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Long non-coding RNAs (lncRNAs) have emerged recently as key regulatory molecules with diverse roles at almost every level of the regulation of gene expression. The roles of these RNAs in the pathogenesis of cystic fibrosis (CF); a lethal multisystem, autosomal recessive disorder have yet to be explored. Our aim was to examine the expression profile of lncRNA, in the airway epithelium of people with CF. We examined the expression of 30,586 lncRNAs by microarray (Human LncRNA Array v3.0, Arraystar, Inc.), in vivo in bronchial cells isolated from endobronchial brushings obtained from CF and non-CF individuals. In total, we identified 1,063 lncRNAs with differential expression between CF and non-CF individuals (fold change ≥3, p≤0.001). The microarray also contained probes for ∼26,109 protein coding transcripts, of which 720 were differentially expressed between CF and non-CF brush samples (fold change ≥3, p≤0.001). Confirmation of a selection of differentially expressed coding mRNA and lncRNA transcripts such as XIST and TLR8 was achieved using qRT-PCR. Gene ontology bioinformatics analysis highlighted that many processes over-represented in the CF bronchial epithelium are related to inflammation. These data show a significantly altered lncRNA and mRNA expression profile in CF bronchial cells in vivo. Dysregulation of some of these lncRNAs may play important roles in the chronic infection and inflammation that exists in the lungs of people with CF.

Adequate sampling of the conformational space is a central challenge in ab initio protein structure prediction. In the absence of a template structure, a conformational search procedure guided by an energy function explores the conformational space, gathering an ensemble of low-energy decoy conformations. If the sampling is inadequate, the native structure may be missed altogether. Even if reproduced, a subsequent stage that selects a subset of decoys for further structural detail and energetic refinement may discard near-native decoys if they are high energy or insufficiently represented in the ensemble. Sampling should produce a decoy ensemble that facilitates the subsequent selection of near-native decoys. In this paper, we investigate a robotics-inspired framework that allows directly measuring the role of energy in guiding sampling. Testing demonstrates that a soft energy bias steers sampling toward a diverse decoy ensemble less prone to exploiting energetic artifacts and thus more likely to facilitate retainment of near-native conformations by selection techniques. We employ two different energy functions, the associative memory Hamiltonian with water and Rosetta. Results show that enhanced sampling provides a rigorous testing of energy functions and exposes different deficiencies in them, thus promising to guide development of more accurate representations and energy functions.

Precious information on protein function can be extracted from a detailed characterization of protein equilibrium dynamics. This remains elusive in wet and dry laboratories, as function-modulating transitions of a protein between functionally-relevant, thermodynamically-stable and meta-stable structural states often span disparate time scales. In this paper we propose a novel, robotics-inspired algorithm that circumvents time-scale challenges by drawing analogies between protein motion and robot motion. The algorithm adapts the popular roadmap-based framework in robot motion computation to handle the more complex protein conformation space and its underlying rugged energy surface. Given known structures representing stable and meta-stable states of a protein, the algorithm yields a time- and energy-prioritized list of transition paths between the structures, with each path represented as a series of conformations. The algorithm balances computational resources between a global search aimed at obtaining a global view of the network of protein conformations and their connectivity and a detailed local search focused on realizing such connections with physically-realistic models. Promising results are presented on a variety of proteins that demonstrate the general utility of the algorithm and its capability to improve the state of the art without employing system-specific insight.

Rapid advances in high-throughout genome sequencing technologies have resulted in millions of protein-encoding gene sequences with no functional characterization. Automated protein function annotation or prediction is a prime problem for computational methods to tackle in the post-genomic era of big molecular data. While recent community-driven experiments demonstrate that the accuracy of function prediction methods has significantly improved, challenges remain. The latter are related to the different sources of data exploited to predict function, as well as different choices in representing and integrating heterogeneous data. Current methods predict function from a protein’s sequence, often in the context of evolutionary relationships, from a protein’s three-dimensional structure or specific patterns in the structure, from neighbors in a protein–protein interaction network, from microarray data, or a combination of these different types of data. Here we review these methods and the state of protein function prediction, emphasizing recent algorithmic developments, remaining challenges, and prospects for future research.

