=Paper=
{{Paper
|id=None
|storemode=property
|title=MicroRNAs Robustness in Genetic Regulatory Networks
|pdfUrl=https://ceur-ws.org/Vol-975/paper-02.pdf
|volume=Vol-975
|dblpUrl=https://dblp.org/rec/conf/ilp/DoncescuID12
}}
==MicroRNAs Robustness in Genetic Regulatory Networks==
https://ceur-ws.org/Vol-975/paper-02.pdf
MicroRNAs Analysis by Hypothesis Finding
Technics
Andrei Doncescu1,2 , Katsumi Inoue1 , and Anne Pradine2
1
National Institute of Informatics Japan
2
Cancer Institute of Toulouse, France
Abstract. The cell is an entity composed of several thousand types of
interacting proteins. Our goal is to comprehend the cancer regulation
mechanisms using the microRNAs. MicroRNAs are present in almost all
genetic regulatory networks acting as inhibitors targeting mRNAs. In
this paper, it is shown how the Artificial Intelligence description method
functioning on the basis of Inductive Logic Programming can be used
successfully to describe essential aspects of cancer mechanisms. The re-
sults obtained show new microRNAs markers for melanoma metastasis.
1 Introduction
New technologies have been developed developed to measure the expression level
of thousands of genes simultaneously. These genomic-scale snapshots of gene ex-
pression (i.e. how much each gene is ”turned on”) are creating a revolution in in
biology. Genes encode proteins, some of which in turn regulate other genes. Un-
derstanding genetic and metabolic networks is of the utmost importance. These
networks control essential cellular processes and the production of important
metabolites in microorganisms, and modeling such networks from model organ-
isms will drive applications to other less characterized organisms, which have a
high biotechnological potential.
The study of signaling events appears to be a key to the research, biologi-
cal, pharmacological and medical. The spread of these types of signals are not
changing the behavior of proteins on three levels: regulation of the activity, inter-
action and expression. The three levels are synchronized in a strong momentum
that leads to changes in protein activity. Since a decade signaling networks have
been studied using analytical methods based on the recognition of proteins by
specific antibodies. Parallel DNA chips (microarrays) are widely used to study
the co-expression of candidate genes to explain the etiology of certain diseases,
including cancer.
From the standpoint of Artificial Intelligence cells are sources of information
that include a myriad of intra and extra cellular signals that as the ultimate
goal of optimal output describing metabolic proteins. Diseases and cancer in
particular can be seen as a pathological alteration in the signaling networks of
the cell.
The study of gene networks poses problems identified and studied in Artifi-
cial Intelligence over the last twenty years. It is clear identified that the reason-
ing have to handle incomplete, uncertain, revisable, contradictory and multiple
sources of information. The logical representation of signaling pathways is not
complete: biological experiments provide a number of protein interactions but
certainly not all. On the other hand the conditions and difficulty of some experi-
ments lead to obtain data which are not always accurate. Some data may be very
wrong and must be corrected or revised in the future. Finally the information
coming from different sources and experiences and can be contradictory. From
this observation, generally for most real human activities, it must, however, rea-
son and make decisions. It is the goal of logics of uncertainty and in particular
of nonmonotonic logics.
The logical approach provides an intuitive method to provide explanations
based on the expressivity of relational language. For example, logic can rep-
resent biological networks such as gene regulation, signaling transduction, and
metabolic pathways. Unlike other approaches, this method lets us introduce
background theory, observations and hypotheses within a common declarative
language. It also provides the basis for the three main forms of inference, i.e.,
deduction (prediction), abduction (explanation) and induction (generalization).
In a quarter of a century of automatic demonstration, propositional calculus
has been largely neglected in favor of more substantial logics, such as predi-
cate calculus or certain kind of non-classical logic. In the case of propositional
calculus, the algorithms of automatic demonstration, be they of syntactical or
semantic nature, are characterized by their great simplicity. It is thus easy to
analyze their limits and weak point, and to work towards an improvement in
their performance.
Another fundamental asset of propositional calculus is its decidability : we
can implement algorithms which will give a result within a finite and reasonable
time, even for non-trivial examples. In first-order logic, such algorithms cannot
exist.
1.1 Causality and classical inference
If the inference of classical logic A → B or A a B is fully described formally,
with all the ”good” logic properties (tautology, not contradiction, transitivity,
contraposition, modus ponens, ...), a description of the properties of causality is
less simple. Causality can not be seen as a classical logic relation. In this paper,
it is described and use a very simple form of causality necessary and probably
sufficient for the application to the cell.
