Electron spin-echo studies of spin-labelled lipid membranes and free fatty acids interacting with human serum albumin

Human serum albumin (HSA) is an abundant plasma protein that transports fatty acids and also binds a wide variety of hydrophobic pharmacores. Echo-detected (ED) EPR spectra and D(2)O-electron spin echo envelope modulation (ESEEM) Fourier-transform spectra of spin-labelled free fatty acids and phospholipids were used jointly to investigate the binding of stearic acid to HSA and the adsorption of the protein on dipalmitoyl phosphatidylcholine (DPPC) membranes. In membranes, torsional librations are detected in the ED-spectra, the intensity of which depends on chain position at low temperature. Water penetration into the membrane is seen in the D(2)O-ESEEM spectra, the intensity of which decreases greatly at the middle of the membrane. Both the chain librational motion and the water penetration are only little affected by adsorption of serum albumin at the DPPC membrane surface. In contrast, both the librational motion and the accessibility of the chains to water are very different in the hydrophobic fatty acid binding sites of HSA from those in membranes. Indeed, the librational motion of bound fatty acids is suppressed at low temperature, and is similar for the different chain positions, at all temperatures. Correspondingly, all segments of the bound chains are accessible to water, to rather similar extents.     

Cross-presentation of caspase-cleaved apoptotic self antigens in HIV infection

We found that the proteome of apoptotic T cells includes prominent fragments of cellular proteins generated by caspases and that a high proportion of distinct T cell epitopes in these fragments is recognized by CD8+ T cells during HIV infection. The frequencies of effector CD8+ T cells that are specific for apoptosis-dependent epitopes correlate with the frequency of circulating apoptotic CD4+ T cells in HIV-1-infected individuals. We propose that these self-reactive effector CD8+ T cells may contribute to the systemic immune activation during chronic HIV infection. The caspase-dependent cleavage of proteins associated with apoptotic cells has a key role in the induction of self-reactive CD8+ T cell responses, as the caspase-cleaved fragments are efficiently targeted to the processing machinery and are cross-presented by dendritic cells. These findings demonstrate a previously undescribed role for caspases in immunopathology.     

AtLACS7 interacts with the TPR domains of the PTS1 receptor PEX5

Long-chain acyl-CoA synthetases (LACSs) activate fatty acids for further metabolism and are encoded by a multi-gene family in Arabidopsis. AtLACS6 possesses a type 2 (PTS2) peroxisomal targeting sequence, whilst AtLACS7 has both a type 1 and type 2 peroxisomal targeting sequence. AtLACS7 was used as bait in a yeast two-hybrid screen. Multiple clones of the PTS1 receptor PEX5 were isolated. Quantitative beta-galactosidase assay indicated that full-length PEX5 interacts with AtLACS7 with higher affinity than the TPR domains alone. The interaction between PEX5 and AtLACS7 was confirmed by co-immunoprecipitation and shown to be specific for the PTS1, therefore the AtLACS7 PTS1 is accessible to bind PEX5 in the full-length AtLACS7 protein. The expression profile of AtLACS6, AtLACS7, AtPEX5, and AtPEX7 revealed that AtLACS6 and 7 have distinct patterns of expression and we speculate that the possession of two targeting signals may be advantageous for the import of AtLACS7 when receptors may be limiting.     

In Central Obesity,Weight Loss Restores Platelet Sensitivity to Nitric Oxide and Prostacyclin

