Fwd: [Biocombio] 2/7, 13:45, charla "Mapping DNA sequences to numbers", Igor Zwir
Sebastian Bassi
primer cuatrimestre, (Laboratorio EPSILON, Depto. de Computación)
Tenemos de orador a Igor Zwir, sobre un tema de bioinformática, el título es:
"Mapping DNA sequences to numbers: From cis-acting elements to complex
kinetic behaviors in bacterial regulatory networks"
Informalmente, Igor nos dice: "La idea es hablar de varias cosas que
estamos haciendo construyendo promotores artificiales, incluyendo motivos
de "binding", medida de su potencial in vitro, el resultado in vivo
midiendo la cinetica de "binding" y transcripcion, y la evolución de
estos sistemas en las enterobacterias, etc. Por ahi, y en otro orden de
cosas, puedo comentar al final algo de enfermedades
neurodegeneativas que estamos haciendo con un núcleo genético en Salta,
Argentina."
Abajo el abstract completo.
Los esperamos a tod@s, con pitanza.
Verónica, Diego y Esteban
-----------------------------------------
Mapping DNA sequences to numbers: From cis-acting elements to complex
kinetic behaviors in bacterial regulatory networks
Igor Zwir
Howard Hughes Medical Institute, Department of Molecular Microbiology,
Washington University School of Medicine, Campus Box 8230, 660 S. Euclid
Ave., St. Louis, Missouri, 63110, USA
Department of Computer Science and Artificial Intelligence, University of
Granada, c/. Daniel Saucedo Aranda, s/n 18071 Granada, Spain
------
Defining the plasticity of transcription factor binding sites by
deconstructing DNA consensus sequences
Transcriptional regulators recognize specific DNA sequences. Because
these sequences are embedded in the background of genomic DNA, it is hard
to identify the key cis-regulatory elements that determine disparate
patterns of gene expression. The detection of the intra- and
inter-species differences among these sequences is crucial for
understanding the molecular basis of both differential gene expression and
evolution.
Here, we address this problem by investigating the target promoters
controlled by the DNA-binding PhoP protein, which governs virulence and
Mg2+ homeostasis in several bacterial species. PhoP is particularly
interesting; it is highly conserved in different gamma/enterobacterias
regulating not only ancestral genes, but also governing the expression of
dozens of horizontally-acquired genes that differ from species to species.
Our approach consists of decomposing the DNA binding site sequences for a
given regulator into families of motifs (i.e., termed submotifs) using a
machine learning method inspired by the “Divide & Conquer” strategy. By
partitioning a motif into sub-patterns, computational advantages for
classification were produced, resulting in discovering of new members of a
regulon, and alleviating the problem of distinguishing functional sites in
chromatin immunoprecipitation and DNA microarray genome-wide analysis.
Moreover, we found that certain partitions were useful in revealing
biological properties of binding site sequences, including modular gains
and losses of PhoP binding sites through evolutionary turnover events, as
well as conservation in distant species. The high conservation of PhoP
submotifs within gamma/enterobacterias, as well as the regulatory protein
that recognizes them, suggests that the major cause of divergence between
related species is not due to the binding sites, as was previously
suggested for other regulators. Instead, the divergence may be attributed
to the fast evolution of orthologous target genes and/or the promoter
architectures resulting from the interaction of those binding sites with
the RNA polymerase.
Distinct expression behaviors of ancestral and horizontally-acquired
co-regulated by a transcription activator
Global transcriptional regulators control the expression of genes encoding
products required in distinct amounts and/or times when a cell experiences
inducing conditions. However, the cis-acting promoter features governing
the expression levels and kinetics of genes co-regulated by a given
regulator remain largely unknown. Here, we determine that the order in
which genes activated by the Salmonella PhoP protein are expressed is not
necessarily correlated with their mRNA levels nor does it automatically
reflect the amount of active PhoP protein in the cell. We establish that
PhoP-activated horizontally-acquired genes are transcribed after ancestral
genes due to the need to overcome silencing by the histone-like
nucleoid-structuring protein, identify the critical promoter features
governing differential gene expression, and demonstrate that PhoP utilizes
distinct mechanisms to promote gene transcription.
Mapping sequence to numbers: A quantitative model of promoter binding and
gene transcription
A major challenge in biology is to develop quantitative, predictive models
of gene regulation that unfold over time in response to environmental
changes. Prokaryotic promoters contain transcription factor binding sites
differing in their affinity and accessibility, but little is understood
about how these variables combine to generate a single fine-tuned,
quantitative response. By using the targets of the PhoP DNA binding
protein in Salmonella, we were able to quantify the relations between
transcription factor input and expression output. From this, we developed
our original model capable of both capturing variable dynamic interactions
between transcription factors and diverse promoter architectures, as well
as uncovering various gene expression profiles consisting of multiple
combinations of binding and transcription kinetic behaviors. The model
faithfully reproduced the observed quantitative changes in terms of times
and levels of the promoters that occurred upon altering the affinity of
the transcription factor for its binding sites and the organization of the
cis-features composing their architectures, as well as those caused by the
silencing effect of nucleotide association proteins that impedes
transcription factors to access to the DNA. The quantitative parameters
of this model were safely replaced by matching similarities to sequence
motifs representing cis-features of promoter architectures to achieve a
more general model. This model, based on sequence analysis, was able to
replicate the results of the previous model and to predict gene expression
of genes that were not previously implicated in the model construction,
without measuring their biochemical parameters.
--
Adrian Turjanski
UBA Professor/CONICET Researcher
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