SNPer: An R Library for Quantitative Variant Analysis on Single Nucleotide Polymorphisms among Influenza Virus Populations

Influenza virus (IFV) can evolve rapidly leading to genetic drifts and shifts resulting in human and animal influenza epidemics and pandemics. The genetic shift that gave rise to the 2009 influenza A/H1N1 pandemic originated from a triple gene reassortment of avian, swine and human IFVs. More minor genetic alterations in genetic drift can lead to influenza drug resistance such as the H274Y mutation associated with oseltamivir resistance. Hence, a rapid tool to detect IFV mutations and the potential emergence of new virulent strains can better prepare us for seasonal influenza outbreaks as well as potential pandemics. Furthermore, identification of specific mutations by closely examining single nucleotide polymorphisms (SNPs) in IFV sequences is essential to classify potential genetic markers associated with potentially dangerous IFV phenotypes. In this study, we developed a novel R library called “SNPer” to analyze quantitative variants in SNPs among IFV subpopulations. The computational SNPer program was applied to three different subpopulations of published IFV genomic information. SNPer queried SNPs data and grouped the SNPs into (1) universal SNPs, (2) likely common SNPs, and (3) unique SNPs. SNPer outperformed manual visualization in terms of time and labor. SNPer took only three seconds with no errors in SNP comparison events compared with 40 hours with errors using manual visualization. The SNPer tool can accelerate the capacity to capture new and potentially dangerous IFV strains to mitigate future influenza outbreaks.     

Diversity and specificity of Frankia strains in nodules of sympatric Myrica gale, Alnus incana, and Shepherdia canadensis determined by rrs gene polymorphism

The identity of Frankia strains from nodules of Myrica gale, Alnus incana subsp. rugosa, and Shepherdia canadensis was determined for a natural stand on a lake shore sand dune in Wisconsin, where the three actinorhizal plant species were growing in close proximity, and from two additional stands with M. gale as the sole actinorhizal component. Unisolated strains were compared by their 16S ribosomal DNA (rDNA) restriction patterns using a direct PCR amplification protocol on nodules. Phylogenetic relationships among nodular Frankia strains were analyzed by comparing complete 16S rDNA sequences of study and reference strains. Where the three actinorhizal species occurred together, each host species was nodulated by a different phylogenetic group of Frankia strains. M. gale strains from all three sites belonged to an Alnus-Casuarina group, closely related to Frankia alni representative strains, and were low in diversity for a host genus considered promiscuous with respect to Frankia microsymbiont genotype. Frankia strains from A. incana nodules were also within the Alnus-Casuarina cluster, distinct from Frankia strains of M. gale nodules at the mixed actinorhizal site but not from Frankia strains from two M. gale nodules at a second site in Wisconsin. Frankia strains from nodules of S. canadensis belonged to a divergent subset of a cluster of Elaeagnaceae-infective strains and exhibited a high degree of diversity. The three closely related local Frankia populations in Myrica nodules could be distinguished from one another using our approach. In addition to geographic separation and host selectivity for Frankia microsymbionts, edaphic factors such as soil moisture and organic matter content, which varied among locales, may account for differences in Frankia populations found in Myrica nodules.     

A Murine Model of Stent Implantation in the Carotid Artery for the Study of Restenosis

Despite the considerable progress made in the stent development in the last decades, cardiovascular diseases remain the main cause of death in western countries. Beside the benefits offered by the development of different drug-eluting stents, the coronary revascularization bears also the life-threatening risks of in-stent thrombosis and restenosis. Research on new therapeutic strategies is impaired by the lack of appropriate methods to study stent implantation and restenosis processes. Here, we describe a rapid and accessible procedure of stent implantation in mouse carotid artery, which offers the possibility to study in a convenient way the molecular mechanisms of vessel remodeling and the effects of different drug coatings.     

Longer lifespan in male mice treated with a weakly estrogenic agonist,an antioxidant,an α‐glucosidase inhibitor or a Nrf2‐inducer

The National Institute on Aging Interventions Testing Program (ITP) evaluates agents hypothesized to increase healthy lifespan in genetically heterogeneous mice. Each compound is tested in parallel at three sites, and all results are published. We report the effects of lifelong treatment of mice with four agents not previously tested: Protandim, fish oil, ursodeoxycholic acid (UDCA) and metformin – the latter with and without rapamycin, and two drugs previously examined: 17‐α‐estradiol and nordihydroguaiaretic acid (NDGA), at doses greater and less than used previously. 17‐α‐estradiol at a threefold higher dose robustly extended both median and maximal lifespan, but still only in males. The male‐specific extension of median lifespan by NDGA was replicated at the original dose, and using doses threefold lower and higher. The effects of NDGA were dose dependent and male specific but without an effect on maximal lifespan. Protandim, a mixture of botanical extracts that activate Nrf2, extended median lifespan in males only. Metformin alone, at a dose of 0.1% in the diet, did not significantly extend lifespan. Metformin (0.1%) combined with rapamycin (14 ppm) robustly extended lifespan, suggestive of an added benefit, based on historical comparison with earlier studies of rapamycin given alone. The α‐glucosidase inhibitor, acarbose, at a concentration previously tested (1000 ppm), significantly increased median longevity in males and 90th percentile lifespan in both sexes, even when treatment was started at 16 months. Neither fish oil nor UDCA extended lifespan. These results underscore the reproducibility of ITP longevity studies and illustrate the importance of identifying optimal doses in lifespan studies.     