Central obesity shows impaired platelet responses to the antiaggregating effects of nitric oxide (NO), prostacyclin, and their effectors—guanosine 3′,5′‐cyclic monophosphate (cGMP) and adenosine 3′,5′‐cyclic monophosphate (cAMP). The influence of weight loss on these alterations is not known. To evaluate whether a diet‐induced body‐weight reduction restores platelet sensitivity to the physiological antiaggregating agents and reduces platelet activation in subjects affected by central obesity, we studied 20 centrally obese subjects before and after a 6‐month diet intervention aiming at reducing body weight by 10%, by measuring (i) insulin sensitivity (homeostasis model assessment of insulin resistance (HOMA_IR)); (ii) plasma lipids; (iii) circulating markers of inflammation of adipose tissue and endothelial dysfunction, and of platelet activation (i.e., soluble CD‐40 ligand (sCD‐40L) and soluble P‐selectin (sP‐selectin)); (iv) ability of the NO donor sodium nitroprusside (SNP), the prostacyclin analog Iloprost and the cyclic nucleotide analogs 8‐bromoguanosine 3′,5′‐cyclic monophosphate (8‐Br‐cGMP) and 8‐bromoadenosine 3′,5′‐cyclic monophosphate (8‐Br‐cAMP) to reduce platelet aggregation in response to adenosine‐5‐diphosphate (ADP); and (v) ability of SNP and Iloprost to increase cGMP and cAMP. The 10 subjects who reached the body‐weight target showed significant reductions of insulin resistance, adipose tissue, endothelial dysfunction, and platelet activation, and a significant increase of the ability of SNP, Iloprost, 8‐Br‐cGMP, and 8‐Br‐cAMP to reduce ADP‐induced platelet aggregation and of the ability of SNP and Iloprost to increase cyclic nucleotide concentrations. No change was observed in the 10 subjects who did not reach the body‐weight target. Changes of platelet function correlated with changes of HOMA_IR. Thus, in central obesity, diet‐induced weight loss reduces platelet activation and restores the sensitivity to the physiological antiaggregating agents, with a correlation with improvements in insulin sensitivity.     

A field guide to cultivating computational biology

Evolving in sync with the computation revolution over the past 30 years, computational biology has emerged as a mature scientific field. While the field has made major contributions toward improving scientific knowledge and human health, individual computational biology practitioners at various institutions often languish in career development. As optimistic biologists passionate about the future of our field, we propose solutions for both eager and reluctant individual scientists, institutions, publishers, funding agencies, and educators to fully embrace computational biology. We believe that in order to pave the way for the next generation of discoveries, we need to improve recognition for computational biologists and better align pathways of career success with pathways of scientific progress. With 10 outlined steps, we call on all adjacent fields to move away from the traditional individual, single-discipline investigator research model and embrace multidisciplinary, data-driven, team science.

Do you want to attract computational biologists to your project or to your department? Despite the major contributions of computational biology, those attempting to bridge the interdisciplinary gap often languish in career advancement, publication, and grant review. Here, sixteen computational biologists around the globe present "A field guide to cultivating computational biology," focusing on solutions.

Biology in the digital era requires computation and collaboration. A modern research project may include multiple model systems, use multiple assay technologies, collect varying data types, and require complex computational strategies, which together make effective design and execution difficult or impossible for any individual scientist. While some labs, institutions, funding bodies, publishers, and other educators have already embraced a team science model in computational biology and thrived [1–7], others who have not yet fully adopted it risk severely lagging behind the cutting edge. We propose a general solution: “deep integration” between biology and the computational sciences. Many different collaborative models can yield deep integration, and different problems require different approaches (Fig 1).Open in a separate windowFig 1Supporting interdisciplinary team science will accelerate biological discoveries.Scientists who have little exposure to different fields build silos, in which they perform science without external input. To solve hard problems and to extend your impact, collaborate with diverse scientists, communicate effectively, recognize the importance of core facilities, and embrace research parasitism. In biologically focused parasitism, wet lab biologists use existing computational tools to solve problems; in computationally focused parasitism, primarily dry lab biologists analyze publicly available data. Both strategies maximize the use and societal benefit of scientific data.In this article, we define computational science extremely broadly to include all quantitative approaches such as computer science, statistics, machine learning, and mathematics. We also define biology broadly, including any scientific inquiry pertaining to life and its many complications. A harmonious deep integration between biology and computer science requires action—we outline 10 immediate calls to action in this article and aim our speech directly at individual scientists, institutions, funding agencies, and publishers in an attempt to shift perspectives and enable action toward accepting and embracing computational biology as a mature, necessary, and inevitable discipline (Box 1).Box 1. Ten calls to action for individual scientists, funding bodies, publishers, and institutions to cultivate computational biology. Many actions require increased funding support, while others require a perspective shift. For those actions that require funding, we believe convincing the community of need is the first step toward agencies and systems allocating sufficient support