To provide the causal links between our relations cause and blocks in a clas-
sical language (propositional calculus or first order logic) it is necessary to do
two things :
1. Describe the internal characteristics of relations and causes
2. Describe the links between these relations and classical logic
Taking into account the aspect of discovery (abduction, field production) the
hypothesis theory is applied. The default logic raises a number of theoretical and
practical problems. To solve these problems we will use the Hypotheses Theory,
wich is a nonmonotonic logic. A fragment of this logic is close to stable model.
One advantage of hypotheses theory is that the information is described in a
classic bimodal logic. It will then be possible to use this formalism to describe
the set of all information, uncertain or not. This logic will also allow the use a
consequence finding algorithms to treat abduction.
2 Integrating induction and abduction in CF-induction
Both induction and abduction are ampliative reasoning, and agree with the logic
to seek hypotheses which account for given observations or examples. That is,
given a background theory B and observations (or positive examples) E, the
task of induction and abduction is common in finding a hypothesis H such that
B ∧ H |= E, (1)
where B∧H is consistent. There are several discussions on the difference between
abduction and induction in the philosophical and pragmatic levels. On the com-
putational side, induction usually involves generalization, while abduction gives
minimal explanations for individual observation.
Inverse entailment (IE) is a logically principled way to compute abductive
and inductive hypotheses H in (1) based on the logically equivalent transforma-
tion of the equation (1) to
B ∧ ¬E |= ¬H. (2)
The equation (2) says that, given B and E, any hypothesis H deductively
follows from B ∧ ¬E in its negated form. The equation (2) is seen in literature,
e.g., [12] for abduction and [15] for induction. The equation (2) is useful for
computing abductive explanations of observations in abduction. This is because,
without loss of generality, in abduction E is written as a ground atom, and each
H is usually assumed to be a conjunction of literals. These conditions make
abductive computation relatively easy, and consequence-finding algorithms [12]
can be directly applied.
In induction, however, E can be clauses and H is usually a general rule.
Universally quantified rules for H cannot be easily obtained from the negation
of consequences of B ∧ ¬E. Then, Muggleton [15] introduced a bridge formula
U between B ∧ ¬E and ¬H:
B ∧ ¬E |= U, U |= ¬H.
As such a bridge formula U , Muggleton considers the conjunction of all unit
clauses that are entailed by B ∧ ¬E. In this case, ¬U is a clause called the
bottom clause ⊥(B, E). A hypothesis H is then constructed by generalizing a
sub-clause of ⊥(B, E), i.e., H |= ⊥(B, E). This method with ⊥(B, E) is adopted
in Progol, but it has turned out that it is incomplete for finding hypotheses
satisfying (1).
In [13], Inoue proposed a simple, yet powerful method to handle inverse
entailment (2) for computing inductive hypotheses. The resulting method called
CF-induction does not restrict the bridge formula U as the set of literals entailed
by B∧¬E, but consider the characteristic clauses [12] of B∧¬E, which obviously
generalizes the method of the bottom clause. CF-induction then realizes sound
and complete hypothesis finding from full clausal theories, and not only definite
clauses but also non-Horn clauses and integrity constraints can be constructed
as H.
In most previous inductive methods including Progol [15], there are syntacti-
cal restrictions such that: (i) each constructed hypothesis in H is usually assumed
to be a single Horn clause, (ii) an example E is given as a single Horn clause,
and (iii) a background theory B is a set of Horn or definite clauses. From the
viewpoint of applications, these restrictions are due to the easiness for handling
such formulas. An extension to multiple non-Horn clauses in B, E, and H is,
however, useful in many applications.
First, an extension allowing multiple clauses in either a hypothesis H or an
observation E is essential in applications of abduction. In fact, recent work on
abductive inference in metabolic pathways [16] uses an independent abductive
procedure to obtain a set of literals that explain an observation. In general, there
are multiple missing data to account for an observation. This abductive inference
is independently computed in [16] not by Progol, and the inductive process for
generalization takes place by Progol only after abductive hypotheses have been
obtained. On the other hand, CF-induction can be used to compute abductive
explanations simply by taking the bridge formula U as a deduced clause. CF-
induction thus integrates induction and abduction from the viewpoint of inverse
entailment through consequence-finding [13].
Second, an extension to non-Horn clauses in representation of B, E, H is
also useful in many applications. For example, indefinite statements can be rep-
resented by disjunctions with more than one positive literals, and integrity con-
straints are usually represented as negative clauses. The clausal form is also
useful to represent causality. For example, when we want to represent inhibition
of reaction in a causal pathway network, positive and negative literals with the
predicate like inhibited can be used in the premise of each causal rule, which
results in a non-Horn clause (we will see an example afterwards). Again, the
inductive machinery of CF-induction can handle all such extended classes.