Anaerobic bacteria are present in large numbers in the airways of people with cystic fibrosis (PWCF). In the gut, anaerobes produce short-chain fatty acids (SCFAs) that modulate immune and inflammatory processes.To investigate the capacity of anaerobes to contribute to cystic fibrosis (CF) airway pathogenesis via SCFAs.Samples of 109 PWCF were processed using anaerobic microbiological culture with bacteria present identified by 16S RNA sequencing. SCFA levels in anaerobic supernatants and bronchoalveolar lavage (BAL) were determined by gas chromatography. The mRNA and/or protein expression of two SCFA receptors, GPR41 and GPR43, in CF and non-CF bronchial brushings and 16HBE14o(-) and CFBE41o(-) cells were evaluated using reverse transcription polymerase chain reaction, Western blot analysis, laser scanning cytometry, and confocal microscopy. SCFA-induced IL-8 secretion was monitored by ELISA.Fifty-seven (52.3%) of 109 PWCF were anaerobe positive. Prevalence increased with age, from 33.3% to 57.7% in PWCF younger (n = 24) and older (n = 85) than 6 years of age. All evaluated anaerobes produced millimolar concentrations of SCFAs, including acetic, propionic, and butyric acids. SCFA levels were higher in BAL samples of adults than in those of children. GPR41 levels were elevated in CFBE41o(-) versus 16HBE14o(-) cells; CF versus non-CF bronchial brushings; and 16HBE14o(-) cells after treatment with cystic fibrosis transmembrane conductance regulator inhibitor CFTR(inh)-172, CF BAL, or inducers of endoplasmic reticulum stress. SCFAs induced a dose-dependent and pertussis toxin-sensitive IL-8 response in bronchial epithelial cells, with a higher production of IL-8 in CFBE41o(-) than in 16HBE14o(-) cells.This study illustrates that SCFAs contribute to excessive production of IL-8 in CF airways colonized with anaerobes via up-regulated GPR41.

BACKGROUND: A study was undertaken to assess patient navigation utilization and its impact on treatment interruptions and clinical trial enrollment among American Indian cancer patients. METHODS: Between February 2004 and September 2009, 332 American Indian cancer patients received patient navigation services throughout cancer treatment. The patient navigation program provided culturally competent navigators to assist patients with navigating cancer therapy, obtaining medications, insurance issues, communicating with medical providers, and travel and lodging logistics. Data on utilization and trial enrollment were prospectively collected. Data for a historical control group of 70 American Indian patients who did not receive patient navigation services were used to compare treatment interruptions among those undergoing patient navigation during curative radiation therapy (subgroup of 123 patients). RESULTS: The median number of contacts with a navigator was 12 (range, 1-119). The median time spent with the navigator at first contact was 40 minutes (range, 10-250 minutes), and it was 15 minutes for subsequent contacts. Patients treated with radiation therapy with curative intent who underwent patient navigation had fewer days of treatment interruption (mean, 1.7 days; 95% confidence interval [CI], 1.1-2.2 days) than historical controls who did not receive patient navigation services (mean, 4.9 days; 95% CI, 2.9-6.9 days). Of the 332 patients, 72 (22%; 95% CI, 17%-26%) were enrolled on a clinical treatment trial or cancer control protocol. CONCLUSIONS: Patient navigation was associated with fewer treatment interruptions and relatively high rates of clinical trial enrollment among American Indian cancer patients compared with national reports. Cancer 2011. © 2010 American Cancer Society.

Objective. Disparities in cancer-related health outcomes exist among Native Americans. This article assesses barriers to timely and effective cancer care among Native American cancer patients. Methods. We conducted a community-based participatory survey of newly diagnosed cancer patients to assess their basic knowledge of cancer screening and their beliefs about cancer management. Sociodemographic and cancer-related information was obtained from medical records. Mean scores for correct answers to the screening knowledge battery were tabulated and analyzed by race/ethnicity and sociodemographic characteristics. Multivariable regression models were used to adjust for sociodemographic characteristics in evaluating the association between screening knowledge and race/ethnicity. Results. The survey response rate was 62%. Of 165 patients, 52 were Native American and 113 were white. Native Americans with cancers for which a screening test is available presented with significantly higher rates of advanced-stage cancer ( p=0.04). Native Americans scored lower on the cancer screening knowledge battery ( p=0.0001). In multivariable analyses adjusting for age, gender, income, education level, employment status, and geographic distance from the cancer center, Native American race/ethnicity was the only factor significantly predictive of lower screening knowledge. Native Americans expressed more negative attitudes toward cancer treatment in some of the items regarding impacts and burden of cancer treatment. Conclusions. Native American cancer patients presented with higher rates of advanced-stage disease for screening-detectable cancers, lower levels of basic cancer screening knowledge, and more negative attitudes about cancer treatment than white patients. Public health interventions regarding screening and cancer education are needed.

