Germline polymorphisms in SIPA1 are associated with metastasis and other indicators of poor prognosis in breast cancer

INTRODUCTION: There is growing evidence that heritable genetic variation modulates metastatic efficiency. Our previous work using a mouse mammary tumor model has shown that metastatic efficiency is modulated by the GTPase-activating protein encoded by Sipa1 ('signal-induced proliferation-associated gene 1'). The aim of this study was to determine whether single nucleotide polymorphisms (SNPs) within the human SIPA1 gene are associated with metastasis and other disease characteristics in breast cancer. METHOD: The study population (n = 300) consisted of randomly selected non-Hispanic Caucasian breast cancer patients identified from a larger population-based series. Genomic DNA was extracted from peripheral leukocytes. Three previously described SNPs within SIPA1 (one within the promoter [-313G>A] and two exonic [545C>T and 2760G>A]) were characterized using SNP-specific PCR. RESULTS: The variant 2760G>A and the -313G>A allele were associated with lymph node involvement (P = 0.0062 and P = 0.0083, respectively), and the variant 545C>T was associated with estrogen receptor negative tumors (P = 0.0012) and with progesterone negative tumors (P = 0.0339). Associations were identified between haplotypes defined by the three SNPs and disease progression. Haplotype 3 defined by variants -313G>A and 2760G>A was associated with positive lymph node involvement (P = 0.0051), and haplotype 4 defined by variant 545C>T was associated with estrogen receptor and progesterone receptor negative status (P = 0.0053 and P = 0.0199, respectively). CONCLUSION: Our findings imply that SIPA1 germline polymorphisms are associated with aggressive disease behavior in the cohort examined. If these results hold true in other populations, then knowledge of SIPA1 SNP genotypes could potentially enhance current staging protocols

Active learning and optimal climate policy

This paper develops a climate-economy model with uncertainty, irreversibility, and active learning. Whereas previous papers assume learning from one observation per period, or experiment with control variables to gain additional information, this paper considers active learning from investment in monitoring, specifically in improved observations of the global mean temperature. We find that the decision maker invests a significant amount of money in climate research, far more than the current level, in order to increase the rate of learning about climate change. This helps the decision maker make improved decisions. The level of uncertainty decreases more rapidly in the active learning model than in the passive learning model with only temperature observations. As the uncertainty about climate change is smaller, active learning reduces the optimal carbon tax. The greater the risk, the larger is the effect of learning. The method proposed here is applicable to any dynamic control problem where the quality of monitoring is a choice variable, for instance, the precision at which we observe GDP, unemployment, or the quality of education

Context and culture associated with alcohol use amongst youth in major urban cities: A cross-country population based survey

Background: Alcohol consumption patterns are dependent upon culture and context. The aim of this study was to interview people aged 18–34 year old living in four cities in different regions of the world to explore differences in a range of alcohol measures to assist in determining culturally appropriate alcohol initiatives for this age group. Method: Multistage random sampling was consistent across the four cities (Ilorin (Nigeria), Wuhan (China), Montevideo (Uruguay) and Moscow (Russia)). The questionnaire was forward and back translated into relevant languages and face-to-face interviewing undertaken. The data were weighted to the population of each city. Uni-variable analysis (ever consumed, first time consumed, age when drunk for first time, number of days consumed, type consumed) and logistic regression modeling were undertaken. The final model for each city was adjusted for age, sex, marital status, highest education and employment status. In total 6235 interviews were undertaken (1391 in Ilorin, 1600 in Montevideo, 1604 in Moscow and 1640 in Wuhan). Results: Alcohol was consumed by 96.4% in Montevideo, 86.1% in Moscow, 53.4% in Wuhan and 33.3% in Ilorin. There was very little difference by gender except Ilorin males were more likely to consume alcohol than females. Alcohol was consumed on more days for Ilorin males; Wuhan females consumed alcohol on the least number of days; Ilorin had the most abstainers; Montevideo and Moscow the highest proportion of light drinkers; Ilorin and Montevideo the highest proportion of heavy drinkers. Differences by type of alcohol were also apparent. The final logistic regression model provided different models including higher alcohol consumption rates for males, 25–34 years of age, divorced/separated marital status and employed part time for Ilorin respondents; males and higher educated for Montevideo; males, 25 to 29 years of age and higher educated for Moscow; and 25–29 years of age, non-married and vocationally trained for those in Wuhan. Conclusion: Alcohol consumption in these four cities does not increase with age as found in most high income countries. The alcohol consumption patterns during this stage of the life cycle are important to assess so that high level, as well as country-specific, planning and interventions can be implemented

