Transcriptional regulation

Numerous members of the cytochrome P450 (CYP) superfamily are induced after exposure to a variety of xenobiotics in human liver. We have gained considerable mechanistic insights into these processes in hepatocytes and multiple ligand-activated transcription factors have been identified over the past two decades. Families CYP1, CYP2 and CYP3 involved in xenobiotic metabolism are also expressed in a range of extrahepatic tissues (e.g. intestine, brain, kidney, placenta, lung, adrenal gland, pancreas, skin, mammary gland, uterus, ovary, testes and prostate). Since the expression of the majority of the isoforms appears to be very low in the extrahepatic tissues in comparison with predominant expression in adult liver, the role of the enzymes in overall biotransformation and total body clearance is minor. However, basal expression and up-regulation of extrahepatic CYP enzymes can significantly affect local disposition of xenobiotics or endogenous compounds in peripheral tissues and thus modify their pharmacological/toxicological effects or affect absorption of xenobiotics into systemic circulation. The goal of this review is to critically examine our current understanding of molecular mechanisms involved in induction of xenobiotic metabolizing CYP genes of human families CYP1, CYP2 and CYP3 by exogenous chemicals in extrahepatic tissues. We concentrate on organs such as the intestine, kidney, lung, placenta and skin, which are involved in drug distribution and clearance or are in direct contact with environmental xenobiotics. We also discuss single nucleotide polymorphisms (SNPs) of key CYPs, which at the level of transcription affect expression of the genes in the extrahepatic tissues or are associated with some pathophysiological stages or disorders in the organs.

Sulfate is required for detoxification of xenobiotics such as acetaminophen (APAP), a leading cause of liver failure in humans. The NaS1 sulfate transporter maintains blood sulfate levels sufficiently high for sulfonation reactions to work effectively for drug detoxification. In the present study, we identified two loss-of-function polymorphisms in the human NaS1 gene and showed the Nas1-null mouse to be hypersensitive to APAP hepatotoxicity. APAP treatment led to increased liver damage and decreased hepatic glutathione levels in the hyposulfatemic Nas1-null mice compared with that in normosulfatemic wild-type mice. Analysis of urinary APAP metabolites revealed a significantly lower ratio of APAP-sulfate to APAP-glucuronide in the Nas1-null mice. These results suggest hyposulfatemia increases sensitivity to APAP-induced hepatotoxicity by decreasing the sulfonation capacity to metabolize APAP. In conclusion, the results of this study highlight the importance of plasma sulfate level as a key modulator of acetaminophen metabolism and suggest that individuals with reduced NaS1 sulfate transporter function would be more sensitive to hepatotoxic agents. (HEPATOLOGY 2006;43: 1241–1247.)

We previously identified a gene, slr0374, in the unicellular cyanobacterium, Synechocystis sp. strain PCC 6803, that was highly expressed under iron-deficient conditions [J. Bacteriol. 182 (2000) 3536]. The gene product contains an AAA domain, a putative leucine zipper and a phosphorylation site and is part of an operon (with slr0373 and slr0376) that is responsive to various environmental stresses. Primer extension mapping and transcript analysis in insertion mutants showed that all transcripts from this operon originated upstream of slr0373 at four contiguous transcription start sites before being processed into individual transcripts. Both primary and processed transcripts were quite stable. The start sites were sensitive to changes in sulfur, light and redox agent, as well as iron. The structural and regulatory elements of this operon were highly conserved in phycobilisome-containing cyanobacteria that have been sequenced to date. Slr0374 and Slr0376 show homology with Ycf46 and...

Bacteria sense and respond to a wide range of physical and chemical signals. Central to sensing and responding to these signals are two-component systems, which have a sensor histidine kinase (SK) and a response regulator (RR) as basic components. Here we review the different molecular mechanisms by which these signals are integrated and modulate the phosphorylation state of SKs. Apart from the basic mechanism, which consists of signal recognition by the SK that leads to an alteration of its autokinase activity and subsequently a change in the RR phosphorylation state, a variety of alternative modes have evolved. The biochemical data available on SKs, particularly their molecular interactions with signals, nucleotides, and their cognate RRs, are also reviewed.

The Pax3–FKHR fusion protein is present in alveolar rhabdomyosarcoma and results from the t (2; 13)(q35; q14) chromosomal translocation. Its oncogenic activity is dependent on a combination of protein–DNA and protein–protein interactions mediated ...

Genes in prokaryotic and eukaryotic cells are typically regulated by complex promoters containing multiple binding sites for a variety of transcription factors leading to a specific functional dependence between regulatory inputs and transcriptional outputs. With increasing regularity, the transcriptional outputs from different promoters are being measured in quantitative detail in single-cell experiments thus providing the impetus for the development of quantitative models of transcription. We describe recent progress in developing models of transcriptional regulation that incorporate, to different degrees, the complexity of multi-state promoter dynamics, and its effect on the transcriptional outputs of single cells. The goal of these models is to predict the statistical properties of transcriptional outputs and characterize their variability in time and across a population of cells, as a function of the input concentrations of transcription factors. The interplay between mathemati...

