Calbindin 2 (CALB2) Regulates 5-Fluorouracil Sensitivity in Colorectal Cancer by Modulating the Intrinsic Apoptotic Pathway

The role of the calcium binding protein, Calbindin 2 (CALB2), in regulating the response of colorectal cancer (CRC) cells to 5-Fluorouracil (5-FU) was investigated. Real-time RT-PCR and Western blot analysis revealed that CALB2 mRNA and protein expression were down-regulated in p53 wild-type and p53 null isogenic HCT116 CRC cell lines following 48 h and 72 h 5-FU treatment. Moreover, 5-FU-induced apoptosis was significantly reduced in HCT116 and LS174T CRC cell lines in which CALB2 expression had been silenced. Further investigation revealed that CALB2 translocated to the mitochondria following 5-FU treatment and that 5-FU-induced loss of mitochondrial membrane potential (Delta psi(m)) was abrogated in CALB2-silenced cells. Furthermore, CALB2 silencing decreased 5-FU-induced cytochrome c and smac release from the mitochondria and also decreased 5-FU-induced activation of caspases 9 and 3/7. Of note, co-silencing of XIAP overcame 5-FU resistance in CALB2-silenced cells. Collectively, these results suggest that following 5-FU treatment in CRC cell lines, CALB2 is involved in apoptosis induction through the intrinsic mitochondrial pathway. This indicates that CALB2 may be an important mediator of 5-FU-induced cell death. Moreover, down-regulation of CALB2 in response to 5-FU may represent an intrinsic mechanism of resistance to this anti-cancer drug.

Computational learning of the conditional phase-type (C-Ph) distribution: Learning C-Ph distributions

This paper presents a new algorithm for learning the structure of a special type of Bayesian network. The conditional phase-type (C-Ph) distribution is a Bayesian network that models the probabilistic causal relationships between a skewed continuous variable, modelled by the Coxian phase-type distribution, a special type of Markov model, and a set of interacting discrete variables. The algorithm takes a dataset as input and produces the structure, parameters and graphical representations of the fit of the C-Ph distribution as output.The algorithm, which uses a greedy-search technique and has been implemented in MATLAB, is evaluated using a simulated data set consisting of 20,000 cases. The results show that the original C-Ph distribution is recaptured and the fit of the network to the data is discussed.

Discrete Conditional Phase-type model (DC_Ph) for patient waiting time with a logistic regression component to predict patient admission to hospital

Discrete Conditional Phase-type (DC-Ph) models are a family of models which represent skewed survival data conditioned on specific inter-related discrete variables. The survival data is modeled using a Coxian phase-type distribution which is associated with the inter-related variables using a range of possible data mining approaches such as Bayesian networks (BNs), the Naïve Bayes Classification method and classification regression trees. This paper utilizes the Discrete Conditional Phase-type model (DC-Ph) to explore the modeling of patient waiting times in an Accident and Emergency Department of a UK hospital. The resulting DC-Ph model takes on the form of the Coxian phase-type distribution conditioned on the outcome of a logistic regression model.

Intrinsic subtypes of gastric cancer, based on gene expression pattern, predict survival and respond differently to chemotherapy

BACKGROUND & AIMS:
Gastric cancer (GC) is a heterogeneous disease comprising multiple subtypes that have distinct biological properties and effects in patients. We sought to identify new, intrinsic subtypes of GC by gene expression analysis of a large panel of GC cell lines. We tested if these subtypes might be associated with differences in patient survival times and responses to various standard-of-care cytotoxic drugs.
METHODS:
We analyzed gene expression profiles for 37 GC cell lines to identify intrinsic GC subtypes. These subtypes were validated in primary tumors from 521 patients in 4 independent cohorts, where the subtypes were determined by either expression profiling or subtype-specific immunohistochemical markers (LGALS4, CDH17). In vitro sensitivity to 3 chemotherapy drugs (5-fluorouracil, cisplatin, oxaliplatin) was also assessed.
RESULTS:
Unsupervised cell line analysis identified 2 major intrinsic genomic subtypes (G-INT and G-DIF) that had distinct patterns of gene expression. The intrinsic subtypes, but not subtypes based on Lauren's histopathologic classification, were prognostic of survival, based on univariate and multivariate analysis in multiple patient cohorts. The G-INT cell lines were significantly more sensitive to 5-fluorouracil and oxaliplatin, but more resistant to cisplatin, than the G-DIF cell lines. In patients, intrinsic subtypes were associated with survival time following adjuvant, 5-fluorouracil-based therapy.
CONCLUSIONS:
Intrinsic subtypes of GC, based on distinct patterns of expression, are associated with patient survival and response to chemotherapy. Classification of GC based on intrinsic subtypes might be used to determine prognosis and customize therapy.

