |

23396 ← 8613 | Indirect | 0.643 | -4.981 | 5.62 (2.44; 8.81) | 0.001 | MS up-regulated |

23396 ← 5880 | Activation | 0.079 | 1.207 | -1.13 (-1.91;-0.35) | 0.005 | MS down-regulated |

23396 ← 8612 | Indirect | -6.219 | -0.945 | -5.27 (-6.62; -3.92) | 0.000 | MS down-regulated |

10093 ← 8936 | Activation | 0.576 | 0.267 | 0.31 (0.08; 0.53) | 0.007 | MS up-regulated |

5581 ← 8613 | Indirect | 0.002 | 0.953 | -0.95 (-1.41; -0.49) | 0.000 | MS down-regulated |

5581 ← 8612 | Indirect | 1.343 | 0.104 | 1.24 (0.78; 1.70) | 0.000 | MS up-regulated |

382 ← 1399 | Directed path | 0.970 | 0.144 | 0.83 (0.19; 1.46) | 0.011 | MS up-regulated |

7454 ← 5338 | Directed path | 1.070 | 0.111 | 0.96 (0.28; 1.64) | 0.006 | MS up-regulated |

5604 ← 8613 | Directed path | 0.618 | 3.133 | -2.52 (-4.64; -0.39) | 0.020 | MS down-regulated |

23396 ← 1399 | Directed path | -0.323 | -0.755 | 0.43 (0.24; 0.62) | 0.000 | MS up-regulated |