(2007). Patient Navigation & Cultural Competency in Cancer Care. Oncology Issues: Vol. 22, No. 5, pp. 38-41.

International audience; Modeling the loop regions of proteins is an active area of research due to their significance in defining how the protein interacts with other molecular partners. The high structural flexibility of loops presents formidable challenges for both experimental and computational approaches. In this work, we combine a robotics approach with reinforcement learning (RL) to compute an ensemble of loop configurations. We are actively performing experiments on well known benchmark sets to illustrate how RL improves the efficiency and effectiveness of our approach.

International audience; Obtaining accurate representations of energy landscapes of biomolecules such as proteins and peptides is central to the study of their physicochemical properties and biological functions. Peptides are particularly interesting, as they exploit structural flexibility to modulate their biological function. Despite their small size, peptide modeling remains challenging due to the complexity of the energy landscape of such highly-flexible dynamic systems. Currently, only stochastic sampling-based methods can efficiently explore the conformational space of a peptide. In this paper, we suggest to combine two such methods to obtain a full characterization of energy landscapes of small yet flexible peptides. First, we propose a simplified version of the classical Basin Hopping algorithm to reveal low-energy regions in the landscape, and thus to identify the corresponding meta-stable structural states of a peptide. Then, we present several variants of a robotics-inspired algorithm, the Transition-based Rapidly-exploring Random Tree, to quickly determine transition path ensembles, as well as transition probabilities between meta-stable states. We demonstrate this combined approach on met-enkephalin.

We propose a novel robotics-inspired algorithm to compute physically-realistic motions connecting thermodynamically-stable and semi-stable structural states in protein molecules. Protein motion computation is a challenging problem due to the high-dimensionality of the search space involved and ruggedness of the potential energy surface underlying the space. To handle the multiple local minima issue, we propose a novel algorithm that is not based on the traditional Molecular Dynamics or Monte Carlo frameworks but instead adapts ideas from robot motion planning. In particular, the algorithm balances computational resources between a global search aimed at obtaining a global view of the network of protein conformations and their connectivity and a detailed local search focused on realizing such connections with physically-realistic models. We present here promising results on a variety of proteins and demonstrate the general utility of the algorithm and its capability to improve the state of the art without employing system-specific insight.

Background: Due to rapid sequencing of genomes, there are now millions of deposited protein sequences with no known function. Fast sequence-based comparisons allow detecting close homologs for a protein of interest to transfer functional information from the homologs to the given protein. Sequence-based comparison cannot detect remote homologs, in which evolution has adjusted the sequence while largely preserving structure. Structure-based comparisons can detect remote homologs but most methods for doing so are too expensive to apply at a large scale over structural databases of proteins. Recently, fragment-based structural representations have been proposed that allow fast detection of remote homologs with reasonable accuracy. These representations have also been used to obtain linearly-reducible maps of protein structure space. It has been shown, as additionally supported from analysis in this paper that such maps preserve functional co-localization of the protein structure space. Methods: Inspired by a recent application of the Latent Dirichlet Allocation (LDA) model for conducting structural comparisons of proteins, we propose higher-order LDA-obtained topic-based representations of protein structures to provide an alternative route for remote homology detection and organization of the protein structure space in few dimensions. Various techniques based on natural language processing are proposed and employed to aid the analysis of topics in the protein structure domain. Results: We show that a topic-based representation is just as effective as a fragment-based one at automated detection of remote homologs and organization of protein structure space. We conduct a detailed analysis of the information content in the topic-based representation, showing that topics have semantic meaning. The fragmentbased and topic-based representations are also shown to allow prediction of superfamily membership. Conclusions: This work opens exciting venues in designing novel representations to extract information about protein structures, as well as organizing and mining protein structure space with mature text mining tools.

