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Supporting Information For
Data mining reveals a network of early-response genes as a consensus
signature of drug-induced in vitro and in vivo toxicity
Jitao David Zhanga,1, Nikolaos Berntenisa, Adrian Rotha and Martin Ebelinga,1
a
Non-Clinical Safety, F. Hoffmann-La Roche AG, Grenzacherstrasse 124, 4070 Basel, Switzerland
1
Corresponding author: Jitao David Zhang ([email protected]) or Martin Ebeling
([email protected]), F. Hoffmann-La Roche AG, Grenzacherstrasse 124, 4070 Basel,
Switzerland.
Contents
Supplementary Methods ........................................................................................................................ 1
Identify early gene signatures with hierarchical linear models .......................................................... 1
SVM performance measures .............................................................................................................. 2
Temporal expected node occupancy fractions from Boolean network ensembles ........................... 2
Supplementary Tables ............................................................................................................................ 2
Supplementary Figure Legends............................................................................................................... 3
Supplementary Methods
Identify early gene signatures with hierarchical linear models
As a starting point we built a simple linear model for regression analysis for differential gene
expression only with regard to time, written as
where
denotes differential gene expression,
denotes the intercept, and
denotes the
coefficient of time points t (2, 8, or 24 hours). This model, however, does not include the
information of treatments that induced progressive profiles. Therefore we next built a two-level
model with varying intercepts and slopes to capture commonly regulated genes in progressive
profiles. The model can be represented as
implying that intercept and slope of the linear model can vary by treatments. Early gene signatures
were defined by the genes of with logFC (log2 fold change) at 2h equal to or larger than 0.5 (or equal
to or smaller than -0.5), and with an associated p value (derived from the t-distribution followed by
,
, and the number of conditions C) smaller than 0.001.
SVM performance measures
With TP, FP, FN denoting counts of true positive, false positive, and false negative cases, the
measures to evaluate performance are defined as:
Temporal expected node occupancy fractions from Boolean network ensembles
In Boolean networks, activators and inhibitors in a biological system are represented as activating
and inhibiting nodes in a network. All nodes can be in only one of two states, ON or OFF, simplifying
the typically sigmoidal stimulus-response relationship to a step function. In any specific network
state, the values of some nodes may be subject to change, according to well-specified rules
capturing the biology of the system, giving rise to a successor state. If it so happens that no node is
subject to change, the system is in a fixed-state, i.e., it has reached an equilibrium point.
Most studies focus on a single network, looking for possible fixed network states (i.e., possible
phenotypes) and then probing fixed state stability under various perturbations. However, Boolean
network ensembles can successfully generate semi-quantitative time-response information. We
developed an algorithm that generates time-response profiles of expected node occupancy fractions
from such an ensemble. In this approach, each ensemble member follows one of the possible time
evolution scenarios. Starting from an initial state the ensemble goes through successive iteration
steps and at each step all possible successor states of all ensemble members are assigned a weight
that is a measure of their likelihood to be occupied. A state-network is thus generated that
encompasses the discrete-time evolution of the ensemble. Each path within it corresponds to an
ensemble member. The expected occupancy fraction of a node at a given time step is the sum of the
weights (at that step) of the ensemble members for which the node is ON. The manuscript
describing the algorithm is under review.
Supplementary Tables
Supplementary Table 1 (Attached XLS file): Compounds and natural molecules that were tested in
TG-GATEs: names, ATC codes, and TG-GATEs download links organized by species, experimental
systems, and dosing paradigms.
Supplementary Table 2: Pathological terms used as prediction targets of Support Vector Machines.
TG-GATEs provides pathological records of in vivo experiments, which we used to test the predictive
power of the early-response network. If any of the pathological changes listed in the following table
was observed in a sample, its pathological state was set as TRUE. Otherwise the state was set as
FALSE.
Liver
Kidney
Anisonucleosis
Anisonucleosis
Cellular infiltration
Cellular infiltration
Change, basophilic
Change, basophilic
Degeneration
Degeneration
Fibrosis
Dilatation
Increased mitosis
Eosinophilic body
Proliferation, bile duct Fibrosis
Single cell necrosis
Hyperplasia
Hypertrophy
Necrosis
Proliferation
Supplementary Figure Legends
Figure 1: There are fewer early-response genes but they are more generic than late-response genes
in rat hepatocytes. The legends follow the definition in the caption of Figure 2 of the manuscript. (A)
Number of differentially expressed genes (DEGs) induced by compounds in rat primary hepatocytes,
stratified by dose and time. (B) The normalized generality score of DEGs, stratified by dose and time.
We observed that the normalized generality scores of 2h DEGs in rat are generally lower than the
scores of their counterparts in human. On average, however, they still tend to be higher than for the
24h DEGs.
Figure 2: Cytotoxicity matrices of TG-GATEs compounds that were tested in human primary
hepatocytes.
Figure 3: Expression patterns of EGR1, ATF3, GDF15, and FGF21 in (A) human and (B) rat primary
hepatocytes where treatments did not cause any moderate or even strong cytotoxicity. Bold lines
indicate average changes of gene expression, and error bars indicate standard errors of the mean.
The line representing GDF15 is overwritten by the line representing FGF21 in both panels.
Figure 4: Unsupervised clustering of differential expression profiles in rat primary hepatocytes
revealed four groups (separated by black lines). Note that the NGTX group (indicated by the black
arrow) contrasts strongly with other profiles. Columns of the matrix are frequently induced genes,
which were induced in more than five percent of all samples.
Figure 5: Cytotoxicity matrices of TG-GATEs compounds that were tested in rat primary hepatocytes.
Figure 6: Identifying early-response signatures in rat primary hepatocytes. (A) Amino-acid (AA)
sequence identity and similarity between orthologous proteins in human and rat. (B) Cxcl2, the rat
ortholog of human CXCL3, which shares 47.8% protein sequence similarity with human IL8, is an
early-response gene in rat. (D) Temporal profiles of Tob2, the rat ortholog of human TOB2. Its
dynamics are highly conserved in both species; however its induction at 2 hours did not meet the
predefined significance level (see supplementary methods).
Figure 7: Robustness of the early-response network assessed by edge permutations. In each
permutation step, one edge of the network is deleted, and dynamics of the permutated network is
simulated with the two initial states defined in the main text. Each panel in the figure represents one
permutation with one initial state. For visualization purposes random noise was added to the
simulation results.