1. Respect collaborators’ specific research interests and motivationsProblem: Researchers face conflicts when their goals do not align with collaborators. For example, projects with routine analyses provide little benefit for computational biologists.Solution: Explicit discussion about interests/expertise/goals at project onset.Opportunity: Clearly defined expectations identify gaps, provide commitment to mutual benefit.
2. Seek necessary input during project design and throughout the project life cycleProblem: Modern research projects require multiple experts spanning the project’s complexity.Solution: Engage complementary scientists with necessary expertise throughout the entire project life cycle.Opportunity: Better designed and controlled studies with higher likelihood for success.
3. Provide and preserve budgets for computational biologists’ workProblem: The perception that analysis is “free” leads to collaborator budget cuts.Solution: When budget cuts are necessary, ensure that they are spread evenly.Opportunity: More accurate, reproducible, and trustworthy computational analyses.
4. Downplay publication author order as an evaluation metric for computational biologistsProblem: Computational biologist roles on publications are poorly understood and undervalued.Solution: Journals provide more equitable opportunities, funding bodies and institutions improve understanding of the importance of team science, scientists educate each other.Opportunity: Engage more computational biologist collaborators, provide opportunities for more high-impact work.
5. Value software as an academic productProblem: Software is relatively undervalued and can end up poorly maintained and supported, wasting the time put into its creation.Solution: Scientists cite software, and funding bodies provide more software funding opportunities.Opportunity: More high-quality maintainable biology software will save time, reduce reimplementation, and increase analysis reproducibility.
6. Establish academic structures and review panels that specifically reward team scienceProblem: Current mechanisms do not consistently reward multidisciplinary work.Solution: Separate evaluation structures to better align peer review to reward indicators of team science.Opportunity: More collaboration to attack complex multidisciplinary problems.
7. Develop and reward cross-disciplinary training and mentoringProblem: Academic labs and institutions are often insufficiently equipped to provide training to tackle the next generation of biological problems, which require computational skills.Solution: Create better training programs aligned to necessary on-the-job skills with an emphasis on communication, encourage wet/dry co-mentorship, and engage younger students to pursue computational biology.Opportunity: Interdisciplinary students uncover important insights in their own data.
8. Support computing and experimental infrastructure to empower computational biologistsProblem: Individual computational labs often fund suboptimal cluster computing systems and lack access to data generation facilities.Solution: Institutions can support centralized compute and engage core facilities to provide data services.Opportunity: Time and cost savings for often overlooked administrative tasks.
9. Provide incentives and mechanisms to share open data to empower discovery through reanalysisProblem: Data are often siloed and have untapped potential.Solution: Provide institutional data storage with standardized identifiers and provide separate funding mechanisms and publishing venues for data reuse.Opportunity: Foster new breed of researchers, “research parasites,” who will integrate multimodal data and enhance mechanistic insights.
10. Consider infrastructural, ethical, and cultural barriers to clinical data accessProblem: Identifiable health data, which include sensitive information that must be kept hidden, are distributed and disorganized, and thus underutilized.Solution: Leadership must enforce policies to share deidentifiable data with interoperable metadata identifiers.Opportunity: Derive new insights from multimodal data integration and build datasets with increased power to make biological discoveries.

     

Age-Related Differences in the Accuracy of Web Query-Based Predictions of Influenza-Like Illness

