Decomposition Patterns of Foliar Litter and Deadwood in Managed and Unmanaged Stands: A 13-Year Experiment in Boreal Mixedwoods

Litter decomposition is a major driver of carbon (C) and nitrogen (N) cycles in forest ecosystems and has major implications for C sequestration and nutrient availability. However, empirical information regarding long-term decomposition rates of foliage and wood remains rare. In this study, we assessed long-term C and N dynamics (12–13 years) during decomposition of foliage and wood for three boreal tree species, under a range of harvesting intensities and slash treatments. We used model selection based on the second-order Akaike’s Information Criterion to determine which decomposition model had the most support. The double-exponential model provided a good fit to C mass loss for foliage of trembling aspen, white spruce, and balsam fir, as well as aspen wood. These litters underwent a rapid initial phase of leaching and mineralisation, followed by a slow decomposition. In contrast, for spruce and fir wood, the single-exponential model had the most support. The long-term average decay rate of wood was faster than that of foliage for aspen, but not of conifers. However, we found no evidence that fir and spruce wood decomposed at slower rates than the recalcitrant fraction of their foliage. The critical C:N ratios, at which net N mineralisation began, were higher for wood than for foliage. Long-term decay rates following clear-cutting were either similar or faster than those observed in control stands, depending on litter material, tree species, and slash treatment. The critical C:N ratios were reached later and decreased for all conifer litters following stem-only clear-cutting, indicating increased N retention in harvested sites with high slash loads. Partial harvesting had weak effects on C and N dynamics of decaying litters. A comprehensive understanding of the long-term patterns and controls of C and N dynamics following forest disturbance would improve our ability to forecast the implications of forest harvesting for C sequestration and nutrient availability.     

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.

     

Synthesis of a peptidocalix[4]arene library and identification of compounds with hydrolytic activity

A 120 member library of peptidocalix[4]arenes was synthesized and screened for catalysis of the hydrolysis of p-nitrophenyl acetate. His-Ser-His-calix[4]arene was found to catalyze this reaction with v(0)=3.24 x 10(-8)M/s, an increase of 1520% above background and 30% above the tripeptide (His-Ser-His) alone.     

Exercise responsive genes measured in peripheral blood of women with chronic fatigue syndrome and matched control subjects

Background  

Chronic fatigue syndrome (CFS) is defined by debilitating fatigue that is exacerbated by physical or mental exertion. To search for markers of CFS-associated post-exertional fatigue, we measured peripheral blood gene expression profiles of women with CFS and matched controls before and after exercise challenge.     

Natural variation in life history and aging phenotypes is associated with mitochondrial DNA deletion frequency in Caenorhabditis briggsae

Background  

Mutations that impair mitochondrial functioning are associated with a variety of metabolic and age-related disorders. A barrier to rigorous tests of the role of mitochondrial dysfunction in aging processes has been the lack of model systems with relevant, naturally occurring mitochondrial genetic variation. Toward the goal of developing such a model system, we studied natural variation in life history, metabolic, and aging phenotypes as it relates to levels of a naturally-occurring heteroplasmic mitochondrial ND5 deletion recently discovered to segregate among wild populations of the soil nematode, Caenorhabditis briggsae. The normal product of ND5 is a central component of the mitochondrial electron transport chain and integral to cellular energy metabolism.     

The cellular trafficking of the secretory proprotein convertase PCSK9 and its dependence on the LDLR

