A spatial interactome reveals the protein organization of the algal CO2 concentrating mechanism

Approximately one-third of global CO 2 fixation is performed by eukaryotic algae. Nearly all algae enhance their carbon assimilation by operating a CO 2-concentrating mechanism (CCM) built around an organelle called the pyrenoid, whose protein composition is largely unknown. Here, we developed tools in the model alga Chlamydomonas reinhardtii to determine the localizations of 135 candidate CCM proteins and physical interactors of 38 of these proteins. Our data reveal the identity of 89 pyrenoid proteins, including Rubisco-interacting proteins, photosystem I assembly factor candidates, and inorganic carbon flux components. We identify three previously undescribed protein layers of the pyrenoid: a plate-like layer, a mesh layer, and a punctate layer. We find that the carbonic anhydrase CAH6 is in the flagella, not in the stroma that surrounds the pyrenoid as in current models. These results provide an overview of proteins operating in the eukaryotic algal CCM, a key process that drives global carbon fixation

A comprehensive approach to decipher biological computation to achieve next generation high-performance exascale computing.

The human brain (volume=1200cm3) consumes 20W and is capable of performing>10%5E16 operations/s. Current supercomputer technology has reached 1015 operations/s, yet it requires 1500m%5E3 and 3MW, giving the brain a 10%5E12 advantage in operations/s/W/cm%5E3. Thus, to reach exascale computation, two achievements are required: 1) improved understanding of computation in biological tissue, and 2) a paradigm shift towards neuromorphic computing where hardware circuits mimic properties of neural tissue. To address 1), we will interrogate corticostriatal networks in mouse brain tissue slices, specifically with regard to their frequency filtering capabilities as a function of input stimulus. To address 2), we will instantiate biological computing characteristics such as multi-bit storage into hardware devices with future computational and memory applications. Resistive memory devices will be modeled, designed, and fabricated in the MESA facility in consultation with our internal and external collaborators

Financing of U.S. Biomedical Research and New Drug Approvals across Therapeutic Areas

We estimated U.S. biomedical research funding across therapeutic areas, determined the association with disease burden, and evaluated new drug approvals that resulted from this investment.We calculated funding from 1995 to 2005 and totaled Food and Drug Administration approvals in eight therapeutic areas (cardiovascular, endocrine, gastrointestinal, genitourinary, HIV/AIDS, infectious disease excluding HIV, oncology, and respiratory) primarily using public data. We then calculated correlations between funding, published estimates of disease burden, and drug approvals. Financial support for biomedical research from 1995 to 2005 increased across all therapeutic areas between 43% and 369%. Industry was the principal funder of all areas except HIV/AIDS, infectious disease, and oncology, which were chiefly sponsored by the National Institutes of Health (NIH). Total (rho = 0.70; P = .03) and industry funding (rho = 0.69; P = .04) were correlated with projected disease burden in high income countries while NIH support (rho = 0.80; P = .01) was correlated with projected disease burden globally. From 1995 to 2005 the number of new approvals was flat or declined across therapeutic areas, and over an 8-year lag period, neither total nor industry funding was correlated with future approvals.Across therapeutic areas, biomedical research funding increased substantially, appears aligned with disease burden in high income countries, but is not linked to new drug approvals. The translational gap between funding and new therapies is affecting all of medicine, and remedies must include changes beyond additional financial investment

Efficacy and cost-effectiveness of an outcall program to reduce carer burden and depression among carers of cancer patients (PROTECT) : rationale and design of a randomized controlled trial

Published: 6 January 2014BACKGROUND: Carers provide extended and often unrecognized support to people with cancer. The aim of this study is to test the hypothesis that excessive carer burden is modifiable through a telephone outcall intervention that includes supportive care, information and referral to appropriate psycho-social services. Secondary aims include estimation of changes in psychological health and quality of life. The study will determine whether the intervention reduces unmet needs among patient dyads. A formal economic program will also be conducted. METHODS/DESIGN: This study is a single-blind, multi-centre, randomized controlled trial to determine the efficacy and cost-efficacy of a telephone outcall program among carers of newly diagnosed cancer patients. A total of 230 carer/patient dyads will be recruited into the study; following written consent, carers will be randomly allocated to either the outcall intervention program (n = 115) or to a minimal outcall / attention control service (n = 115). Carer assessments will occur at baseline, at one and six months post-intervention. The primary outcome is change in carer burden; the secondary outcomes are change in carer depression, quality of life, health literacy and unmet needs. The trial patients will be assessed at baseline and one month post-intervention to determine depression levels and unmet needs. The economic analysis will include perspectives of both the health care sector and broader society and comprise a cost-consequences analysis where all outcomes will be compared to costs. DISCUSSION: This study will contribute to our understanding on the potential impact of a telephone outcall program on carer burden and provide new evidence on an approach for improving the wellbeing of carers.Patricia M Livingston, Richard H Osborne, Mari Botti, Cathy Mihalopoulos, Sean McGuigan, Leila Heckel, Kate Gunn, Jacquie Chirgwin, David M Ashley and Melinda William