BackgroundWeb queries are now widely used for modeling, nowcasting and forecasting influenza-like illness (ILI). However, given that ILI attack rates vary significantly across ages, in terms of both magnitude and timing, little is known about whether the association between ILI morbidity and ILI-related queries is comparable across different age-groups. The present study aimed to investigate features of the association between ILI morbidity and ILI-related query volume from the perspective of age.MethodsSince Google Flu Trends is unavailable in Italy, Google Trends was used to identify entry terms that correlated highly with official ILI surveillance data. All-age and age-class-specific modeling was performed by means of linear models with generalized least-square estimation. Hold-out validation was used to quantify prediction accuracy. For purposes of comparison, predictions generated by exponential smoothing were computed.ResultsFive search terms showed high correlation coefficients of > .6. In comparison with exponential smoothing, the all-age query-based model correctly predicted the peak time and yielded a higher correlation coefficient with observed ILI morbidity (.978 vs. .929). However, query-based prediction of ILI morbidity was associated with a greater error. Age-class-specific query-based models varied significantly in terms of prediction accuracy. In the 0–4 and 25–44-year age-groups, these did well and outperformed exponential smoothing predictions; in the 15–24 and ≥ 65-year age-classes, however, the query-based models were inaccurate and highly overestimated peak height. In all but one age-class, peak timing predicted by the query-based models coincided with observed timing.ConclusionsThe accuracy of web query-based models in predicting ILI morbidity rates could differ among ages. Greater age-specific detail may be useful in flu query-based studies in order to account for age-specific features of the epidemiology of ILI.     

Strategies for genetic conservation of trees in the Peruvian Amazon

Forestry practices and high rates of land clearance for agriculture are causing genetic erosion of valuable tree species in the Peruvian Amazon, thereby endangering the economic sustainability of rural communities and limiting Peru's opportunities for the development of new timber and non-timber forest products. The potential utility and limitations of six low-input interventions to help forestall further genetic erosion in the region are discussed, with a focus on local community involvement. Improved agroforestry systems may help reduce deforestation by increasing farm productivity, although methods to increase the currently low adoption rate of these technologies need to be developed. Use of strategic tree domestication techniques can also improve farm productivity and prevent inadvertent genetic drift and inbreeding associated with traditional domestication practices, although to have a major impact, current programs need to be extended across the region. Woodlot forestry could supplant selective extraction of timber and offers an attractive opportunity for poverty alleviation if appropriate credit and land tenure policies can be developed. However, it may also result in increased deforestation if activities on public land cannot be controlled. The implementation of improved seed collection systems and simple seed transfer guidelines would help to reduce the collection of seed of poor quality and low genetic diversity, and avoid maladapted plantings, although such programs are difficult to monitor and seed costs may increase. Strategic identification and design of in situ conservation areas would help to ensure the viability of conserved populations, but requires the forfeiture of significant revenue from timber concessions.     

Sponge-associated microbial Antarctic communities exhibiting antimicrobial activity against Burkholderia cepacia complex bacteria

The aerobic heterotrophic bacterial communities isolated from three different Antarctic sponge species were analyzed for their ability to produce antimicrobial compounds active toward Cystic Fibrosis opportunistic pathogens belonging to the Burkholderia cepacia complex (Bcc). The phylogenetic analysis performed on the 16S rRNA genes affiliated the 140 bacterial strains analyzed to 15 genera. Just three of them (Psychrobacter, Pseudoalteromonas and Arthrobacter) were shared by the three sponges. The further Random Amplified Polymorphic DNA analysis allowed to demonstrate that microbial communities are highly sponge-specific and a very low degree of genus/species/strain sharing was detected. Data obtained revealed that most of these sponge-associated Antarctic bacteria and belonging to different genera were able to completely inhibit the growth of bacteria belonging to the Bcc. On the other hand, the same Antarctic strains did not have any effect on the growth of other pathogenic bacteria, strongly suggesting that the inhibition is specific for Bcc bacteria. Moreover, the antimicrobial compounds synthesized by the most active Antarctic bacteria are very likely Volatile Organic Compounds (VOCs), a finding that was confirmed by the SPME-GC-MS technique, which revealed the production of a large set of VOCs by a representative set of Antarctic bacteria. The synthesis of these VOCs appeared to be related neither to the presence of pks genes nor the presence of plasmid molecules. The whole body of data obtained in this work indicates that sponge-associated bacteria represent an untapped source for the identification of new antimicrobial compounds and are paving the way for the discovery of new drugs that can be efficiently and successfully used for the treatment of CF infections.