Mutations in the proprotein convertase PCSK9 gene are associated with autosomal dominant familial hyper- or hypocholesterolemia. These phenotypes are caused by a gain or loss of function of proprotein convertase subtilisin kexin 9 (PCSK9) to elicit the degradation of the low-density lipoprotein receptor (LDLR) protein. Herein, we asked whether the subcellular localization of wild-type PCSK9 or mutants of PCSK9 and the LDLR would provide insight into the mechanism of PCSK9-dependent LDLR degradation. We show that the LDLR is the dominant partner in regulating the cellular trafficking of PCSK9. In cells lacking the LDLR, PCSK9 localized in the endoplasmic reticulum (ER). In cells expressing the LDLR, PCSK9 sorted to post-ER compartments (i.e. endosomes in cell lines and Golgi apparatus in primary hepatocytes), where it colocalized with the LDLR. In cell lines, PCSK9 also colocalized with the LDLR at the cell surface, requiring the presence of the C-terminal Cys/His-rich domain of PCSK9. We provide evidence that PCSK9 promotes the degradation of the LDLR by an endocytic mechanism, as small interfering RNA-mediated knockdown of the clathrin heavy chain reduced the functional activity of PCSK9. We also compared the subcellular localization of natural mutants of PCSK9 with that of the wild-type enzyme in human hepatic (HuH7) cells. Whereas the mutants associated with hypercholesterolemia (S127R, F216L and R218S) localized to endosomes/lysosomes, those associated with hypocholesterolemia did not reach this compartment. We conclude that the sorting of PCSK9 to the cell surface and endosomes is required for PCSK9 to fully promote LDLR degradation and that retention in the ER prevents this activity. Mutations that affect this transport can lead to hyper- or hypocholesterolemia.     

Basal insulin, glucagon, and growth hormone replacement

Conclusions drawn from the pancreatic (or islet) clamp technique (suppression of endogenous insulin, glucagon, and growth hormone secretion with somatostatin and replacement of basal hormone levels by intravenous infusion) are critically dependent on the biological appropriateness of the selected doses of the replaced hormones. To assess the appropriateness of representative doses we infused saline alone, insulin (initially 0.20 mU.kg(-1).min(-1)) alone, glucagon (1.0 ng.kg(-1).min(-1)) alone, and growth hormone (3.0 ng.kg(-1).min(-1)) alone intravenously for 4 h in 13 healthy individuals. That dose of insulin raised plasma insulin concentrations approximately threefold, suppressed glucose production, and drove plasma glucose concentrations down to subphysiological levels (65 +/- 3 mg/dl, P < 0.0001 vs. saline), resulting in nearly complete suppression of insulin secretion (P < 0.0001) and stimulation of glucagon (P = 0.0059) and epinephrine (P = 0.0009) secretion. An insulin dose of 0.15 mU.kg(-1).min(-1) caused similar effects, but a dose of 0.10 mU.kg(-1).min(-1) did not. The glucagon and growth hormone infusions did not alter plasma glucose levels or those of glucoregulatory factors. Thus, insulin "replacement" doses of 0.20 and even 0.15 mU.kg(-1).min(-1) are excessive, and conclusions drawn from the pancreatic clamp technique using such doses may need to be reassessed.     

Genetic variation in C57BL/6 ES cell lines and genetic instability in the Bruce4 C57BL/6 ES cell line

Genetically modified mouse strains derived from embryonic stem (ES) cells are powerful tools for gene function analysis. ES cells from the C57BL/6 mouse strain are not widely used to generate mouse models despite the advantage of a defined genetic background. We assessed genetic variation in six such ES cell lines with 275 SSLP markers. Compared to C57BL/6, Bruce4 differed at 34 SSLP markers and had significant heterozygosity on three chromosomes. BL/6#3 and Dale1 ES cell lines differed at only 3 SSLP makers. The C2 and WB6d ES cell lines differed at 6 SSLP markers. It is important to compare the efficiency of producing mouse models with available C57BL/6 ES cells relative to standard 129 mouse strain ES cells. We assessed genetic stability (the tendency of cells to become aneuploid) in 110 gene-targeted ES cell clones from the most widely used C57BL/6 ES cell line, Bruce4, and 710 targeted 129 ES cell clones. Bruce4 clones were more likely to be aneuploid and unsuitable for ES cell-mouse chimera production. Despite their tendency to aneuploidy and consequent inefficiency, use of Bruce4 ES cells can be valuable for models requiring behavioral studies and other mouse models that benefit from a defined C57BL/6 background. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.