The genomes of two key bumblebee species with primitive eusocial organization

Background: The shift from solitary to social behavior is one of the major evolutionary transitions. Primitively eusocial bumblebees are uniquely placed to illuminate the evolution of highly eusocial insect societies. Bumblebees are also invaluable natural and agricultural pollinators, and there is widespread concern over recent population declines in some species. High-quality genomic data will inform key aspects of bumblebee biology, including susceptibility to implicated population viability threats. Results: We report the high quality draft genome sequences of Bombus terrestris and Bombus impatiens, two ecologically dominant bumblebees and widely utilized study species. Comparing these new genomes to those of the highly eusocial honeybee Apis mellifera and other Hymenoptera, we identify deeply conserved similarities, as well as novelties key to the biology of these organisms. Some honeybee genome features thought to underpin advanced eusociality are also present in bumblebees, indicating an earlier evolution in the bee lineage. Xenobiotic detoxification and immune genes are similarly depauperate in bumblebees and honeybees, and multiple categories of genes linked to social organization, including development and behavior, show high conservation. Key differences identified include a bias in bumblebee chemoreception towards gustation from olfaction, and striking differences in microRNAs, potentially responsible for gene regulation underlying social and other traits. Conclusions: These two bumblebee genomes provide a foundation for post-genomic research on these key pollinators and insect societies. Overall, gene repertoires suggest that the route to advanced eusociality in bees was mediated by many small changes in many genes and processes, and not by notable expansion or depauperation

In-Vivo Biodistribution and Safety of 99mTc-LLP2A-HYNIC in Canine Non-Hodgkin Lymphoma

Theranostic agents are critical for improving the diagnosis and treatment of non-Hodgkin Lymphoma (NHL). The peptidomimetic LLP2A is a novel peptide receptor radiotherapy candidate for treating NHL that expresses the activated α4β1 integrin. Tumor-bearing dogs are an excellent model of human NHL with similar clinical characteristics, behavior, and compressed clinical course. Canine in vivo imaging studies will provide valuable biodistribution and affinity information that reflects a diverse clinical population of lymphoma. This may also help to determine potential dose-limiting radiotoxicity to organs in human clinical trials. To validate this construct in a naturally occurring model of NHL, we performed in-vivo molecular targeted imaging and biodistribution in 3 normal dogs and 5 NHL bearing dogs. 99mTc-LLP2A-HYNIC-PEG and 99mTc-LLP2A-HYNIC were successfully synthesized and had very good labeling efficiency and radiochemical purity. 99mTc-LLP2A-HYNIC and 99mTc-LLP2A-HYNIC-PEG had biodistribution in keeping with their molecular size, with 99mTc-LLP2A-HYNIC-PEG remaining longer in the circulation, having higher tissue uptake, and having more activity in the liver compared to 99mTc-LLP2A-HYNIC. 99mTc-LLP2A-HYNIC was mainly eliminated through the kidneys with some residual activity. Radioactivity was reduced to near-background levels at 6 hours after injection. In NHL dogs, tumor showed moderately increased activity over background, with tumor activity in B-cell lymphoma dogs decreasing after chemotherapy. This compound is promising in the development of targeted drug-delivery radiopharmaceuticals and may contribute to translational work in people affected by non-Hodgkin lymphoma

