Multiscale analysis of protein functions and stochastic modelling of gene transcriptional regulatory networks

Genomic and proteomic analyses have attracted a great deal of interests in biological research in recent years. Many methods have been applied to discover useful information contained in the enormous databases of genomic sequences and amino acid sequences. The results of these investigations inspire further research in biological fields in return. These biological sequences, which may be considered as multiscale sequences, have some specific features which need further efforts to characterise using more refined methods. This project aims to study some of these biological challenges with multiscale analysis methods and stochastic modelling approach. The first part of the thesis aims to cluster some unknown proteins, and classify their families as well as their structural classes. A development in proteomic analysis is concerned with the determination of protein functions. The first step in this development is to classify proteins and predict their families. This motives us to study some unknown proteins from specific families, and to cluster them into families and structural classes. We select a large number of proteins from the same families or superfamilies, and link them to simulate some unknown large proteins from these families. We use multifractal analysis and the wavelet method to capture the characteristics of these linked proteins. The simulation results show that the method is valid for the classification of large proteins. The second part of the thesis aims to explore the relationship of proteins based on a layered comparison with their components. Many methods are based on homology of proteins because the resemblance at the protein sequence level normally indicates the similarity of functions and structures. However, some proteins may have similar functions with low sequential identity. We consider protein sequences at detail level to investigate the problem of comparison of proteins. The comparison is based on the empirical mode decomposition (EMD), and protein sequences are detected with the intrinsic mode functions. A measure of similarity is introduced with a new cross-correlation formula. The similarity results show that the EMD is useful for detection of functional relationships of proteins. The third part of the thesis aims to investigate the transcriptional regulatory network of yeast cell cycle via stochastic differential equations. As the investigation of genome-wide gene expressions has become a focus in genomic analysis, researchers have tried to understand the mechanisms of the yeast genome for many years. How cells control gene expressions still needs further investigation. We use a stochastic differential equation to model the expression profile of a target gene. We modify the model with a Gaussian membership function. For each target gene, a transcriptional rate is obtained, and the estimated transcriptional rate is also calculated with the information from five possible transcriptional regulators. Some regulators of these target genes are verified with the related references. With these results, we construct a transcriptional regulatory network for the genes from the yeast Saccharomyces cerevisiae. The construction of transcriptional regulatory network is useful for detecting more mechanisms of the yeast cell cycle.

A fission yeast homolog of Int-6, the mammalian oncoprotein and eIF3 subunit, induces drug resistance when overexpressed

Through a screen to identify genes that induce multi-drug resistance when overexpressed, we have identified a fission yeast homolog of Int-6, a component of the human translation initiation factor eIF3. Disruption of the murine Int-6 gene by mouse mammary tumor virus (MMTV) has been implicated previously in tumorigenesis, although the underlying mechanism is not yet understood. Fission yeast Int6 was shown to interact with other presumptive components of eIF3 in vivo, and was present in size fractions consistent with its incorporation into a 43S translation preinitiation complex. Drug resistance induced by Int6 overexpression was dependent on the AP-1 transcription factor Pap1, and was associated with increased abundance of Pap1-responsive mRNAs, but not with Pap1 relocalization. Fission yeast cells lacking the int6 gene grew slowly. This growth retardation could be corrected by the expression of full length Int6 of fission yeast or human origin, or by a C-terminal fragment of the fission yeast protein that also conferred drug resistance, but not by truncated human Int-6 proteins corresponding to the predicted products of MMTV-disrupted murine alleles. Studies in fission yeast may therefore help to explain the ways in which Int-6 function can be perturbed during MMTV-induced mammary tumorigenesis.

Failure of amino acid homeostasis causes cell death following proteasome inhibition

The ubiquitin-proteasome system targets many cellular proteins for degradation and thereby controls most cellular processes. Although it is well established that proteasome inhibition is lethal, the underlying mechanism is unknown. Here, we show that proteasome inhibition results in a lethal amino acid shortage. In yeast, mammalian cells, and flies, the deleterious consequences of proteasome inhibition are rescued by amino acid supplementation. In all three systems, this rescuing effect occurs without noticeable changes in the levels of proteasome substrates. In mammalian cells, the amino acid scarcity resulting from proteasome inhibition is the signal that causes induction of both the integrated stress response and autophagy, in an unsuccessful attempt to replenish the pool of intracellular amino acids. These results reveal that cells can tolerate protein waste, but not the amino acid scarcity resulting from proteasome inhibition.

Control of cell cycle progression by phosphorylation of cyclin-dependent kinase (CDK) substrates

The eukaryotic cell cycle is a fundamental evolutionarily conserved process that regulates cell division from simple unicellular organisms, such as yeast, through to higher multicellular organisms, such as humans. The cell cycle comprises several phases, including the S-phase (DNA synthesis phase) and M-phase (mitotic phase). During S-phase, the genetic material is replicated, and is then segregated into two identical daughter cells following mitotic M-phase and cytokinesis. The S- and M-phases are separated by two gap phases (G1 and G2) that govern the readiness of cells to enter S- or M-phase. Genetic and biochemical studies demonstrate that cell division in eukaryotes is mediated by CDKs (cyclin-dependent kinases). Active CDKs comprise a protein kinase subunit whose catalytic activity is dependent on association with a regulatory cyclin subunit. Cell-cycle-stage-dependent accumulation and proteolytic degradation of different cyclin subunits regulates their association with CDKs to control different stages of cell division. CDKs promote cell cycle progression by phosphorylating critical downstream substrates to alter their activity. Here, we will review some of the well-characterized CDK substrates to provide mechanistic insights into how these kinases control different stages of cell division.