Aminoarabinose is essential for lipopolysaccharide export and intrinsic antimicrobial peptide resistance in Burkholderia cenocepacia

One common mechanism of resistance against antimicrobial peptides in Gram-negative bacteria is the addition of 4-amino-4-deoxy-l-arabinose (l-Ara4N) to the lipopolysaccharide (LPS) molecule. Burkholderia cenocepacia exhibits extraordinary intrinsic resistance to antimicrobial peptides and other antibiotics. We have previously discovered that unlike other bacteria, B. cenocepacia requires l-Ara4N for viability. Here, we describe the isolation of B. cenocepacia suppressor mutants that remain viable despite the deletion of genes required for l-Ara4N synthesis and transfer to the LPS. The absence of l-Ara4N is the only structural difference in the LPS of the mutants compared with that of the parental strain. The mutants also become highly sensitive to polymyxin B and melittin, two different classes of antimicrobial peptides. The suppressor phenotype resulted from a single amino acid replacement (aspartic acid to histidine) at position 31 of LptG, a protein component of the multi-protein pathway responsible for the export of the LPS molecule from the inner to the outer membrane. We propose that l-Ara4N modification of LPS provides a molecular signature required for LPS export and proper assembly at the outer membrane of B. cenocepacia, and is the most critical determinant for the intrinsic resistance of this bacterium to antimicrobial peptides.

Assessment of three Resistance-Nodulation-Cell Division drug efflux transporters of Burkholderia cenocepacia in intrinsic antibiotic resistance

Burkholderia cenocepacia are opportunistic Gram-negative bacteria that can cause chronic pulmonary infections in patients with cystic fibrosis. These bacteria demonstrate a high-level of intrinsic antibiotic resistance to most clinically useful antibiotics complicating treatment. We previously identified 14 genes encoding putative Resistance-Nodulation-Cell Division (RND) efflux pumps in the genome of B. cenocepacia J2315, but the contribution of these pumps to the intrinsic drug resistance of this bacterium remains unclear.

The Conditional Phase-type distribution: Graphical Models for Continuous Non-Normal Data:The Conditional Phase-type (C-Ph) distribution:

Conditional Gaussian (CG) distributions allow the inclusion of both discrete and continuous variables in a model assuming that the continuous variable is normally distributed. However, the CG distributions have proved to be unsuitable for survival data which tends to be highly skewed. A new method of analysis is required to take into account continuous variables which are not normally distributed. The aim of this paper is to introduce the more appropriate conditional phase-type (C-Ph) distribution for representing a continuous non-normal variable while also incorporating the causal information in the form of a Bayesian network.

The major facilitator superfamily transporter MdtM contributes to intrinsic resistance of Escherichia coli to quaternary ammonium compounds