Studies have endeavored to reconcile whether dysfunction of neutrophils in people with cystic fibrosis (CF) is a result of the genetic defect or is secondary due to infection and inflammation. In this study, we illustrate that disrupted function of the CF transmembrane conductance regulator (CFTR), such as that which occurs in patients with ∆F508 and/or G551D mutations, correlates with impaired degranulation of antimicrobial proteins. We demonstrate that CF blood neutrophils release less secondary and tertiary granule components compared with control cells and that activation of the low-molecular-mass GTP-binding protein Rab27a, involved in the regulation of granule trafficking, is defective. The mechanism leading to impaired degranulation involves altered ion homeostasis caused by defective CFTR function with increased cytosolic levels of chloride and sodium, yet decreased magnesium measured in CF neutrophils. Decreased magnesium concentration in vivo and in vitro resulted in significantly decreased levels of GTP-bound Rab27a. Treatment of G551D patients with the ion channel potentiator ivacaftor resulted in normalized neutrophil cytosolic ion levels and activation of Rab27a, thereby leading to increased degranulation and bacterial killing. Our results confirm that intrinsic alterations of circulating neutrophils from patients with CF are corrected by ivacaftor, thus illustrating additional clinical benefits for CFTR modulator therapy.

Secretory leukoprotease inhibitor (SLPI) is an anti-inflammatory protein present in respiratory secretions. Whilst epithelial cell SLPI is extensively studied, neutrophil associated SLPI is poorly characterised. Neutrophil function including chemotaxis and degranulation of proteolytic enzymes involves changes in cytosolic calcium (Ca2+) levels which is mediated by production of inositol 1,4,5-triphosphate (IP3) in response to G-protein-coupled receptor (GPCR) stimuli. The aim of this study was to investigate the intracellular function of SLPI and the mechanism-based modulation of neutrophil function by this antiprotease. Neutrophils were isolated from healthy controls (n=10), individuals with cystic fibrosis (CF) (n=5) or chronic obstructive pulmonary disease (COPD) (n=5). Recombinant human SLPI significantly inhibited fMet-Leu-Phe (fMLP) and interleukin(IL)-8 induced neutrophil chemotaxis (P<0.05) and decreased degranulation of matrix metalloprotease-9 (MMP-9), hCAP-18, and myeloperoxidase (MPO) (P<0.05). The mechanism of inhibition involved modulation of cytosolic IP3production and downstream Ca2+flux. The described attenuation of Ca2+flux was overcome by inclusion of exogenous IP3in electropermeabilized cells. Inhibition of IP3generation and Ca2+flux by SLPI may represent a novel anti-inflammatory mechanism, thus strengthening the attractiveness of SLPI as a potential therapeutic molecule in inflammatory airway disease associated with excessive neutrophil influx including CF, non-CF bronchiectasis, and COPD.

Fragment-based representations of protein structure have recently been proposed to identify remote homologs with reasonable accuracy. The representations have also been shown through PCA to elucidate low-dimensional maps of protein structure space. In this work we conduct further analysis of these representations, showing that the low-dimensional maps preserve functional co-localization. Moreover, we employ Latent Dirichlet Allocation to investigate a new, topic-based representation. We show through various techniques adapted from text mining that the topics have unique signatures over structural classes and allow a complementary yet informative organization of protein structure space.

