Stringent response of Escherichia coli: revisiting the bibliome using literature mining

Understanding the mechanisms responsible for cellular responses depends on the systematic collection and analysis of information on the main biological concepts involved. Indeed, the identification of biologically relevant concepts in free text, namely genes, tRNAs, mRNAs, gene products and small molecules, is crucial to capture the structure and functioning of different responses. Results In this work, we review literature reports on the study of the stringent response in Escherichia coli. Rather than undertaking the development of a highly specialised literature mining approach, we investigate the suitability of concept recognition and statistical analysis of concept occurrence as means to highlight the concepts that are most likely to be biologically engaged during this response. The co-occurrence analysis of core concepts in this stringent response, i.e. the (p)ppGpp nucleotides with gene products was also inspected and suggest that besides the enzymes RelA and SpoT that control the basal levels of (p)ppGpp nucleotides, many other proteins have a key role in this response. Functional enrichment analysis revealed that basic cellular processes such as metabolism, transcriptional and translational regulation are central, but other stress-associated responses might be elicited during the stringent response. In addition, the identification of less annotated concepts revealed that some (p)ppGpp-induced functional activities are still overlooked in most reviews. Conclusions In this paper we applied a literature mining approach that offers a more comprehensive analysis of the stringent response in E. coli. The compilation of relevant biological entities to this stress response and the assessment of their functional roles provided a more systematic understanding of this cellular response. Overlooked regulatory entities, such as transcriptional regulators, were found to play a role in this stress response. Moreover, the involvement of other stress-associated concepts demonstrates the complexity of this cellular response

Molecular targeted therapies for breast cancer treatment

Targeting the oestrogen receptor, HER2 (human epidermal growth factor receptor 2) and vascular endothelial growth factor has markedly improved breast cancer therapy. New targeted therapeutic approaches to induction of apoptosis or inhibition of anti-apoptosis, cell cycle progression, signal transduction and angiogenesis are described. The molecular pathways and their inhibitory or repair mechanisms are discussed in the preclinical and clinical settings

Mechanisms of estrogen-independent breast cancer growth driven by low estrogen concentrations are unique versus complete estrogen deprivation

Despite the success of the aromatase inhibitors (AIs) in treating estrogen receptor positive breast cancer, 15–20 % of patients receiving adjuvant AIs will relapse within 5–10 years of treatment initiation. Long-term estrogen deprivation (LTED) of breast cancer cells in culture mimics AI-induced estrogen depletion to dissect mechanisms of AI resistance. However, we hypothesized that a subset of patients receiving AI therapy may maintain low circulating concentrations of estrogens that influence the development of endocrine resistance. We expanded established LTED models to account for incomplete suppression of estrogen synthesis during AI therapy. MCF-7 cells were grown in medium with charcoal-stripped serum supplemented with defined concentrations of 17β-estradiol (E2) or the estrogenic androgen metabolite 5α-androstane-3β,17β-diol (3βAdiol), an endogenous selective estrogen receptor modulator. Cells were selected in concentrations of E2 or 3βAdiol that induce 10 or 90 percent of maximal proliferation (EC(10) and EC(90), respectively), or estrogen deprived. Estrogen independence was evaluated during selection by assessing cell growth in the absence or presence of E2 or 3βAdiol. Following >7 months of selection, estrogen independence developed in estrogen-deprived cells and EC(10)-selected cells. Functional analyses demonstrated that estrogen-deprived and EC(10)-selected cells developed estrogen independence via unique mechanisms, ERα-independent and dependent, respectively. Estrogen-independent proliferation in EC(10)-selected cells could be blocked by kinase inhibitors. However, these cells were resistant to kinase inhibition in the presence of low steroid concentrations. These data demonstrate that further understanding of the total estrogen environment in patients on AI therapy who experience recurrence is necessary to effectively treat endocrine-resistant disease