Radioimmunoimaging of Liver Metastases with PET Using a 64Cu-Labeled CEA Antibody in Transgenic Mice

Purpose

Colorectal cancer is one of the most common forms of cancer, and the development of novel tools for detection and efficient treatment of metastases is needed. One promising approach is the use of radiolabeled antibodies for positron emission tomography (PET) imaging and radioimmunotherapy. Since carcinoembryonic antigen (CEA) is an important target in colorectal cancer, the CEA-specific M5A antibody has been extensively studied in subcutaneous xenograft models; however, the M5A antibody has not yet been tested in advanced models of liver metastases. The aim of this study was to investigate the ⁶⁴Cu-DOTA-labeled M5A antibody using PET in mice bearing CEA-positive liver metastases.

Procedures

Mice were injected intrasplenically with CEA-positive C15A.3 or CEA-negative MC38 cells and underwent micro-computed tomography (micro-CT) to monitor the development of liver metastases. After metastases were detected, PET/MRI scans were performed with ⁶⁴Cu-DOTA-labeled M5A antibodies. H&E staining, immunohistology, and autoradiography were performed to confirm the micro-CT and PET/MRI findings.

Results

PET/MRI showed that M5A uptake was highest in CEA-positive metastases. The %ID/cm³ (16.5%±6.3%) was significantly increased compared to healthy liver tissue (8.6%±0.9%) and to CEA-negative metastases (5.5%±0.6%). The tumor-to-liver ratio of C15A.3 metastases and healthy liver tissue was 1.9±0.7. Autoradiography and immunostaining confirmed the micro-CT and PET/MRI findings.

Conclusion

We show here that the ⁶⁴Cu-DOTA-labeled M5A antibody imaged by PET can detect CEA positive liver metastases and is therefore a potential tool for staging cancer, stratifying the patients or radioimmunotherapy.     

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.

     

Anthrax in Western and Central African great apes

During the period of December 2004 to January 2005, Bacillus anthracis killed three wild chimpanzees (Pan troglodytes troglodytes) and one gorilla (Gorilla gorilla gorilla) in a tropical forest in Cameroon. While this is the second anthrax outbreak in wild chimpanzees, this is the first case of anthrax in gorillas ever reported. The number of great apes in Central Africa is dramatically declining and the populations are seriously threatened by diseases, mainly Ebola. Nevertheless, a considerable number of deaths cannot be attributed to Ebola virus and remained unexplained. Our results show that diseases other than Ebola may also threaten wild great apes, and indicate that the role of anthrax in great ape mortality may have been underestimated. These results suggest that risk identification, assessment, and management for the survival of the last great apes should be performed with an open mind, since various pathogens with distinct characteristics in epidemiology and pathogenicity may impact the populations. An animal mortality monitoring network covering the entire African tropical forest, with the dual aims of preventing both great ape extinction and human disease outbreaks, will create necessary baseline data for such risk assessments and management plans.     

Relating Species and Community Dynamics in an Heavily Exploited Marine Fish Community

We examined the dynamics of fish species and how they relate to species assemblage coherence in the heavily exploited Georges Bank fish community. Coherence is defined as reduced temporal variability of total assemblage biomass. We assumed that a higher degree of compensation hence coherence occurs within competitively coupled species assemblages; therefore, fisheries may directly alter the dynamics of certain targeted species sizes but assemblage structure will be relatively more stable owing to compensatory interactions. Species-sizes were grouped, based on negative covariance coupling in biomass time series from survey data. Assemblages representing benthic feeders were clearly identified by this method; furthermore, the most heavily exploited species-sizes were decoupled from other species-sizes suggesting that fisheries have diminished their potential to compensate or to be compensated for by competitive interactions. Biomass of species-sizes within known trophic guilds strongly compensated other guild-member biomass fluctuations if the diet of guild members was more specialized. This is an indication that more competitive conditions (more specialization) foster greater compensatory responses between competitors biomass fluctuations.     

Intrauterine programming of ageing

Ageing is an unavoidable corollary to being alive; the most intuitive interpretation of ageing being that it is the consequence of progressive body degeneration. In agreement with this, current models propose that ageing occurs through a stepwise accumulation of DNA damage, which ultimately limits the regenerative capacity of tissues. On the other hand, there is increasing evidence that fetal distress can influence the development of disease in adult life, a phenomenon known as ‘intrauterine programming’. The extent to which an intrauterine exposure to DNA damage can compromise lifespan remains unclear. My group has recently generated a murine model of a human syndrome linked to defective DNA repair and observed that these animals age prematurely, but the accumulation of DNA damage is restricted mostly to the embryonic period. Here, I discuss the implications of this finding and propose that ageing can be influenced by fetal distress.