Third, introducing multiple, non-Horn clauses in a hypothesis H is an uni-
fying extension that combines the first and second extensions. In this case, a
hypothesis H forms a theory, which can also account for multiple observed data
at once.
In our application to cancer analysis, given the background theory of network
structures of a pathway and observations, we need a hypothesis H that explains
the behavior of the metabolic system. In principle, such a hypothesis consists
of multiple non-Horn clauses each of which represents a causal relation. CF-
induction is thus particularly useful for this type of applications.
Here the structure of representation is based on the specification of CF-
induction program, which is compatible with the consequence-finding program
SOLAR [19] and the TPTP format for theorem proving. SOLAR is a Java im-
plementation of the tableaux variant of SOL resolution [12].
For example, the input clauses can be described as
input_clause(axiom1, bg, [-p(X), -q(X), r(X)]).
input_clause(example1, obs, [r(a)]).
production_field([predicates(pos_all), length < 3]).
Here, axiom1 and example1 are ID names of clauses, and bg and obs repre-
sent background knowledge and observation, respectively. The axiom1 means
ep(x)∨eq(x) ∨ r(x). Each clause is represented as a list of literals. The predicate
production field indicates the production field of SOLAR, and this example
allows it to generate consequences consisting of less than 2 positive literals. In
this way, a production field can be used to specify an inductive bias in CF-
induction. There is other meta information to control deduction in SOLAR such
as the search strategy and the depth limit.In this case, CF-induction produces
the abductive hypothesis:
Hypotheses: [ [p(a)], [q(a)] ]
The current CF-induction program has several generalizers, which, given a set T
of clauses, produce a set S of clauses such that S |= T . These basic generalizers
include anti-instantiation, reverse Skolemization, Plotkin’s least generalization,
and dropping literals (see [13]).
Therefore, CF-induction is related to top-down decision tree learning algo-
rithm generating a set of rules in the form of predicate logic clauses which can
be used to separate the classes.
2.1 Signaling Pathway Representation
In this paper, it is used only a propositional representation. In practice the
detailed study of interactions will be asked to represent increases or decrease
some biological quantities which could be protein concentration. It therefore
falls outside the scope of propositional but the basic problems are the same. To
represent a change in concentration is for example possible by using predicates
such as ”increased” or ”decrease” [3].
To describe interactions between genes/proteins we use a language L of clas-
sical logic (propositional). The proposition A (resp. ¬A) says that A is true
(false). We are in a logical framework, so it is possible to represent almost ev-
erything in a natural way. Interactions between genes/proteins is a very simple
form of causality.
3 MicroRNAs
MicroRNAs are small RNA molecules that were discovered in the 1990s in ani-
mals and plants, and which play an essential role in controlling gene expression.
In human being, more than 500 microRNAs have been identified, and we now
know that their dysfunction is associated with several diseases, including can-
cer. The microRNAS play an important role of not specific inhibition in many
circumstances of the cell life, like chromatin clock control and have a big influ-
ence on many metabolic controls of functions like energy systems, cell cycle and
defense systems against pathogens.
MicroRNAs are present in almost all genetic regulatory networks acting as
inhibitors targeting mRNAs, by hybridizing at most one of their triplets, hence
acting as translation factors by preventing the protein elongation in the ribo-
some. MicroRNAs act as source nodes in the interaction graph of genetic regu-
latory networks, which are made of elements, the genes, in interaction through
the protein they express and control important cell or tissue functions like prolif-
eration, differentiation, energy systems maintenance, and more generally home-
ostasis [3,4].
3.1 MicroRNAs Identification in Melanoma
The microRNAs appear increasingly as crucial actors in oncogenesis (miR-21 in
breast cancer ) or as a tumor suppressor. Furthermore, the expression profiles of
microRNAs in solid tumors of different origins have been made using prognostic
values.
In 2008 the circulating microRNA were revealed for the first time in serum
and plasma, with very different profiles between healthy donors (which have a
similar expression profile) and patients with breast cancer, lung , prostate with
specific expression patterns. In addition, these microRNAs have a high stability
since they form complexes with lipids or lipoproteins, allowing the resistance to
the activity of RNase and DNase. They can also withstand harsh experimental
conditions such as high temperature or high pH variation. These data therefore
show the circulating microRNAs (noninvasive) could be novel plasma biomarkers
in oncology.
