Post-Transcriptional Gene Regulation

The cytokine tumour necrosis factor (TNF)alpha is a vital mediator of the innate immune response, and a pleiotropic regulator of cellular function. Its involvement in rheumatoid arthritis is illustrated by the clinical benefits of TNF alpha blockade. Post-transcriptional regulation (the control of mRNA stability and translation) appears to play a critical role in the regulation of TNF alpha expression by mitogen activated protein kinase signal transduction pathways and by anti-inflammatory agents. The aim of this article is to review some recent advances in our understanding of these processes, and to speculate on mechanisms of regulation of TNF alpha and other pro-inflammatory genes.

The key to the keys to immortality and eternal youth lies in the correct answer to the main question: How to naively discover new essential – but still hidden – features required for properly training novel adaptive supervised machine learning algorithms (SMLA)

 Regulation of HLA-DQ gene transcription is a complex phenomenon because the allelic polymorphism associated with these genes and their promoters is a putative source of differential allele expression. Both transcriptional and post-transcriptional regulation could account for the density of the molecules expressed at the cell surface and then for the specificity of the immune response. Different methods have been developed to evaluate the functional consequences of this polymorphism, but at present no universal method allows measurement of either the steady-state level or the half-life time of mRNA species of both DQA1 and DQB1 polymorphic genes in heterozygous cell lines. Here, we propose a potent method, based on relative quantification of reverse transcriptase-polymerase chain reaction products, which analyzes the differential expression of all DQA1 or DQB1 allele combinations. This method is used to analyze the differential expression of HLA-DQB1*0201/0402 alleles in the human heterozygous lymphoblastoid B-cell line. Nucleotidic sequences of the proximal upstream regulatory region of these alleles exhibit significant differences. We show that the DQB1*0402 promoter is able to mediate a transcription strength twice as efficiently as *0201. In addition, the *0402 mRNA steady-state level is also governed by a remarkable post-transcriptional regulation. Indeed, an important part (20%) of the *0402 primary transcript is derived by alternative splicing in a short mRNA translated into a nonfunctional protein. Despite their variable sequence and length, no difference in the half-life of DQB1*0201 and both DQB*0402 mRNAs was observed in B-lymphoblastoid cells. The implications of these findings are discussed.

Cancer is a multi-etiologic, multistage disease with a prevalent genetic component, which happens when a large number of genes, critical for cell growth, death, differentiation, migration, and metabolic plasticity are altered irreversibly, so as to either "gain" (oncogenes) or "lose" (tumour suppressors) their function. Recent discoveries have revealed the previously underestimated etiologic importance of multiple epigenetic, that is to say, reversible factors (histone modifications, DNA methylation, non-coding RNA) involved in the transcriptional and post-transcriptional regulation of proteins, indispensable for the control of cancerous phenotype. Stable alterations of epigenetic machinery ("epimutations") turn out to play a critical role at different steps of carcinogenesis. In addition, due to substantial recent progress in stem cell biology, the new concept of cancer stem cells has emerged. This, along with newly discovered epigenetic cancer mechani...

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

Self-pollination of diploid zonal geranium (Pelargonium x hortorum L.H. Bailey) florets leads to a dramatic rise in ethylene production, followed by abscission within 4 h. Neither wounding of the stigma, pollination with tetraploid pollen, nor heat-killed self pollen could elicit as much ethylene production and petal abscission as self-pollination. A cDNA sharing sequence identity with ACC synthase (GACS2) and three different cDNAs sharing sequence identity with ACC oxidase (GACO1, GACO2, GACO3) were isolated from geranium pistils. Transcripts hybridizing with these probes increased slightly in response to self-pollination, but the degree of accumulation in response to various treatments did not correlate with ethylene production. When calculated on a per-plant-part basis, transcripts hybridizing with GACS2 were equally distributed among the stigma+style, sterile ovary, and ovary tissues, but transcripts hybridizing with the three ACC oxidase clones were differentially distributed. ...

Jatropha curcas L., a multipurpose shrub has acquired significant economic importance for its seed oil which can be converted to biodiesel, is emerging as an alternative to petro-diesel. The deoiled seed cake remains after oil extraction is toxic and cannot be used as a feed despite having best nutritional contents. No quantitative and qualitative differences were observed between toxic and non-toxic varieties of J. curcas except for phorbol esters content. Development of molecular marker will enable to differentiate non-toxic from toxic variety in a mixed population and also help in improvement of the species through marker assisted breeding programs. The present investigation was undertaken to characterize the toxic and non-toxic varieties at molecular level and to develop PCR based molecular markers for distinguishing non-toxic from toxic or vice versa. The polymorphic markers were successfully identified specific to non-toxic and toxic variety using RAPD and AFLP techniques. Totally 371 RAPD, 1,442 AFLP markers were analyzed and 56 (15.09%) RAPD, 238 (16.49%) AFLP markers were found specific to either of the varieties. Genetic similarity between non-toxic and toxic verity was found to be 0.92 by RAPD and 0.90 by AFLP fingerprinting. In the present study out of 12 microsatellite markers analyzed, seven markers were found polymorphic. Among these seven, jcms21 showed homozygous allele in the toxic variety. The study demonstrated that both RAPD and AFLP techniques were equally competitive in identifying polymorphic markers and differentiating both the varieties of J. curcas. Polymorphism of SSR markers prevailed between the varieties of J. curcas. These RAPD and AFLP identified markers will help in selective cultivation of specific variety and along with SSRs these markers can be exploited for further improvement of the species through breeding and Marker Assisted Selection (MAS).