**Not significant (null edge) in MS and in control groups**

Multiple sclerosis (MS) | Control (C) | |||||
---|---|---|---|---|---|---|

Paths | Type | Estimate | P-value | Estimate | P-value | MS/C |

23396 ← 382 | Activation | 0.114 | 0.657 | 0.082 | 0.324 | OFF/OFF |

10093 ← 7454 | Activation | 0.419 | 0.199 | 0.065 | 0.726 | OFF/OFF |

5580 ← 8612 | Indirect | 0.326 | 0.388 | 5.119 | 0.144 | OFF/OFF |

5894 ← 5580 | Activation | 0.338 | 0.207 | -0.103 | 0.657 | OFF/OFF |

5894 ← 5581 | Activation | 1.399 | 0.436 | -2.815 | 0.349 | OFF/OFF |

5595 ← 5604 | Phoshorylation | -0.107 | 0.808 | -0.963 | 0.408 | OFF/OFF |

1794 ← 1399 | Binding/association | 0.056 | 0.399 | -0.007 | 0.747 | OFF/OFF |

8936 ← 5880 | Activation | 0.015 | 0.972 | -0.410 | 0.240 | OFF/OFF |

7454 ← 382 | Directed path | 0.057 | 0.753 | -0.076 | 0.696 | OFF/OFF |

5604 ← 5338 | Directed path | -0.024 | 0.810 | -0.170 | 0.454 | OFF/OFF |

5595 ← 5338 | Directed path | 0.124 | 0.404 | 0.590 | 0.065 | OFF/OFF |

5338 ← 382 | Activation | 0.164 | 0.612 | 0.216 | 0.007 | OFF/ON |

5580 ← 8613 | Indirect | 3.021 | 0.133 | -0.819 | 0.019 | OFF/ON |

5604 ← 5894 | Activation | 0.160 | 0.183 | 0.273 | 0.041 | OFF/ON |

5321 ← 5595 | Activation | 0.356 | 0.535 | -0.285 | 0.052 | OFF/ON |

382 ← 5880 | Activation | 0.302 | 0.455 | 1.314 | 0.002 | OFF/ON |

5580 ← 382 | Directed path | -0.073 | 0.688 | 0.156 | 0.005 | OFF/ON |

382 ← 1399 | Directed path | 0.144 | 0.414 | 0.970 | 0.001 | OFF/ON |

7454 ← 5338 | Directed path | 0.111 | 0.358 | 1.070 | 0.002 | OFF/ON |

10093 ← 382 | Directed path | 0.269 | 0.252 | 0.498 | 0.001 | OFF/ON |

7454 ← 5880 | String evidence | 0.109 | 0.621 | 0.864 | 0.077 | OFF/OFF |

5581 ← 5595 | String evidence | 0.001 | 0.978 | 0.049 | 0.001 | OFF/ON |

23396 ← 5880 | Activation | 1.207 | 0.000 | 0.079 | 0.708 | ON/OFF |

5581 ← 8613 | Indirect | 0.953 | 0.000 | 0.002 | 0.921 | ON/OFF |

10093 ← 5880 | Directed path | 1.044 | 0.000 | 0.365 | 0.397 | ON/OFF |

8613 ← 1399 | Directed path | -0.024 | 0.027 | -0.093 | 0.225 | ON/OFF |

8936 ← 1794 | Directed path | 1.819 | 0.008 | -1.627 | 0.608 | ON/OFF |

5580 ← 5338 | String evidence | 0.417 | 0.004 | 0.258 | 0.222 | ON/OFF |

## Discussion

This work illustrated a new procedure based on Structural Equation Modeling (SEM) to discover and compare perturbed pathway-modules, similar to that proposed by [12]. Unlike these authors, new pathway analysis (SPIA) and new model generation, based on mixed graph theory and principles of network biology, were added. Starting from the significant dysregulated pathways, a model for each pathway was generated, that allowed both to verify how DEGs were connected between them in a causal structure and to improve the model interpretation by grouping not DEGs in PIR superfamilies. To note that the initial model is not inferred by data, because connections among genes represent the biological knowledge enclosed in the pathway and in PIRSFs. The use of SEM proved to be very versatile in the downstream analysis of microarray data. It was used to: 1) test and improve the generated models (this could be a useful way to overcome the limitation of accuracy relative to the public pathway database); 2) verify the overall differences between groups, and 3) individuate differential expression level of one gene and differential connection between two genes.

Our procedure was tested on two experiments of gene expression microarray data finalized to unravel the biological mechanisms that allowed us to explain the differences between yes/no mutant groups and disease/healthy groups. Starting from the output of SPIA, the model generation procedure, illustrated in this paper, was applied. The first step was to obtain a subgraph for each perturbed pathway containing only the genes present in the microarray. Then, the connections between DEGs through shortest paths were found, and finally not DEGs were grouped in PIRSFs. The models generated were tested and improved using an integrate approach based on the combination between SEM and other type of evidences as explicated in the STRING database. Once a good model has been determined, a two-groups SEM analysis has been performed to unveil significant differences between groups. Studying each link present in the final perturbed model, we hypothesized which connections could be altered. For example in FTLD-U experiment, we found the connection between SHANK2 and PSD-95, and in MS we hypothesized that the genes IL-1b, ARF6 and CRKL could be involved in a same pathological mechanism. These results confirmed and could be able to elucidate the mechanisms that lead to the pathogenesis and progression of the observed diseases.

The framework illustrated, being a composition of different methods, could be easily adapted to new solutions. The idea is to have a general and modular framework where different other methods could be taken in consideration in each step of the pipeline.

First, alternative ways to select DEGs and perturbed pathways could be considered. In [59] a unified framework was proposed to jointly find significant perturbed pathways and DEGs by sparse Linear Discriminant Analysis (sLDA). Other methods for identifying DEGs and differential connections, based on Graphical Gaussian Modeling (GGM), are the following: a first one generates networks directly from very high dimensional data, determining the pattern of zeros in the inverse covariance matrix [60]; a second one defines Bayesian networks (directed acyclic graph: DAGs) on a structure of dependence derived from external resources [61]; the last one derives DAGs from external resources converting them into undirected cycle-free graphs [62]. An additional approach for conducting a differential analysis of networks directly from data by measuring gene association/interaction with connectivity scores, based on Partial Least Squares (PLS), was suggested by [63].

Second, we used PCA to create new observed composite variables that represent PIRSFs. Another plan could be to take advantage of the potentiality of SEM in the creation of latent variables as proposed by [64] in transcriptional regulation of protein-DNA interactions. A valid alternative to the modification indices of SEM could be the PC-algorithm [65], that allow to infer causal information from data. The idea in this case is to fix an initial model, provided by the perturbed pathways model, and then use the PC-algorithm to add new links supervised by STRING knowledge. Lastly, since in SEM multigroup analysis numerous hypothesis are tested, multiple testing control procedures using the method of [66] can be desirable.

## Conclusions

The pipeline proposed introduces in the analysis of gene expression data the main principles that govern biological networks as well described by [14]. Otherwise from reverse-engineering gene regulatory networks, that build networks directly from data, our initial models are obtained by a biological curated pathway database (KEGG) and then modified on the basis of the knowledge provided by another database (STRING). The principal evidence is that the manifestation of a particular phenotype depends on the interactions existing among many causal agents. This was obtained taking in consideration not only DEGs but also how genes interact. The pipeline has been validated on two expression datasets. In both the cases, we tested the models, improved them and individuated the gene expression levels and the connections that were perturbed and that could justify the different phenotypes observed. The results were satisfactory and strongly coherent with experimental findings available in literature, considering that most of the genes in the model are known to characterize the phenomena analyzed and that links perturbed were previously connected to the progression of the diseases.

## Notes

## Declarations

### Acknowledgements

This work was funded by Laboratory of Multivariate Statistics, Department of Brain and Behavioural Sciences, Medical and Genomic Statistics Unit, University of Pavia, Pavia- Italy. We want to thank, the PhD student Fernando Palluzzi and the PhD Valeria Carossa for their advice and helping.

## Authors’ Affiliations

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