Melanoma is a cancer of the skin or mucous membranes, developed at the
expense of melanocytes. In most cases, it develops first on the skin but it is
common to find melanoma of the eye (choroidal melanoma), mucous membranes
(mouth, anal canal, vagina), or even more rarely internal organs. The incidence
of this disease is increasing worldwide. In France, it was evaluated in 2010 at
approximately 7-9 new cases per year per 100 000 individuals with a mortality
rate ranging from 1.2 to 1.5 individuals.
For this study, we collected 29 subjects having metastatic melanomas and
5 healthy volunteers. The micro-arrays are spotted with microRNA of human,
murine, and viral control. In all, there were 4608 different microRNA spots and
2000 different microRNA.
In order to highlight the existence of circulating microRNA biomarkers in
melanoma, a technique was developed for extracting microRNAs from the plasma
of healthy donors. This lets us collect a larger amount purified RNA from the
serum). The expression profile of microRNA was obtained using by a technique
of monochrome chip. These chips were spotted (labeled with fluorescent HY3
) with probes modified with bases LNA (Locked Nucleic Acid home Exiqon).
The goal was to optimize the specificity and sensitivity of probes (annealing
temperature adapted to detect microRNA with a low percentage of GC). Each
microRNA was represented by two different spots. These chips were analyzed
using image analysis software GenePix Pro 6.1.0.4 (Axon Instruments).
3.2 MicroRNAs expressed between 2 populations : healthy subjects
and patients with melanoma
Using Bayesian approach (Limma) [17] , the original human microRNA differ-
entially expressed between the two groups of subjects were identified (Fig.1 ).
Fig. 1. The different microRNAs expressed between the 2 populations (healthy subjects
and patients with melanoma).
4 Discretization of Continous Values
Discretizing time series is a research domain on its own and many works [6, 23]
have been conducted recently. Our practical problem is that we want to have a
statistically relevant (unsuppervised) discretization for N chemical compounds
concentrations over time. For that purpose, we compute an appropriate number
of levels (5) in regard to a Bayesian score such as Bayesian Information Criterion
[24].
We use continuous (Gaussian) hidden Markov models with parameter tying,
which means that each chemical compound has a corresponding HMM but all
the N Gaussian HMM share the same parameters (means and covariances),
to share the same discrete outputed levels between the different compounds of
one experiment. This relevant discretized levels of concentration are computed
through expectation maximisation with maximum a posteriori [3, 7] . The level
of microRNA expression are represented as : very low, low, medium, high, very
high
4.1 Analysis of microRNAs expressed in melanoma
The highly aggressive character of melanoma makes it an excellent model to
probe the mechanisms underlying metastasis, the process by which cancer cells
travel from the primary tumor to distant sites in the body. Therefore, the goal
of this analysis is to find out a microRNA signature in the case of aggressive
melanoma. This signature is represented by a logical clause including the rele-
vant microRNA, age and Breslow index. One hundred thirsty microRNAs have
been identified and have been selected for modelling but only 51 has at least five
valid replicas. After identifying 23 microRNAs, which already have extensively
been studied a file containing the among of these microRNA compared with
the healthy persons was created. The 23 microRNA candidates to melonama
signature are : hsa-let-7b, hsa-miR-17, hsa-miR-18a, hsa-miR-20a, hsa-miR-
21, hsa-miR-34a, hsa-miR-130a, hsa-miR-141, hsa-miR-143, hsa-miR-145, hsa-
miR-146a, hsa-miR-152, hsa-miR-155, hsa-miR-185, hsa-miR-191, hsa-miR-200c,
hsa-miR-221, hsa-miR-222, hsa-miR-338-3p, hsa-miR-1246, hsa-miR-1290, hsa-
miR-2110, miR-27b.
The representation contains information about microRNAs (23), Breslow in-
dex, age, relapse and metastasis or die.
A logical predicate expressed for each patient has been defined. Each patient
is characterized by 23 microRNAs. An important aspect of the representation is
to consider the clause as background in CF-induction (bg).
Example1: [input_clause(patient11,bg,[hsamiR20a(patient1,medium),
hsamiR21(patient1,medium),
...
hsamiR34a(patient1,low),hsamiR222(patient1,medium),hsamiR3383p(patient1,low)]). ]
Concerning the representation of Breslow Index we decided to introduce a
predicate breslow
input_clause(patient17, obs, [breslow(patient1,medium)]).
The age of patients has been discretized according with the next empirical
information provided by doctors (low: 1-30, medium: 31-60 and high: 61 and
more) and the clause is similar to Breslow index. Relapse is an important clinical
parameter related to the number of days that the patient kept his ”health”. It is
given as low (1-500 days), medium (501-1000 days) and high (more of 1001 days).