Genetic variation in insulin-like growth factor signaling genes and breast cancer risk among BRCA1 and BRCA2 carriers

Abstract Introduction Women who carry mutations in BRCA1 and BRCA2 have a substantially increased risk of developing breast cancer as compared with the general population. However, risk estimates range from 20 to 80%, suggesting the presence of genetic and/or environmental risk modifiers. Based on extensive in vivo and in vitro studies, one important pathway for breast cancer pathogenesis may be the insulin-like growth factor (IGF) signaling pathway, which regulates both cellular proliferation and apoptosis. BRCA1 has been shown to directly interact with IGF signaling such that variants in this pathway may modify risk of cancer in women carrying BRCA mutations. In this study, we investigate the association of variants in genes involved in IGF signaling and risk of breast cancer in women who carry deleterious BRCA1 and BRCA2 mutations. Methods A cohort of 1,665 adult, female mutation carriers, including 1,122 BRCA1 carriers (433 cases) and 543 BRCA2 carriers (238 cases) were genotyped for SNPs in IGF1, IGF1 receptor (IGF1R), IGF1 binding protein (IGFBP1, IGFBP2, IGFBP5), and IGF receptor substrate 1 (IRS1). Cox proportional hazards regression was used to model time from birth to diagnosis of breast cancer for BRCA1 and BRCA2 carriers separately. For linkage disequilibrium (LD) blocks with multiple SNPs, an additive genetic model was assumed; and for single SNP analyses, no additivity assumptions were made. Results Among BRCA1 carriers, significant associations were found between risk of breast cancer and LD blocks in IGF1R (global P = 0.011 for LD block 2 and global P = 0.012 for LD block 11). Among BRCA2 carriers, an LD block in IGFBP2 (global P = 0.0145) was found to be associated with the time to breast cancer diagnosis. No significant LD block associations were found for the other investigated genes among BRCA1 and BRCA2 carriers. Conclusions This is the first study to investigate the role of genetic variation in IGF signaling and breast cancer risk in women carrying deleterious mutations in BRCA1 and BRCA2. We identified significant associations in variants in IGF1R and IRS1 in BRCA1 carriers and in IGFBP2 in BRCA2 carriers. Although there is known to be interaction of BRCA1 and IGF signaling, further replication and identification of causal mechanisms are needed to better understand these associations

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Data from: High-throughput genotyping of green algal mutants reveals random distribution of mutagenic insertion sites and endonucleolytic cleavage of transforming DNA

A high-throughput genetic screening platform in a single-celled photosynthetic eukaryote would be a transformative addition to the plant biology toolbox. Here, we present ChlaMmeSeq (Chlamydomonas MmeI-based insertion site Sequencing), a tool for simultaneous mapping of tens of thousands of mutagenic insertion sites in the eukaryotic unicellular green alga Chlamydomonas reinhardtii. We first validated ChlaMmeSeq by in-depth characterization of individual insertion sites. We then applied ChlaMmeSeq to a mutant pool and mapped 11,478 insertions, covering 39% of annotated protein coding genes. We observe that insertions are distributed in a manner largely indistinguishable from random, indicating that mutants in nearly all genes can be obtained efficiently. The data reveal that sequence-specific endonucleolytic activities cleave the transforming DNA and allow us to propose a simple model to explain the origin of the poorly understood exogenous sequences that sometimes surround insertion sites. ChlaMmeSeq is quantitatively reproducible, enabling its use for pooled enrichment screens and for the generation of indexed mutant libraries. Additionally, ChlaMmeSeq allows genotyping of hits from Chlamydomonas screens on an unprecedented scale, opening the door to comprehensive identification of genes with roles in photosynthesis, algal lipid metabolism, the algal carbon-concentrating mechanism, phototaxis, the biogenesis and function of cilia, and other processes for which C. reinhardtii is a leading model system

Supplemental Dataset 7: Insertion list for the dataset used for Figures 3 and 4, replicate a, raw.

Supplemental Dataset 7: Insertion list for the dataset used for Figures 3 and 4, replicate a, raw