iTRAQ-based proteomics analysis of autophagy-mediated immune responses against the vascular fungal pathogen Verticillium dahliae in Arabidopsis

The mechanisms underlying the functional link between autophagy and plant innate immunity remain largely unknown. In this study, we investigated the autophagy-mediated plant defense responses against Verticillium dahliae (V. dahliae) infection by comparative proteomics and cellular analyses. An assessment of the autophagy activity and disease development showed that autophagic processes were tightly related to the tolerance of Arabidopsis plant to Verticillium wilt. An isobaric tags for relative and absolute quantification (iTRAQ)-based proteomics analysis was performed, and we identified a total of 780 differentially accumulated proteins (DAPs) between wild-type and mutant atg10-1 Arabidopsis plants upon V. dahliae infection, of which, 193 ATG8-family-interacting proteins were identified in silico and their associations with autophagy were verified for several selected proteins. Three important aspects of autophagy-mediated defense against V. dahliae infection were revealed: 1) autophagy is required for the activation of upstream defense responses; 2) autophagy-mediated mitochondrial degradation (mitophagy) occurs and is an important player in the defense process; and 3) autophagy promotes the transdifferentiation of perivascular cells and the formation of xylem hyperplasia, which are crucial for protection against this vascular disease. Together, our results provide several novel insights for understanding the functional association between autophagy and plant immune responses.     

秦巴山区野生百合(Lilium spp.)的育种应用研究

为探索秦巴山区野生百合资源在百合育种中的应用方向及途径, 获得具有其独特遗传背景的育种材料,将秦巴山区野生百合的抗病毒及独特观赏特性等优良遗传性状逐渐渗到栽培品种中去。该研究借助切割柱头杂交及胚抢救技术,选取6种秦巴山区野生百合［岷江百合(Lilium gegale)、宜昌百合(L. leucanthum)、山丹（L. pumilum）、野百合（L. brownii）、宝兴百合（L. duchartrei）、川百合（L. davidii）］以及亚洲百合（Asiatic hybrids,AA）品种‘Elite’、东方百合（Oriental hybrids,OO）品种‘Sorbonne’、‘Siberia’和‘Marlon’、OT百合（Oriental × Trumpet hybrids,OT）品种‘Yelloween’、‘Serano’、‘Corel′door’进行了32组共计263朵花的（品）种间杂交,并针对膨大变软的果实剥离可供离体培养的胚及胚囊进行胚抢救。结果表明：（1）不同杂交组合坐果率、胚获得及萌发率呈现出较大差异,综合坐果率为11.4%,对30个膨大的果实中共计38个可供离体培养的胚及胚囊进行胚抢救,有7株最终萌发。（2）以野生百合为父母本的12组杂交组合中,6组获得了膨大果实,得到6株杂交后代。（3）栽培百合做母本,野生百合做父本的20组杂交组合中,共9组获得了膨大果实,除‘Elite’ × 山丹可直接收获种子外,共得到1株杂交后代。（4）以岷江百合及宜昌百合为亲本的远缘杂交TT × AA及回交OT × TT成功获得杂种后代。（5）秦巴山区6种野生百合在远缘杂交中获得育种后代的几率存在较大差异,宜昌百合和岷江百合获得后代几率较高,宝兴百合获得后代几率较低,野百合未获得后代。以上结果表明,岷江百合和宜昌百合为母本的TT × AA杂交和宝兴百合为父本的TT × AA杂交以及岷江百合和宜昌百合为父本的OT回交,为三种百合的育种利用提供了新途径,野百合的育种应用途径需要继续探索。     

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.

     

甘南地区紫果云杉、岷江冷杉生命表

本文以径级(胸径间隔)为基础,编制甘南地区紫果云杉、岷江冷杉生命表。 一、研究方法 本项研究于1986和1987年,在甘肃省南部云、冷杉林主要分布地带的白龙江林区和洮河林区进行。选择原始状态下的紫果云杉(Picea purpurea Mast.)、岷江冷杉(Abies faxoniana Rehd.et Wils)林,海拔3000米至3600米之间,位于白龙江中上游;同时选取择伐后的云杉(Picae asperata Mast.)、岷江冷杉林     

Identification of TAF1, HNF4A,and CALM2 as potential therapeutic target genes for liver fibrosis

The molecular mechanism of liver fibrosis caused by hepatitis C virus (HCV) is not clear. The aim of this study is to understand the molecular mechanism of liver fibrosis induced by HCV and to identify potential therapeutic targets for hepatic fibrosis. We analyzed gene expression patterns between high liver fibrosis and low liver fibrosis samples, and identified genes related to liver fibrosis. We identified TAF1, HNF4A, and CALM2 were related to the development of liver fibrosis. HNF4A is important for hepatic fibrogenesis, and upregulation of HNF4A is an ideal choice for treating liver fibrosis. The gene expression of CALM2 is significantly lower in liver fibrosis samples than nonfibrotic samples. TAF1 may serve as a biomarker for liver fibrosis. The results were further validated by an independent data set GSE84044. In summary, our study described changes in the gene expression during the occurrence and development of liver fibrosis. The TAF1, HNF4A, and CALM2 may serve as novel targets for the treatment of liver fibrosis.     

Effects of germination time on seed morph ratio in a seed-dimorphic species and possible ecological significance

Background and Aims Diaspores of heteromorphic species may germinate at different times due to distinct dormancy-breaking and germination requirements, and this difference can influence life history traits. The primary aim of this study was to determine the effect of germination time of the two seed morphs of Suaeda corniculata subsp. mongolica on life history traits of the offspring.Methods Germinated brown and black seeds were sown on the 20th of each month from April to September in a simulated but near-natural habitat of the species. Phenological and vegetative traits of the maternal plants, and number, size and germination percentage of the offspring were determined.Key Results Germinated seeds sown late in the year produced smaller plants that had a higher proportion of non-dormant brown than dormant black seeds, and these brown seeds were larger than those produced by germinated seeds sown early in the year. The length of the seedling stage for brown seeds was shorter than that for black seeds, and the root/shoot ratio and reproductive allocation of plants from brown seeds were more variable than they were for plants from black seeds. Late-germinating brown seeds produced larger plants than late-germinating black seeds.Conclusions Altering the proportion of the two seed types in response to germination timing can help alleviate the adverse effects of delayed germination. The flexible strategy of a species, such as S. corniculata, that produces different proportions of dimorphic seeds in response to variation in germination timing may favour the maintenance and regeneration of the population in its unpredictable environment.     

A New Pathway for Senescence Regulation

<正>Research concerning senescence has become a hotspot since the conception of‘cellular senescence’was put forward by Drs.Hayflick and Moorhead over five decades ago[1].Recently,a paper published in Science by Kang and colleagues,which this article aims to comment on,provides evidence of a new pathway involved in senescence[2].Senescence is a physiological and pathological process induced by numerous factors,during which cell growth ceases