The predicate relapsed(X,Y) contains 2 atoms : the first one is the patient and
the second parameter gives information about the number of days considered
”healthy”.
The expert knowledge is represented by the microRNA well known in melanoma
development or invasive cancer.
input_clause(bg1, bg, [-hsamiR20a(X,high),
-hsamiR21(X,high), ...
-hsmaiR34a(X,low),
relapsed(X,high)]).
Basically, melanomas, expressions of miRNA-20a, miRNA-106a, miRNA-17,
miRNA-21, and miRNA-34a are significantly up-regulated, while miRNA-145
and miRNA-204 expression are significantly down-regulated. In our case we have
considered the microRNA-34a down-regulated. microRNA-34a maps to a chro-
mosome 1p36 region that is commonly deleted and it has been found to act as
a tumor suppressor through targeting of numerous genes associated with cell
proliferation and apoptosis. The patients analyzed have advanced ages and our
statistical analysis showed a low expression in this case.
4.2 Results in microRNA signature
The goal of our logical model is to identify the patients who have a fast evolution
of cancer. We have used CF-Induction to obtain diagnosis rules based on these
predicates. CF-Induction searches for a set of st-order predicate logic clauses
that distinguish between two classes, one which is presented to the learner as
positive and negative examples. CF-Induction is independent of the ordering
of the positive examples. Another important aspect is the possibility to check
whether B and H are consistent or not.
The most noteworthy result is the discover of a signature in the case of
aggressive melanoma represented by 5 microRNAs : 20a,21,34a, 222 and 333-3p.
This results has been obtained from the analysis of the number of examples
explained by these microRNA and compared with the the results published by
PubMed. The last two microRNAs are not well-known having a strong oncogene
or anti-oncogene regulation.
The idea was to produce all predicates : beginquote
production_field(
[
predicates(all)
]
).
[relapsed(patient25, low), -hsamiR20a(patient25, medium)]
[relapsed(patient25, low), -hsamiR21(patient25, medium)]
[relapsed(patient25, low), -hsamiR34a(patient25, low)]
[relapsed(patient25, low), -hsamiR222(patient25, medium)]
[relapsed(patient25, low), -hsamiR3383p(patient25, low)]
Patient 25 was born in 1947 and died in 2011. The screening was done during
stage iV of his cancer.
Using CF-induction with the production field :
production_field(
[
length <= 8,
term_depth < 3,
predicates([hsamiR222(_,_),hsmaiR3383p(_,_),age(_,_),relapsed(_,_)])
]
).
we have obtained the next result.
B & H is consistent
Hypotheses:
[hsamiR21(patient25, low), hsamiR3383p(patient25, low),
hsamiR222(patient25, verylow), hsamiR20a(patient25, low),
-age(patient25, high)]
This means the patient’s age explains the observation of 4 microRNA: mi-
croRNA 21,20a, 222 and 338-3p. Therefore if we corroborate the 2 results, the
microRNA 34a is a potential marker of melanoma in the case of older people.
Basically microRNA 34a, maps to a chromosome 1p36 region that is commonly
deleted and it has been found to act as a tumor suppressor through targeting
of numerous genes associated with cell proliferation and apoptosis. A similar
result has been obtained for the patient 24, who is born in 1932 and he is still
living. Her screening was done during stage III of her cancer. It seems a strong
correlation may exist between age and the microRNA 34a.
Another interesting result concerns patient 17, who has a very low concen-
tration of microRNA 338-3p. At first glance, this could be considered a cancer
marker, but the next results :
[relapsed(patient17, high), -age(patient17, medium)]
[hsamiR3383p(patient17, veryverylow), -age(patient17, medium)]
show that in this case age is the most important parameter of melanoma evo-
lution. This person was born in 1962; the melanoma was identified for the first
time in 2006. 6 years later she remains in phase IV.
5 Conclusion
Understanding genetic and metabolic networks is of the utmost importance.
With the development of DNA microarrays, it is possible to simultaneously an-
alyze the expression of up to thousands of genes and to construct gene networks
based on inferences over gene expression data. We have found in this study that 5
microRNAs : 20a,21,34a, 222 and 333-3p are an important indicator of melanoma
evolution. The microRNA 333-3p is not known as significantly down-regulated
in the case of aggressive melanoma. Another surprising result is the correlation
between age and microRNA 34a. Therefore, we are confident that future work
will let us appreciate the complexity of micro-RNAs in the case of the invasive
melanoma and going further to find out a signature of homeostasis. Homeosta-
sis, the subtle balance between proliferation, differentiation and cell death of
an organism is a complex phenomenon still little understood. Researchers are
pointing more and more the important role of micro-RNA in this phenomenon.
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