Towards understanding the myometrial physiome: approaches for the construction of a virtual physiological uterus

Premature labour (PTL) is the single most significant factor contributing to neonatal morbidity in Europe with enormous attendant healthcare and social costs. Consequently, it remains a major challenge to alleviate the cause and impact of this condition. Our ability to improve the diagnosis and treatment of women most at risk of PTL is, however, actually hampered by an incomplete understanding of the ways in which the functions of the uterine myocyte are integrated to effect an appropriate biological response at the multicellular whole organ system. The level of organization required to co-ordinate labouring uterine contractile effort in time and space can be considered immense. There is a multitude of what might be considered mini-systems involved, each with their own regulatory feedback cycles, yet they each, in turn, will influence the behaviour of a related system. These include, but are not exclusive to, gestational-dependent regulation of transcription, translation, post-translational modifications, intracellular signaling dynamics, cell morphology, intercellular communication and tissue level morphology. We propose that in order to comprehend how these mini-systems integrate to facilitate uterine contraction during labour (preterm or term) we must, in concert with biological experimentation, construct detailed mathematical descriptions of our findings. This serves three purposes: firstly, providing a quantitative description of series of complex observations; secondly, proferring a database platform that informs further testable experimentation; thirdly, advancing towards the establishment of a virtual physiological uterus and in silico clinical diagnosis and treatment of PTL

The presence of bone marrow cytokeratin-immunoreactive cells does not predict outcome in gastric cancer patients

The independent prognostic significance of isolated tumour cells in bone marrow is still a matter of debate. This study evaluated the possible association of bone marrow micrometastases with tumour progression and prognosis in patients affected by gastric cancer. Bone marrow aspirates from both iliac crests were obtained from 114 consecutive patients operated on for gastric cancer. The specimens were stained with monoclonal antibody CAM 5.2 which reacts predominantly with cytokeratin filaments 8 and 19. Among 114 cases analysed, 33 cases (29%) had cytokeratine-positive cells in the bone marrow. There was no significant relationship between the presence of bone marrow micrometastases and site, depth of tumour invasion, lymph node metastases, presence of metastases. Patients with cytokeratine-positive cells had a trend towards a diffuse type histology (P=0.06). Among the 88 curatively resected patients, median survivals were 40 months and 36 months for cytokeratine-negative and cytokeratine-positive subsets respectively (P=0.9). Recurrence of the disease was observed in 39 cases (44.3%); 11 of 24 (45.8%) in the cytokeratine-positive subset and 28 of 64 (43.7%) in the cytokeratine-negative subset. In conclusion in our experience the presence of cytokeratine-positive cells in the bone marrow of curatively resected gastric cancer patients did not affect outcome and its independent prognostic significance remains to be proven before its official acceptance in the TNM classification

Large-scale clustering of CAGE tag expression data

Background: Recent analyses have suggested that many genes possess multiple transcription start sites (TSSs) that are differentially utilized in different tissues and cell lines. We have identified a huge number of TSSs mapped onto the mouse genome using the cap analysis of gene expression (CAGE) method. The standard hierarchical clustering algorithm, which gives us easily understandable graphical tree images, has difficulties in processing such huge amounts of TSS data and a better method to calculate and display the results is needed. Results: We use a combination of hierarchical and non-hierarchical clustering to cluster expression profiles of TSSs based on a large amount of CAGE data to profit from the best of both methods. We processed the genome-wide expression data, including 159,075 TSSs derived from 127 RNA samples of various organs of mouse, and succeeded in categorizing them into 70-100 clusters. The clusters exhibited intriguing biological features: a cluster supergroup with a ubiquitous expression profile, tissue-specific patterns, a distinct distribution of non-coding RNA and functional TSS groups. Conclusion: Our approach succeeded in greatly reducing the calculation cost, and is an appropriate solution for analyzing large-scale TSS usage data

Ensemble Modeling for Aromatic Production in Escherichia coli

Ensemble Modeling (EM) is a recently developed method for metabolic modeling, particularly for utilizing the effect of enzyme tuning data on the production of a specific compound to refine the model. This approach is used here to investigate the production of aromatic products in Escherichia coli. Instead of using dynamic metabolite data to fit a model, the EM approach uses phenotypic data (effects of enzyme overexpression or knockouts on the steady state production rate) to screen possible models. These data are routinely generated during strain design. An ensemble of models is constructed that all reach the same steady state and are based on the same mechanistic framework at the elementary reaction level. The behavior of the models spans the kinetics allowable by thermodynamics. Then by using existing data from the literature for the overexpression of genes coding for transketolase (Tkt), transaldolase (Tal), and phosphoenolpyruvate synthase (Pps) to screen the ensemble, we arrive at a set of models that properly describes the known enzyme overexpression phenotypes. This subset of models becomes more predictive as additional data are used to refine the models. The final ensemble of models demonstrates the characteristic of the cell that Tkt is the first rate controlling step, and correctly predicts that only after Tkt is overexpressed does an increase in Pps increase the production rate of aromatics. This work demonstrates that EM is able to capture the result of enzyme overexpression on aromatic producing bacteria by successfully utilizing routinely generated enzyme tuning data to guide model learning

Growth landscape formed by perception and import of glucose in yeast

An important challenge in systems biology is to quantitatively describe microbial growth using a few measurable parameters that capture the essence of this complex phenomenon. Two key events at the cell membrane—extracellular glucose sensing and uptake—initiate the budding yeast’s growth on glucose. However, conventional growth models focus almost exclusively on glucose uptake. Here we present results from growth-rate experiments that cannot be explained by focusing on glucose uptake alone. By imposing a glucose uptake rate independent of the sensed extracellular glucose level, we show that despite increasing both the sensed glucose concentration and uptake rate, the cell’s growth rate can decrease or even approach zero. We resolve this puzzle by showing that the interaction between glucose perception and import, not their individual actions, determines the central features of growth, and characterize this interaction using a quantitative model. Disrupting this interaction by knocking out two key glucose sensors significantly changes the cell’s growth rate, yet uptake rates are unchanged. This is due to a decrease in burden that glucose perception places on the cells. Our work shows that glucose perception and import are separate and pivotal modules of yeast growth, the interaction of which can be precisely tuned and measured.National Institutes of Health (U.S.). Pioneer AwardNatural Sciences and Engineering Research Council of Canada (NSERC). Graduate Fellowshi

Lymphatic mapping and sentinel node biopsy in gynecological cancers: a critical review of the literature

Although it does not have a long history of sentinel node evaluation (SLN) in female genital system cancers, there is a growing number of promising study results, despite the presence of some aspects that need to be considered and developed. It has been most commonly used in vulvar and uterine cervivcal cancer in gynecological oncology. According to these studies, almost all of which are prospective, particularly in cases where Technetium-labeled nanocolloid is used, sentinel node detection rate sensitivity and specificity has been reported to be 100%, except for a few cases. In the studies on cervical cancer, sentinel node detection rates have been reported around 80–86%, a little lower than those in vulva cancer, and negative predictive value has been reported about 99%. It is relatively new in endometrial cancer, where its detection rate varies between 50 and 80%. Studies about vulvar melanoma and vaginal cancers are generally case reports. Although it has not been supported with multicenter randomized and controlled studies including larger case series, study results reported by various centers around the world are harmonious and mutually supportive particularly in vulva cancer, and cervix cancer. Even though it does not seem possible to replace the traditional approaches in these two cancers, it is still a serious alternative for the future. We believe that it is important to increase and support the studies that will strengthen the weaknesses of the method, among which there are detection of micrometastases and increasing detection rates, and render it usable in routine clinical practice