Background Colonization by Aspergillus fumigatus in patients with cystic fibrosis (CF) can cause A fumigatus sensitization and/or allergic bronchopulmonary aspergillosis (ABPA), which affects pulmonary function and clinical outcomes. Recent studies show that specific allergens upregulate the surface-expressed basophil marker CD203c in sensitized subjects, a response that can be readily measured by using flow cytometry. Objective We sought to identify A fumigatus sensitization in patients with CF by using the basophil activation test (BAT). Methods Patients with CF attending Beaumont Hospital were screened for study inclusion. BAT was used to identify A fumigatus sensitization. Serologic (total and A fumigatus –specific IgE), pulmonary function, and body mass index measurements were performed. Results The BAT discriminates A fumigatus –sensitized from nonsensitized patients with CF. Persistent isolation of A fumigatus in sputum is a significant risk factor for A fumigatus sensitization. Levels of the A fumigatus –stimulated basophil activation marker CD203c inversely correlated with pulmonary function and body mass index in A fumigatus –sensitized but not nonsensitized patients with CF. Total and A fumigatus –specific IgE, but not IgG, levels are increased in A fumigatus –sensitized patients with CF and ABPA when compared with those in A fumigatus –sensitized and nonsensitized patients with CF without ABPA. Itraconazole treatment did not affect A fumigatus sensitization. Conclusion Combining the BAT with routine serologic testing allows classification of patients with CF into 3 groups: nonsensitized, A fumigatus –sensitized, and ABPA. Accurate and prompt identification of A fumigatus –associated clinical status might allow early and targeted therapeutic intervention, potentially improving clinical outcomes.

A central challenge in ab-initio protein structure prediction is the selection of low-resolution decoy conformations whose subsequent refinement leads to high-resolution near-native conformations. Successful selection strategies are tightly coupled with the exploration method employed to obtain decoys. Density-based clustering is often used to identify regions of the energy surface that are highly sampled by exploration trajectories. The trajectories are often numerous and long, because the goal is to obtain both a broad view of the energy surface and to converge to regions that are promising for further refinement. In this paper we separate this into two subgoals. We first investigate a robotics-inspired exploration framework and demonstrate its ability to steer sampling towards diverse decoy conformations. Once a broad view of the energy surface is obtained, Metropolis Monte Carlo trajectories continue the exploration from selected decoys. Density-based clustering then identifies regions where trajectories converge. The two exploration stages both employ molecular fragment replacement but gradually add more detail through different fragment lengths. Results on a diverse list of proteins show that highly-sampled regions contain near-native conformations that are worthy of further refinement for use in a blind prediction setting.

A central challenge in ab-initio structure prediction is the attainment of a large diverse ensemble of decoy conformations at low resolution so that subsequent refinement of selected decoys at higher resolution can identify the native structure among them [1]. The focus on the size and diversity is due to the weak discriminatory power of potential energy. Even state-of-the-art coarse grained energy functions are often weakly funneled, i.e., incapable of energetically distinguishing structurally dissimilar conformations [2]. While obtaining low-energy decoys increases the chance that a percentage of them are near-native, lowest energies are not necessarily associated with the native structure. Instead, the most effective selection techniques use redundancy, often in the form of the most populous conformational clusters identified, to identify the native structure. While redundancy is useful in this context, it is also a reason for the computational inefficiency of many conformational search algorithms.

Characterization of transition trajectories that take a protein between different functional states is an important yet challenging problem in computational biology. Approaches based on Molecular Dynamics can obtain the most detailed and accurate information but at considerable computational cost. To address the cost, sampling-based path planning methods adapted from robotics forego protein dynamics and seek instead conformational paths, operating under the assumption that dynamics can be incorporated later to transform paths to transition trajectories. Existing methods focus either on short peptides or large proteins; on the latter, coarse representations simplify the search space. Here we present a robotics-inspired tree-based method to sample conformational paths that connect known structural states of small- to medium- size proteins. We address the dimensionality of the search space using molecular fragment replacement to efficiently obtain physically-realistic conformations. The method grows a tree in conformational space rooted at a given conformation and biases the growth of the tree to steer it to a given goal conformation. Different bias schemes are investigated for their efficacy. Experiments on proteins up to 214 amino acids long with known functionally-relevant states more than 13A apart show that the method effectively obtains conformational paths connecting significantly different structural states.

The pathogenesis of lung disease in cystic fibrosis is characterised by decreased airway surface liquid volume and subsequent failure of normal mucociliary clearance. Mucus within the cystic fibrosis airways is enriched in negatively charged matrices composed of DNA released from colonizing bacteria or inflammatory cells, as well as F-actin and elevated concentrations of anionic glycosaminoglycans. Therapies acting against airway mucus in cystic fibrosis include aerosolized hypertonic saline. It has been shown that hypertonic saline possesses mucolytic properties and aids mucociliary clearance by restoring the liquid layer lining the airways. However, recent clinical and bench-top studies are beginning to broaden our view on the beneficial effects of hypertonic saline, which now extend to include anti-infective as well as anti-inflammatory properties. This review aims to discuss the described therapeutic benefits of hypertonic saline and specifically to identify novel models of hypertonic saline action independent of airway hydration.