The specification and growth of organs is controlled simultaneously by networks of transcription factors. While the connection between these transcription factors with fate determinants is increasingly clear, how they establish the link with the cell cycle is far less understood. Here we investigate this link in the developing Drosophila eye, where two transcription factors, the MEIS1 homologue hth and the Zn-finger tsh, synergize to stimulate the proliferation of naïve eye progenitors. Experiments combining transcriptomics, open-chromatin profiling, motif analysis and functional assays indicate that these progenitor transcription factors exert a global regulation of the proliferation program. Rather than directly regulating cell cycle genes, they control proliferation through an intermediary layer of nuclear receptors of the ecdysone/estrogen-signaling pathway. This regulatory subnetwork between hth, tsh and nuclear receptors might be conserved from Drosophila to mammals, as we fin...

Heart development results from a complex but highly integrated series of interactions involving various cell types of diverse origin. These interactions are carried out in a strictly regulated environment at both genetic and epigenetic levels. This review focuses on the molecular events that control the sequential processes involved in heart development from cardiac progenitors through the fully-developed four-chambered heart. Recent advances in molecular regulation of mesodermal cell determination to cardiac progenitors, their migration and fusion resulting into cardiac tube formation followed by looping, and septae and valve formation are described. The myocardium is an essential component of the heart. To understand the molecular basis of myocardial cell growth, it is imperative to evaluate the cascade of molecular events involved in the recruitment of mesodermal cells to muscle lineage, their subsequent differentiation into cardiac cells expressing muscle proteins, and delineate factors that modulate contractile protein expression. An overview of transcriptional regulatory processes in each of these steps, namely, cardiomyogenic determination, differentiation and modulation of cardiac-specific muscle protein expression is also provided.

Regulation of gene expression in Corynebacterium glutamicum represents an important issue since this Gram-positive bacterium is a notable industrial amino acid producer. Transcription initiation, beginning by binding of RNA polymerase to the promoter DNA sequence, is one of the main points at which bacterial gene expression is regulated. More than 50 transcriptional promoters have so far been experimentally localized in C. glutamicum. Most of them are assumed to be promoters of vegetative genes recognized by the main sigma factor. Although transcription initiation rate defined by many of these promoters may be affected by transcription factors, which activate or repress their function, the promoter regions share common sequence features, which may be generalized in a consensus sequence. In the consensus C. glutamicum promoter, the prominent feature is a conserved extended −10 region tgngnTA(c/t)aaTgg, while the −35 region is much less conserved. Some commonly utilized heterologous promoters were shown to drive strong gene expression in C. glutamicum. Conversely, some C. glutamicum promoters were found to function in Escherichia coli and in other bacteria. These observations suggest that C. glutamicum promoters functionally conform with the common bacterial promoter scheme, although they differ in some sequence structures.

Mitogen-activated protein kinases (MAPKs) are important components of the tripartite mitogen-activated protein kinase signaling cascade and play an important role in plant growth and development. Although members of the MAPK gene family have been identified in model plants, little information is available regarding this gene family in fruit crops. In this study, we carried out a computational analysis using the Musa Genome database to identify members of the MAPK gene family in banana, an economically important crop and the most popular fruit worldwide. Our analysis identified 25 members of the MAP kinase (MAPK or MPK) gene family. Phylogenetic analyses of MPKs in Arabidopsis, Oryza, and Populus have classified these MPKs into four subgroups. The presence of conserved domains in the deduced amino acid sequences, phylogeny, and genomic organization strongly support their identity as members of the MPK gene family. Expression analysis during ethylene-induced banana fruit ripening sugg...

Background DNA methylation in promoters is closely linked to downstream gene repression. However, whether DNA methylation is a cause or a consequence of gene repression remains an open question. If it is a cause, then DNA methylation may affect the affinity of transcription factors (TFs) for their binding sites (TFBSs). If it is a consequence, then gene repression caused by chromatin modification may be stabilized by DNA methylation. Until now, these two possibilities have been supported only by non-systematic evidence and they have not been tested on a wide range of TFs. An average promoter methylation is usually used in studies, whereas recent results suggested that methylation of individual cytosines can also be important. Results We found that the methylation profiles of 16.6% of cytosines and the expression profiles of neighboring transcriptional start sites (TSSs) were significantly negatively correlated. We called the CpGs corresponding to such cytosines “traffic lights”. We ...

