Supporting carers to manage pain medication in cancer patients at the end of life: A feasibility trial

Background: Carers of people with advanced cancer play a significant role in managing pain medication, yet they report insufficient information and support to do so confidently and competently. There is limited research evidence on the best ways for clinicians to help carers with medication management. Aims: To develop a pain medicines management intervention (Cancer Carers Medicines Management) for cancer patients’ carers near the end of life and evaluate feasibility and acceptability to nurses and carers. To test the feasibility of trial research procedures and to inform decisions concerning a full-scale randomised controlled trial. Design: Phase I-II clinical trial. A systematic, evidence-informed participatory method was used to develop CCMM: a nurse-delivered structured conversational process. A two-arm, cluster randomised controlled feasibility trial of Cancer Carers Medicines Management was conducted, with an embedded qualitative study to evaluate participants’ experiences of Cancer Carers Medicines Management and trial procedures. Setting: Community settings in two study sites. Participants: Phase I comprises 57 carers, patients and healthcare professionals and Phase II comprises 12 nurses and 15 carers. Results: A novel intervention was developed. Nurses were recruited and randomised. Carer recruitment to the trial was problematic with fewer than predicted eligible participants, and nurses judged a high proportion unsuitable to recruit into the study. Attrition rates following recruitment were typical for the study population. Cancer Carers Medicines Management was acceptable to carers and nurses who took part, and some benefits were identified. Conclusion: Cancer Carers Medicines Management is a robustly developed medicines management intervention which merits further research to test its effectiveness to improve carers’ management of pain medicines with patients at the end of life. The study highlighted aspects of trial design that need to be considered in future research

Trees and shrubs as sources of fodder in Australia

Experience with browse plants in Australia is briefly reviewed in terms of their forage value to animals, their economic value to the landholder and their ecological contribution to landscape stability. Of the cultivated species only two have achieved any degree of commercial acceptance (Leucaena leucocephala and Chamaecytisus palmensis). Both of these are of sufficiently high forage value to be used as the sole source of feed during seasonal periods of nutritional shortage. Both are also leguminous shrubs that establish readily from seed. It is suggested that a limitation in their present use is the reliance on stands of single species which leaves these grazing systems vulnerable to disease and insects. Grazing systems so far developed for high production and persistence of cultivated species involve short periods of intense grazing followed by long periods of recovery. Similar management may be necessary in the arid and semi-arid rangelands where palatable browse species are in decline

Structural Transformation of the Tandem Ubiquitin-Interacting Motifs in Ataxin-3 and Their Cooperative Interactions with Ubiquitin Chains

The ubiquitin-interacting motif (UIM) is a short peptide with dual function of binding ubiquitin (Ub) and promoting ubiquitination. We elucidated the structures and dynamics of the tandem UIMs of ataxin-3 (AT3-UIM12) both in free and Ub-bound forms. The solution structure of free AT3-UIM12 consists of two α-helices and a flexible linker, whereas that of the Ub-bound form is much more compact with hydrophobic contacts between the two helices. NMR dynamics indicates that the flexible linker becomes rigid when AT3-UIM12 binds with Ub. Isothermal titration calorimetry and NMR titration demonstrate that AT3-UIM12 binds diUb with two distinct affinities, and the linker plays a critical role in association of the two helices in diUb binding. These results provide an implication that the tandem UIM12 interacts with Ub or diUb in a cooperative manner through an allosteric effect and dynamics change of the linker region, which might be related to its recognitions with various Ub chains and ubiquitinated substrates

Correlation Network Analysis Applied to Complex Biofilm Communities

The complexity of the human microbiome makes it difficult to reveal organizational principles of the community and even more challenging to generate testable hypotheses. It has been suggested that in the gut microbiome species such as Bacteroides thetaiotaomicron are keystone in maintaining the stability and functional adaptability of the microbial community. In this study, we investigate the interspecies associations in a complex microbial biofilm applying systems biology principles. Using correlation network analysis we identified bacterial modules that represent important microbial associations within the oral community. We used dental plaque as a model community because of its high diversity and the well known species-species interactions that are common in the oral biofilm. We analyzed samples from healthy individuals as well as from patients with periodontitis, a polymicrobial disease. Using results obtained by checkerboard hybridization on cultivable bacteria we identified modules that correlated well with microbial complexes previously described. Furthermore, we extended our analysis using the Human Oral Microbe Identification Microarray (HOMIM), which includes a large number of bacterial species, among them uncultivated organisms present in the mouth. Two distinct microbial communities appeared in healthy individuals while there was one major type in disease. Bacterial modules in all communities did not overlap, indicating that bacteria were able to effectively re-associate with new partners depending on the environmental conditions. We then identified hubs that could act as keystone species in the bacterial modules. Based on those results we then cultured a not-yet-cultivated microorganism, Tannerella sp. OT286 (clone BU063). After two rounds of enrichment by a selected helper (Prevotella oris OT311) we obtained colonies of Tannerella sp. OT286 growing on blood agar plates. This system-level approach would open the possibility of manipulating microbial communities in a targeted fashion as well as associating certain bacterial modules to clinical traits (e.g.: obesity, Crohn's disease, periodontal disease, etc)