Since its introduction, the basin hopping (BH) framework has proven useful for hard nonlinear optimization problems with multiple variables and modalities. Applications span a wide range, from packing problems in geometry to characterization of molecular states in statistical physics. BH is seeing a reemergence in computational structural biology due to its ability to obtain a coarse-grained representation of the protein energy surface in terms of local minima. In this paper, we show that the BH framework is general and versatile, allowing to address problems related to the characterization of protein structure, assembly, and motion due to its fundamental ability to sample minima in a high-dimensional variable space. We show how specific implementations of the main components in BH yield algorithmic realizations that attain state-of-the-art results in the context of ab initio protein structure prediction and rigid protein-protein docking. We also show that BH can map intermediate minima related with motions connecting diverse stable functionally relevant states in a protein molecule, thus serving as a first step towards the characterization of transition trajectories connecting these states.

A protein molecule assumes specific conformations under native conditions to fit and interact with other molecules. Due to the role that three-dimensional structure plays in protein function, significant efforts are devoted to elucidating native conformations. Many search algorithms are proposed to navigate the high-dimensional protein conformational space and its underlying energy surface in search of low-energy conformations that comprise the native state. In this work, we identify two strategies to enhance the sampling of native conformations. We show that employing an enhanced fragment library with greater structural diversity to assemble low-energy conformations allows sampling more native conformations. To efficiently handle the ensuing vast conformational space, only a representative subset of the sampled conformations are maintained and employed to further guide the search for native conformations. Our results show that these two strategies greatly enhance the sampling of the conformational space near the native state.

The serine proteinase inhibitor α-1 anti-trypsin (AAT) provides an antiprotease protective screen throughout the body. Mutations in the AAT gene (SERPINA1) that lead to deficiency in AAT are associated with chronic obstructive pulmonary diseases. The Z mutation encodes a misfolded variant of AAT that is not secreted effectively and accumulates intracellularly in the endoplasmic reticulum of hepatocytes and other AAT-producing cells. Until recently, it was thought that loss of antiprotease function was the major cause of ZAAT-related lung disease. However, the contribution of gain-of-function effects is now being recognized. Here we describe how both loss- and gain-of-function effects can contribute to ZAAT-related lung disease. In addition, we explore how SERPINA1 heterozygosity could contribute to smoking-induced chronic obstructive pulmonary diseases and consider the consequences.

The three-dimensional structure of a protein is a key determinant of its biological function. Given the cost and time required to acquire this structure through experimental means, computational models are necessary to complement wet-lab efforts. Many computational techniques exist for navigating the high-dimensional protein conformational search space, which is explored for low-energy conformations that comprise a protein's native states. This work proposes two strategies to enhance the sampling of conformations near the native state. An enhanced fragment library with greater structural diversity is used to expand the search space in the context of fragment-based assembly. To manage the increased complexity of the search space, only a representative subset of the sampled conformations is retained to further guide the search towards the native state. Our results make the case that these two strategies greatly enhance the sampling of the conformational space near the native state. A detailed comparative analysis shows that our approach performs as well as state-of-the-art ab initio structure prediction protocols.