RADICAL-INDUCED CELL DEATH1 (RCD1) is an important regulator of stress and hormonal and developmental responses in Arabidopsis thaliana. Together with its closest homolog, SIMILAR TO RCD-ONE1 (SRO1), it is the only Arabidopsis protein containing the WWE domain, which is known to mediate protein-protein interactions in other organisms. Additionally, these two proteins contain the core catalytic region of poly-ADP-ribose transferases and a conserved C-terminal domain. Tissue and subcellular localization data indicate that RCD1 and SRO1 have partially overlapping functions in plant development. In contrast mutant data indicate that rcd1 has defects in plant development, whereas sro1 displays normal development. However, the rcd1 sro1 double mutant has severe growth defects, indicating that RCD1 and SRO1 exemplify an important genetic principle - unequal genetic redundancy. A large pair-wise interaction test against the REGIA transcription factor collection revealed that RCD1 interacts with a large number of transcription factors belonging to several protein families, such as AP2/ERF, NAC and basic helix-loop-helix (bHLH), and that SRO1 interacts with a smaller subset of these. Full genome array analysis indicated that in many cases targets of these transcription factors have altered expression in the rcd1 but not the sro1 mutant. Taken together RCD1 and SRO1 are required for proper plant development.

Aging is one of the chief biomedical problems of the 21st century. After decades of basic research on biogerontology
(the science of aging), the aging process still remains an enigma. Although hundreds of "theories" on aging have
been formulated and many fundamental insights about age-related changes and genetic as well as environmental
interventions that change the pace of aging have been discovered, the actual why and how we age remain enigmatic.
In the post-genomic era there is an exponential increase in data. As a consequence it is a challenge to utilize all
information based on it and derive meaningful knowledge about biological phenomena. No individual scientist, no
group, nor consortium is capable of keeping up within their own field and are overwhelmed by the explosion of data
increase. Machine learning applied on biological data has the potential to solve this and cause a paradigm shift from
hypothesis-driven research (which predominates biological research including biogerontology) to data-driven
research.
This dissertation addresses this problem. In particular it proposes and executes the use of machine learning on
current existing data to predict drivers of aging (and therefore helps to distinguish causes from consequences),
interventions to counteract aging, and specific hypotheses to fill in research gaps that require experimental
validation.
The objective of this project is therefore to build computational models that are based on data relevant to the
phenomenon of aging and to predict as many of its aspects and dimensions as possible (thus elucidate their relations
to each other). For converting between and sorting within dimensions which are relevant to aging, different machine
learning models are evaluated. Ones models are built, it can be determined how much they can explain different
aspects of aging. Those models will also be capable of specifying which features are most relevant for prediction (in
both classification or regression). It is possible to train models that incorporate age-related changes based on
transcriptomic, proteomic, metabolomic, epigenomic as well as morphological data and their combinations. Machine
learning is further used to convert between and within them.
This work focuses on three types of predictors. Subsequently, discoveries are made with the statistical and learning
algorithms. The first model (lifespan predictor) is trained on predicting the lifespan based on genotype, environment
and combinations thereof. It is useful for predicting lifespan extending interventions on the population level. The
second model (age predictor) is trained on predicting the age given features measured on individuals. This is useful
for identifying biomarkers of aging and to determine the effects of interventions on the level of individuals. The third
model predicts functions/regulations of biological entities in regard to the aging process based on heterogeneous data
such as ontologies and diverse omics including time-series gene expression profiles (which can be visualized as
plots), and linked data. It is used to understand the role of genes and proteins as well as perhaps other entities such as
small molecules including lipids and other metabolites. Functions of proteins, which are still unknown, especially
those involved in yeast lipid metabolism and its regulation, can be predicted.
For this purpose we use primarily yeast as model organism as well as data on humans. Other biomedical model
organisms might be added if found beneficial.
The novel aspects of this research are for instance that 1) aging is investigated systematically in an unbiased
data-driven approach, 2) lifespan is predicted as continuous values, 3) age is predicted by combining multiple omics
data, 4) functions and regulations of biological entities like genes are predicted with high confidence from
heterogeneous data sources.
This thesis discovered that genetics is the most important feature of lifespan determination. Phenotypic features
related to lipid and membranes such as vacuolar morphology and autophagy activity are important for lifespan
determination according to the best performing models. A age predictor based on transcriptomics and proteomics can
highly accurately determine the age. It selected features are associated with both translation and lipid metabolism.
Among the top selected features are transcripts of genes when deleted exhibit abnormal vacuolar morphology as well
as targets of Opi1. Opi1 itself and its regulators were found to be differentially regulated post-transcriptional or
post-translational. Lastly, a function predictor for genes was created that achieved exceptional accuracy of
classifying aging genes. It learned for instance that piecemeal autophagy of the nucleus is strongly predictive for
aging-suppressor genes while cytoplasmic translation is strongly predictive for gerontogenes.
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