OBJECTIVES:
Quaternary ammonium compounds (QACs) are used extensively as biocides and their misuse may be contributing to the development of bacterial resistance. Although the major intrinsic resistance to QACs of Gram-negative bacteria is mediated by the action of tripartite multidrug transporters of the resistance-nodulation-division family, we aimed to test if the promiscuity of the recently characterized major facilitator superfamily multidrug transporter, MdtM, from Escherichia coli enabled it also to function in the efflux of QACs.
METHODS:
The ability of the major facilitator mdtM gene product, when overexpressed from multicopy plasmid, to protect E. coli cells from the toxic effects of a panel of seven QACs was determined using growth inhibition assays in liquid medium. Interaction between QACs and MdtM was studied by a combination of substrate binding assays using purified protein in detergent solution and transport assays using inverted vesicles.
RESULTS:
E. coli cells that overproduced MdtM were less susceptible to the cytotoxic effects of each of the QACs tested compared with cells that did not overproduce the transporter. Purified MdtM bound each QAC with micromolar affinity and the protein utilized the electrochemical proton gradient to transport QACs across the cytoplasmic membrane. Furthermore, the results suggested a functional interaction between MdtM and the tripartite resistance-nodulation-division family AcrAB-TolC efflux system.
CONCLUSIONS:
The results support a hitherto unidentified capacity for a single-component multidrug transporter of the major facilitator superfamily, MdtM, to function in the efflux of a broad range of QACs and thus contribute to the intrinsic resistance of E. coli to these compounds.

Computational techniques for a simple theory of conditional preferences

A simple logic of conditional preferences is defined, with a language that allows the compact representation of certain kinds of conditional preference statements, a semantics and a proof theory. CP-nets and TCP-nets can be mapped into this logic, and the semantics and proof theory generalise those of CP-nets and TCP-nets. The system can also express preferences of a lexicographic kind. The paper derives various sufficient conditions for a set of conditional preferences to be consistent, along with algorithmic techniques for checking such conditions and hence confirming consistency. These techniques can also be used for totally ordering outcomes in a way that is consistent with the set of preferences, and they are further developed to give an approach to the problem of constrained optimisation for conditional preferences.

Clinical and technical factors associated with skin intrinsic fluorescence in subjects with type 1 diabetes from the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Study

The Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Interventions and Complications(EDIC) studies have established multiyear mean hemoglobin A1c (HbA1c) as predictive of microvascular complications in persons with type 1 diabetes. However, multiyear mean HbA1c is not always available in the clinical setting. Skin advanced glycation end products (AGEs) are thought to partially reflect effects of hyperglycemia over time, and measurement of skin AGEs might be a surrogate for multiyear mean HbA1c. As certain AGEs fluoresce and skin fluorescence has been demonstrated to correlate with the concentration of skin AGEs, noninvasive measurement by skin intrinsic fluorescence(SIF) facilitates the exploration of the association of mean HbA1c and other clinical/technical factors with SIF using the detailed phenotypic database available in DCCT/EDIC.

The Association of Skin-Intrinsic Fluorescence With Type 1 Diabetes Complications in the DCCT/EDIC Study

OBJECTIVESTo determine whether skin-intrinsic fluorescence (SIF) is associated with long-term complications of type 1 diabetes (T1D) and, if so, whether it is independent of chronic glycemic exposure and previous intensive therapy.RESEARCH DESIGN AND METHODSWe studied 1,185 (92%) of 1,289 active Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Interventions and Complications (EDIC) participants from 2010 to 2011. SIF was determined using a fluorescence spectrometer and related cross-sectionally to recently determined measures of retinopathy (stereo fundus photography), cardiac autonomic neuropathy (CAN; R-R interval), confirmed clinical neuropathy, nephropathy (albumin excretion rate [AER]), and coronary artery calcification (CAC).RESULTSOverall, moderately strong associations were seen with all complications, before adjustment for mean HbA1c over time, which rendered these associations nonsignificant with the exception of sustained AER >30 mg/24 h and CAC, which were largely unaffected by adjustment. However, when examined within the former DCCT treatment group, associations were generally weaker in the intensive group and nonsignificant after adjustment, while in the conventional group, associations remained significant for CAN, sustained AER >30 mg/24 h, and CAC even after mean HbA1c adjustment.CONCLUSIONSSIF is associated with T1D complications in DCCT\EDIC. Much of this association appears to be related to historical glycemic exposure, particularly in the previously intensively treated participants, in whom adjustment for HbA1c eliminates statistical significance.