Cancer mortality rates for Native Americans are among the highest of all racial and ethnic groups in the United States.1–3 Investigators have also reported other cancer-related disparities among Native Americans, such as lower rates of screening utilization1 and higher rates of advanced-stage disease at presentation.1,2,4–6 Furthermore, Native Americans in the Northern Plains region of the U. S. have age-adjusted mortality rates that are significantly higher than rates for Whites for certain cancers for which effective screening tests exist, such as cervical cancer (79% higher), colorectal cancer (58% higher), and prostate cancer (49% higher).7 A growing literature regarding health disparities has begun to illuminate causes for differences in health outcomes for various racial and ethnic groups.8,9 Root causes of such disparities are multiple. They arise from patient-, physician-, and health care system related factors. For Native Americans, patient-related factors may include mistrust of physicians or hospitals or lack of knowledge/health literacy concerning preventable diseases;10–12 physician-related factors may include lack of cultural competency in caring for this racial/ethnic group; and health-system-related factors may include issues related to the unique health care system (and its funding status) under which many Native Americans receive health care.13–15 However, scant literature focuses specifically on Native American health disparities and few studies have specifically sought to study causes of disparities disfavoring Native American cancer patients. Patient-related factors contributing to disparities include socioeconomic status, cultural differences, and limited health care literacy. Many interventions aimed at overcoming these impediments require trust between health care providers and vulnerable populations. Mistrust of and dissatisfaction with the health care system have been most thoroughly investigated as barriers for African American patient populations.16–19 Evidence exists that Native Americans also feel mistrust of health care providers and dissatisfaction with their health care experiences.20,21 However, no studies have been conducted that examine these attitudes among Native American cancer patients. Data exploring trust and perceptions of health care are critical in helping to formulate interventions in this vulnerable population. Effective clinical care for Native Americans requires understanding and sensitivity regarding attitudes and beliefs about health care. In 2003, a review of the Rapid City Regional Hospital (RCRH) (Rapid City, South Dakota) tumor registry (1990–2000) revealed that 50% of Native Americans presenting with breast, colorectal, prostate, cervical, or lung cancer presented with stage III–IV disease, compared with only 36% of non–Native Americans presenting with those cancers having the disease at an advanced stage.4,5 The RCRH is a regional facility that provides secondary and tertiary cancer care for approximately 60,000 adult Native Americans living on nearby reservations, in surrounding rural communities, and in Rapid City itself. In 2003, RCRH was awarded a Cancer Disparities Research Partnership (CDRP) grant to study the causes of cancer-related racial and ethnic disparities and to develop effective interventions to eliminate these disparities. Since that time, a multi-faceted, community-based participatory research and intervention effort has been forged to explore root causes of racial and ethnic disparities in Rapid City and surrounding areas, promote screening and prevention, enroll Native Americans in clinical trials, and provide patient navigation through cancer treatment.4,5,22 As part of this effort, we undertook a comparative study to examine mistrust and satisfaction with the health care system and to determine whether there are differences in these attitudes by race. We hypothesized that Native American cancer patients mistrust the health care system more and perceive the health care system more negatively than White patients.

Background American Indians (AIs) in the Northern Plains region suffer disproportionately high cancer mortality rates compared with the general US population and with AIs from other regions in the United States. Methods The National Cancer Institute developed the Cancer Disparity Research Partnership to address these inequities. This initiative in Rapid City, South Dakota, attempts to lower cancer mortality rates for AIs by access to innovative clinical trials, behavioral research, and a genetic study. Patient navigation is a critical part of the program. Two navigation strategies are described: navigators at the cancer center and navigators on each reservation. A retrospective analysis was performed to determine if navigated patients (n = 42) undergoing potentially curative radiotherapy had fewer treatment interruptions compared with nonnavigated patients (n = 74). Results A total of 213 AIs with cancer have undergone patient navigation. For those undergoing cancer treatment, the median number of patient navigation interactions was 15 (range 1 to 95), whereas for those seen in follow-up after their cancer treatment, the median number of contacts was 4 (range 1 to 26). AIs who received navigation services during curative radiation treatment had on average 3 fewer days of treatment interruptions compared to AIs who did not receive navigation services during curative radiation treatment (P = .002, N = 116). Conclusions Early findings suggest that patient navigation is a critical component in addressing cancer disparities in this population. The program has established trust with individual cancer patients, with the tribal councils, and with the general population on each of the three reservations of western South Dakota.

The article discusses a number of issues arising from the importation of literature by and for the Irish minority in New Zealand. The need for Irish and Catholic literature in New Zealand is also highlighted and debated.

The roughness of the protein energy surface poses a significant challenge to search algorithms that seek to obtain a structural characterization of the native state. Recent research seeks to bias search toward near-native conformations through one-dimensional structural profiles of the protein native state. Here we investigate the effectiveness of such profiles in a structure prediction setting for proteins of various sizes and folds. We pursue two directions. We first investigate the contribution of structural profiles in comparison to or in conjunction with physics-based energy functions in providing an effective energy bias. We conduct this investigation in the context of Metropolis Monte Carlo with fragment-based assembly. Second, we explore the effectiveness of structural profiles in providing projection coordinates through which to organize the conformational space. We do so in the context of a robotics-inspired search framework proposed in our lab that employs projections of the conformational space to guide search. Our findings indicate that structural profiles are most effective in obtaining physically realistic near-native conformations when employed in conjunction with physics-based energy functions. Our findings also show that these profiles are very effective when employed instead as projection coordinates to guide probabilistic search toward undersampled regions of the conformational space.