Imperatively Hidden Objects/Factors (IHO/F) refers to relevant items, which we cannot perceive and measure yet. Nevertheless, they have an impact on our predicted outcome. We have created model that allows for the integration and discovery for the still unknown. If researchers would take the time to understand it, they'd design their experimental studies in a much different way because their scientific objective would shift toward feature discovery to enhance feature selection to make even better predictions with supervised machine learning algorithms.

MicroRNAs (miRNAs) are post-transcriptional regulators of growth and development in both plants and animals. In plants, roots play essential roles in their anchorage to the soil as well as in nutrient and water uptake. In this review, we present recent advances made in the identification of miRNAs involved in embryonic root development, radial patterning, vascular tissue differentiation and formation of lateral organs (i.e., lateral and adventitious roots and symbiotic nitrogen-fixing nodules in legumes). Certain mi/siRNAs target members of the Auxin Response Factors family involved in auxin homeostasis and signalling and participate in complex regulatory loops at several crucial stages of root development. Other miRNAs target and restrict the action of various transcription factors that control root-related processes in several species. Finally, because abiotic stresses, which include nutrient or water deficiencies, generally modulate root growth and branching, we summarise the action of certain miRNAs in response to these stresses that may be involved in the adaptation of the root system architecture to the soil environment.

Please click here http://singledrug.com/media/Temporal_Analysis_of_Aging_description.pdf to see it.

I plan to add the following:

I found Osh6 at a very high rank of lifespan extending genes.  That is why I want to take all the genes that are part of the same molecular function as Osh6 and run an expression similarity analysis on SPELL (Serial Pattern of Expression Levels Locator) (see spell.yeastgenome.org/). 

I hope to find genes of unknown functions that could be involved in the same molecular function.  If I find this, I'll validate with my time series plots and TFBS similarity analysis.  That way I hope to predict the function of an unknown gene in lipid metabolism.  I will also try the opposite approach, i.e. I'll take genes of unknown function and look for a group of similarly expressed genes, which belong to the same GO term.

I added 19 pages to the second chapter.  I was able to further improve the accuracy of my yeast lifespan predicting machine learning algorithm by adding more features about lipids.  My last draft had an average prediction error of +/-5 replications.  Now it is less than +/-3 replication for the 50th percentile of the replicative lifespan.  To my knowledge nobody has published any research on about using machine learning and feature selection to predict the replicative age.

If you have any more ideas how I could make my dissertation look better, I'd be very thankful for any new feasible ideas and inspirations.  I have mainly been using Python and R for all my analysis.

I found that ribosomal RNA and proteins are most closely follow the cell cycle and that if knocked out the yeast's lifespan can be extended significantly.  I want to use the different cyclical periodically recurring and highly fluctuating gene expression oscillation pattern to discover the functions of genes we have not yet discovered.  I am interested in this because a friend of mine showed me a cancer dataset that looked very similar.  Since cancer is very similar to the yeast in that it keeps constantly dividing and hence its age can be expressed in replication we could use the same guilt-by-association approach to infer the functions or at least the way their expression is controlled by TFs from genes with similar temporal expression patterns of genes with already known functions.

When analyzing the time series plots of the 62 yeast chaperons I discovered that their expression plot trajectories can easily be clustered into 7 expression patterns, which are consisted with the 7 classes of yeast chaperons.  If I had only taken the average centroid of all 62 chaperons I would have been misled because using this approach could not be applied to potentially discover more new currently still unknown chaperons because no unknown chaperon would have time series trajectories, which would highly correlate with the average chaperon expression pattern of all 7 classes since their in-group expression pattern is very homogeneous but between the 7 different chaperon classes the expression pattern is very different.

I intended to elucidate the different ways by which we age by a very similar clustering algorithm, which I have developed based on time series curves over three subsequent yeast cell cycle consisting of 36 mRNA measures each about 25 minutes apart; thus, allowing to measure very steep mRNA expression fluctuation periods by manifold within the only 90 minutes of the cell cycle because even very brief but rapid expression changes that last not much longer than 1/12th of a cell cycle, of which I found surprisingly many.

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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