DISPARE: DIScriminative PAttern REfinement for Position Weight Matrices

<p>Abstract</p> <p>Background</p> <p>The accurate determination of transcription factor binding affinities is an important problem in biology and key to understanding the gene regulation process. Position weight matrices are commonly used to represent the binding properties of transcription factor binding sites but suffer from low information content and a large number of false matches in the genome. We describe a novel algorithm for the refinement of position weight matrices representing transcription factor binding sites based on experimental data, including ChIP-chip analyses. We present an iterative weight matrix optimization method that is more accurate in distinguishing true transcription factor binding sites from a negative control set. The initial position weight matrix comes from JASPAR, TRANSFAC or other sources. The main new features are the discriminative nature of the method and matrix width and length optimization.</p> <p>Results</p> <p>The algorithm was applied to the increasing collection of known transcription factor binding sites obtained from ChIP-chip experiments. The results show that our algorithm significantly improves the sensitivity and specificity of matrix models for identifying transcription factor binding sites.</p> <p>Conclusion</p> <p>When the transcription factor is known, it is more appropriate to use a discriminative approach such as the one presented here to derive its transcription factor-DNA binding properties starting with a matrix, as opposed to performing <it>de novo </it>motif discovery. Generating more accurate position weight matrices will ultimately contribute to a better understanding of eukaryotic transcriptional regulation, and could potentially offer a better alternative to <it>ab initio </it>motif discovery.</p

Metabolic Factors Limiting Performance in Marathon Runners

Each year in the past three decades has seen hundreds of thousands of runners register to run a major marathon. Of those who attempt to race over the marathon distance of 26 miles and 385 yards (42.195 kilometers), more than two-fifths experience severe and performance-limiting depletion of physiologic carbohydrate reserves (a phenomenon known as ‘hitting the wall’), and thousands drop out before reaching the finish lines (approximately 1–2% of those who start). Analyses of endurance physiology have often either used coarse approximations to suggest that human glycogen reserves are insufficient to fuel a marathon (making ‘hitting the wall’ seem inevitable), or implied that maximal glycogen loading is required in order to complete a marathon without ‘hitting the wall.’ The present computational study demonstrates that the energetic constraints on endurance runners are more subtle, and depend on several physiologic variables including the muscle mass distribution, liver and muscle glycogen densities, and running speed (exercise intensity as a fraction of aerobic capacity) of individual runners, in personalized but nevertheless quantifiable and predictable ways. The analytic approach presented here is used to estimate the distance at which runners will exhaust their glycogen stores as a function of running intensity. In so doing it also provides a basis for guidelines ensuring the safety and optimizing the performance of endurance runners, both by setting personally appropriate paces and by prescribing midrace fueling requirements for avoiding ‘the wall.’ The present analysis also sheds physiologically principled light on important standards in marathon running that until now have remained empirically defined: The qualifying times for the Boston Marathon

Systems-wide analysis of manganese deficiency-induced changes in gene activity of Arabidopsis roots

Manganese (Mn) is pivotal for plant growth and development, but little information is available regarding the strategies that evolved to improve Mn acquisition and cellular homeostasis of Mn. Using an integrated RNA-based transcriptomic and high-throughput shotgun proteomics approach, we generated a comprehensive inventory of transcripts and proteins that showed altered abundance in response to Mn deficiency in roots of the model plant Arabidopsis. A suite of 22,385 transcripts was consistently detected in three RNA-seq runs; LC-MS/MS-based iTRAQ proteomics allowed the unambiguous determination of 11,606 proteins. While high concordance between mRNA and protein expression (R = 0.87) was observed for transcript/protein pairs in which both gene products accumulated differentially upon Mn deficiency, only approximately 10% of the total alterations in the abundance of proteins could be attributed to transcription, indicating a large impact of protein-level regulation. Differentially expressed genes spanned a wide range of biological functions, including the maturation, translation, and transport of mRNAs, as well as primary and secondary metabolic processes. Metabolic analysis by UPLC-qTOF-MS revealed that the steady-state levels of several major glucosinolates were significantly altered upon Mn deficiency in both roots and leaves, possibly as a compensation for increased pathogen susceptibility under conditions of Mn deficiency