Background Alpha-1 antitrypsin (AAT) is the most abundant circulating antiprotease and is a member of the serine protease inhibitor (SERPIN) superfamily. The gene encoding AAT is the highly polymorphic SERPINA1 gene, found at 14q32.1. Mutations in the SERPINA1 gene can lead to AAT deficiency (AATD) which is associated with a substantially increased risk of lung and liver disease. The most common pathogenic AAT variant is Z (Glu342Lys) which causes AAT to misfold and polymerise within hepatocytes and other AAT-producing cells. A group of rare mutations causing AATD, termed Null or Q0, are characterised by a complete absence of AAT in the plasma. While ultra rare, these mutations confer a particularly high risk of emphysema. Methods We performed the determination of AAT serum levels by a rate immune nephelometric method or by immune turbidimetry. The phenotype was determined by isoelectric focusing analysis on agarose gel with specific immunological detection. DNA was isolated from whole peripheral blood or dried blood spot (DBS) samples using a commercial extraction kit. The new mutations were identified by sequencing all coding exons (II-V) of the SERPINA1 gene. Results We have found eight previously unidentified SERPINA1 Null mutations, named: Q0cork, Q0perugia, Q0brescia, Q0torino, Q0cosenza, Q0pordenone, Q0lampedusa, and Q0dublin . Analysis of clinical characteristics revealed evidence of the recurrence of lung symptoms (dyspnoea, cough) and lung diseases (emphysema, asthma, chronic bronchitis) in M/Null subjects, over 45 years-old, irrespective of smoking. Conclusions We have added eight more mutations to the list of SERPINA1 Null alleles. This study underlines that the laboratory diagnosis of AATD is not just a matter of degree, because the precise determination of the deficiency and Null alleles carried by an AATD individual may help to evaluate the risk for the lung disease. Electronic supplementary material The online version of this article (doi:10.1186/s13023-014-0172-y) contains supplementary material, which is available to authorized users.

Background Many proteins tune their biological function by transitioning between different functional states, effectively acting as dynamic molecular machines. Detailed structural characterization of transition trajectories is central to understanding the relationship between protein dynamics and function. Computational approaches that build on the Molecular Dynamics framework are in principle able to model transition trajectories at great detail but also at considerable computational cost. Methods that delay consideration of dynamics and focus instead on elucidating energetically-credible conformational paths connecting two functionally-relevant structures provide a complementary approach. Effective sampling-based path planning methods originating in robotics have been recently proposed to produce conformational paths. These methods largely model short peptides or address large proteins by simplifying conformational space. Methods We propose a robotics-inspired method that connects two given structures of a protein by sampling conformational paths. The method focuses on small- to medium-size proteins, efficiently modeling structural deformations through the use of the molecular fragment replacement technique. In particular, the method grows a tree in conformational space rooted at the start structure, steering the tree to a goal region defined around the goal structure. We investigate various bias schemes over a progress coordinate for balance between coverage of conformational space and progress towards the goal. A geometric projection layer promotes path diversity. A reactive temperature scheme allows sampling of rare paths that cross energy barriers. Results and conclusions Experiments are conducted on small- to medium-size proteins of length up to 214 amino acids and with multiple known functionally-relevant states, some of which are more than 13Å apart of each-other. Analysis reveals that the method effectively obtains conformational paths connecting structural states that are significantly different. A detailed analysis on the depth and breadth of the tree suggests that a soft global bias over the progress coordinate enhances sampling and results in higher path diversity. The explicit geometric projection layer that biases the exploration away from over-sampled regions further increases coverage, often improving proximity to the goal by forcing the exploration to find new paths. The reactive temperature scheme is shown effective in increasing path diversity, particularly in difficult structural transitions with known high-energy barriers.