Independent functions of yeast Pcf11p in pre-mRNA 3' end processing and in transcription termination

Pcf11p, an essential subunit of the yeast cleavage factor IA, is required for pre‐mRNA 3′ end processing, binds to the C‐terminal domain (CTD) of the largest subunit of RNA polymerase II (RNAP II) and is involved in transcription termination. We show that the conserved CTD interaction domain (CID) of Pcf11p is essential for cell viability. Interestingly, the CTD binding and 3′ end processing activities of Pcf11p can be functionally uncoupled from each other and provided by distinct Pcf11p fragments in trans. Impaired CTD binding did not affect the 3′ end processing activity of Pcf11p and a deficiency of Pcf11p in 3′ end processing did not prevent CTD binding. Transcriptional run‐on analysis with the CYC1 gene revealed that loss of cleavage activity did not correlate with a defect in transcription termination, whereas loss of CTD binding did. We conclude that Pcf11p is a bifunctional protein and that transcript cleavage is not an obligatory step prior to RNAP II termination.

Single molecule microscopy in 3D cell cultures and tissues

From the onset of the first microscopic visualization of single fluorescent molecules in living cells at the beginning of this century, to the present, almost routine application of single molecule microscopy, the method has well-proven its ability to contribute unmatched detailed insight into the heterogeneous and dynamic molecular world life is composed of. Except for investigations on bacteria and yeast, almost the entire story of success is based on studies on adherent mammalian 2D cell cultures. However, despite this continuous progress, the technique was not able to keep pace with the move of the cell biology community to adapt 3D cell culture models for basic research, regenerative medicine, or drug development and screening. In this review, we will summarize the progress, which only recently allowed for the application of single molecule microscopy to 3D cell systems and give an overview of the technical advances that led to it. While initially posing a challenge, we finally conclude that relevant 3D cell models will become an integral part of the on-going success of single molecule microscopy.

Effective Cell-Centred Time-Domain Maxwell's Equations Numerical Solvers

This research work analyses techniques for implementing a cell-centred finite-volume time-domain (ccFV-TD) computational methodology for the purpose of studying microwave heating. Various state-of-the-art spatial and temporal discretisation methods employed to solve Maxwell's equations on multidimensional structured grid networks are investigated, and the dispersive and dissipative errors inherent in those techniques examined. Both staggered and unstaggered grid approaches are considered. Upwind schemes using a Riemann solver and intensity vector splitting are studied and evaluated. Staggered and unstaggered Leapfrog and Runge-Kutta time integration methods are analysed in terms of phase and amplitude error to identify which method is the most accurate and efficient for simulating microwave heating processes. The implementation and migration of typical electromagnetic boundary conditions. from staggered in space to cell-centred approaches also is deliberated. In particular, an existing perfectly matched layer absorbing boundary methodology is adapted to formulate a new cell-centred boundary implementation for the ccFV-TD solvers. Finally for microwave heating purposes, a comparison of analytical and numerical results for standard case studies in rectangular waveguides allows the accuracy of the developed methods to be assessed.

Combining ganglion cell topology and data of patients with glaucoma to determine a structure-function map

PURPOSE: To introduce techniques for deriving a map that relates visual field locations to optic nerve head (ONH) sectors and to use the techniques to derive a map relating Medmont perimetric data to data from the Heidelberg Retinal Tomograph. METHODS: Spearman correlation coefficients were calculated relating each visual field location (Medmont M700) to rim area and volume measures for 10 degrees ONH sectors (HRT III software) for 57 participants: 34 with glaucoma, 18 with suspected glaucoma, and 5 with ocular hypertension. Correlations were constrained to be anatomically plausible with a computational model of the axon growth of retinal ganglion cells (Algorithm GROW). GROW generated a map relating field locations to sectors of the ONH. The sector with the maximum statistically significant (P < 0.05) correlation coefficient within 40 degrees of the angle predicted by GROW for each location was computed. Before correlation, both functional and structural data were normalized by either normative data or the fellow eye in each participant. RESULTS: The model of axon growth produced a 24-2 map that is qualitatively similar to existing maps derived from empiric data. When GROW was used in conjunction with normative data, 31% of field locations exhibited a statistically significant relationship. This significance increased to 67% (z-test, z = 4.84; P < 0.001) when both field and rim area data were normalized with the fellow eye. CONCLUSIONS: A computational model of axon growth and normalizing data by the fellow eye can assist in constructing an anatomically plausible map connecting visual field data and sectoral ONH data.