Genetic Variations in the Regulator of G-Protein Signaling Genes Are Associated with Survival in Late-Stage Non-Small Cell Lung Cancer

The regulator of G-protein signaling (RGS) pathway plays an important role in signaling transduction, cellular activities, and carcinogenesis. We hypothesized that genetic variations in RGS gene family may be associated with the response of late-stage non-small cell lung cancer (NSCLC) patients to chemotherapy or chemoradiotherapy. We selected 95 tagging single nucleotide polymorphisms (SNPs) in 17 RGS genes and genotyped them in 598 late-stage NSCLC patients. Thirteen SNPs were significantly associated with overall survival. Among them, rs2749786 of RGS12 was most significant. Stratified analysis by chemotherapy or chemoradiation further identified SNPs that were associated with overall survival in subgroups. Rs2816312 of RGS1 and rs6689169 of RGS7 were most significant in chemotherapy group and chemoradiotherapy group, respectively. A significant cumulative effect was observed when these SNPs were combined. Survival tree analyses identified potential interactions between rs944343, rs2816312, and rs1122794 in affecting survival time in patients treated with chemotherapy, while the genotype of rs6429264 affected survival in chemoradiation-treated patients. To our knowledge, this is the first study to reveal the importance of RGS gene family in the survival of late-stage NSCLC patients

Integrative Gene Regulatory Network Analysis Reveals Light-Induced Regional Gene Expression Phase Shift Programs in the Mouse Suprachiasmatic Nucleus

We use the multigenic pattern of gene expression across suprachiasmatic nuclei (SCN) regions and time to understand the dynamics within the SCN in response to a circadian phase-resetting light pulse. Global gene expression studies of the SCN indicate that circadian functions like phase resetting are complex multigenic processes. While the molecular dynamics of phase resetting are not well understood, it is clear they involve a “functional gene expression program”, e.g., the coordinated behavior of functionally related genes in space and time. In the present study we selected a set of 89 of these functionally related genes in order to further understand this multigenic program. By use of high-throughput qPCR we studied 52 small samples taken by anatomically precise laser capture from within the core and shell SCN regions, and taken at time points with and without phase resetting light exposure. The results show striking regional differences in light response to be present in the mouse SCN. By using network-based analyses, we are able to establish a highly specific multigenic correlation between genes expressed in response to light at night and genes normally activated during the day. The light pulse triggers a complex and highly coordinated network of gene regulation. The largest differences marking neuroanatomical location are in transmitter receptors, and the largest time-dependent differences occur in clock-related genes. Nighttime phase resetting appears to recruit transcriptional regulatory processes normally active in the day. This program, or mechanism, causes the pattern of core region gene expression to transiently shift to become more like that of the shell region

Improving stability of prediction models based on correlated omics data by using network approaches

Building prediction models based on complex omics datasets such as transcriptomics, proteomics, metabolomics remains a challenge in bioinformatics and biostatistics. Regularized regression techniques are typically used to deal with the high dimensionality of these datasets. However, due to the presence of correlation in the datasets, it is difficult to select the best model and application of these methods yields unstable results. We propose a novel strategy for model selection where the obtained models also perform well in terms of overall predictability. Several three step approaches are considered, where the steps are 1) network construction, 2) clustering to empirically derive modules or pathways, and 3) building a prediction model incorporating the information on the modules. For the first step, we use weighted correlation networks and Gaussian graphical modelling. Identification of groups of features is performed by hierarchical clustering. The grouping information is included in the prediction model by using group-based variable selection or group-specific penalization. We compare the performance of our new approaches with standard regularized regression via simulations. Based on these results we provide recommendations for selecting a strategy for building a prediction model given the specific goal of the analysis and the sizes of the datasets. Finally we illustrate the advantages of our approach by application of the methodology to two problems, namely prediction of body mass index in the DIetary, Lifestyle, and Genetic determinants of Obesity and Metabolic syndrome study (DILGOM) and prediction of response of each breast cancer cell line to treatment with specific drugs using a breast cancer cell lines pharmacogenomics dataset