Oplopane Sesquiterpenes from Ligularia knorringiana and Their Anti‐Complementary Activity

Three new oplopane sesquiterpenes, knorringianalarins D – F ( 1 – 3 , respectively), and five known analogues ( 4 – 8 , respectively), were isolated from the roots and rhizomes of Ligularia knorringiana. The structures of three new compounds were identified as 4‐acetoxy‐11α,12‐epoxy‐2β‐hydroxy‐3β‐(2‐methylbutyryloxy)‐9α‐(4‐methylsenecioyloxy)oplop‐10(14)‐ene ( 1 ), 3β,4‐diacetoxy‐9α‐(4‐acetoxy‐4‐methylsenecioyloxy)‐11α,12‐epoxy‐8α‐(2‐methylbutyryloxy)oplop‐10(14)‐ene ( 2 ), and (1R,5R,6R,7R,9R)‐5,9,11‐trihydroxy‐4,15‐dinoroplop‐10(14)‐en‐3‐one ( 3 ) based on spectroscopic methods including 1D‐ and 2D‐NMR, mass spectrometry, and CD spectroscopy techniques. All compounds were evaluated for their anti‐complementary activity on the classical pathway of the complement system in vitro. Among which, three oplopane sesquiterpenes ( 3 , 7 , and 8 ) exhibited better anti‐complementary effects with CH₅₀ values ranging from 0.33 to 0.89 mm , which are plausible candidates for developing potent anti‐complementary agents.     

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.     

Genome-wide association study of <Emphasis Type="Italic">Mycobacterium avium</Emphasis> subspecies <Emphasis Type="Italic">Paratuberculosis</Emphasis> infection in Chinese Holstein

Background

Paratuberculosis is a contagious, chronic and enteric disease in ruminants, which is caused by Mycobacterium avium subspecies paratuberculosis (MAP) infection, resulting in enormous economic losses worldwide. There is currently no effective cure for MAP infection or a vaccine, it is thus important to explore the genetic variants that contribute to host susceptibility to infection by MAP, which may provide a better understanding of the mechanisms of paratuberculosis and benefit animal genetic improvement. Herein we performed a genome-wide association study (GWAS) to identify genomic regions and candidate genes associated with susceptibility to MAP infection in dairy cattle.

Results

Using Illumina Bovine 50?K (54,609 SNPs) and GeneSeek HD (138,893 SNPs) chips, two analytical approaches were performed, GRAMMAR-GC and ROADTRIPS in 937 Chinese Holstein cows, among which individuals genotyped by the 50?K chip were imputed to HD SNPs with Beagle software. Consequently, 15 and 11 significant SNPs (P?<?5?×?10^??5) were identified with GRAMMAR-GC and ROADTDRIPS, respectively. A total of 10 functional genes were in proximity to (i.e., within 1?Mb) these SNPs, including IL4, IL5, IL13, IRF1, MyD88, PACSIN1, DEF6, TDP2, ZAP70 and CSF2. Functional enrichment analysis showed that these genes were involved in immune related pathways, such as interleukin, T cell receptor signaling pathways and inflammatory bowel disease (IBD), implying their potential associations with susceptibility to MAP infection. In addition, by examining the publicly available cattle QTLdb, a previous QTL for MAP was found to be overlapped with one of regions detected currently at 32.5?Mb on BTA23, where the TDP2 gene was anchored.

Conclusions

In conclusion, we identified 26 SNPs located on 15 chromosomes in the Chinese Holstein population using two GWAS strategies with high density SNPs. Integrated analysis of GWAS, biological functions and the reported QTL information helps to detect positional candidate genes and the identification of regions associated with susceptibility to MAP traits in dairy cattle.

     

Quantitative analysis of mitral valve morphology in atrial functional mitral regurgitation using real-time 3-dimensional echocardiography atrial functional mitral regurgitation

Background

Atrial fibrillation (AF) can result in atrial functional mitral regurgitation (MR), but the mechanism remains controversial. Few data about the relationship between the 3-dimensional morphology of the MV and the degree of MR in AF exist.

Methods

Real-time 3-dimensional transesophageal echocardiography (3D-TEE) of the MV was acquired in 168 patients with AF (57.7% persistent AF), including 25 (14.9%) patients with moderate to severe MR (the MR+ group) and 25 patients without AF as controls. The 3-dimensional geometry of the MV apparatus was acquired using dedicated quantification software.

Results

Compared with the group of patients with no or mild MR (the MR- group) and the controls, the MR+ group had a larger left atrium (LA), a more dilated mitral annulus (MA), a reduced annular height to commissural width ratio (AHCWR), indicating flattening of the annular saddle shape, and greater leaflet surfaces and tethering. MR severity was correlated with the MA area (r²?=?0.43, P?<?0.01) and the annulus circumference (r²?=?0.38, P?<?0.01). A logistic regression analysis indicated that the MA area (OR: 1.02, 95% CI: 1.01–1.03, P?<?0.01), AHCWR (OR: 0.24, 95% CI: 0.14–0.35, P?=?0.04) and MV tenting volume (OR: 3.24, 95% CI: 1.16–9.08, P?=?0.03) were independent predictors of MR severity in AF patients.

Conclusions

The mechanisms of “atrial functional MR” are complex and include dilation of the MA, flattening of the annular saddle shape and greater leaflet tethering.

     

凡纳滨对虾肠道红杆菌科细菌富集碳源筛选及其定向分离

【目的】红杆菌科(Rhodobacteraceae)细菌为凡纳滨对虾肠道微生物的优势类群,在健康对虾肠道中具有较高的相对丰度,是指示对虾健康的关键类群,探究对虾肠道红杆菌科细菌定向富集和分离方法,可为对虾养殖益生菌菌剂的研发提供基础。【方法】利用16S rRNA基因高通量测序技术研究不同碳源添加对凡纳滨对虾肠道中红杆菌科细菌的富集作用,筛选对红杆菌科细菌有显著富集作用的碳源;利用纯培养技术从红杆菌科细菌富集的样品中定向分离红杆菌科细菌,并对其进行鉴定和遗传多样性分析。【结果】添加短链脂肪酸(乙酸、丙酸、丁酸、戊酸)和碳酸氢钠对红杆菌科细菌有显著富集作用,主要富集到Cribrihabitans、Tritonibacter、Rhodovulum、Ruegeria、Sagittula和Thalassobius属相关菌株;对红杆菌科细菌相对丰度最高的样品进行稀释涂布培养,共分离纯化出303株细菌,分属于2门12科,其中红杆菌科细菌为主导类群共119株,主要包括Tritonibacter (90株)、Phaeobacter (25株)、Sulfitobacter (1株)、Ruegeria (1...     

Investigation of fullerenol-induced changes in poroelasticity of human hepatocellular carcinoma by AFM-based creep tests

In this study, atomic force microscopy (AFM) is used to investigate the alterations of the poroelastic properties of hepatocellular carcinoma (SMMC-7721) cells treated with fullerenol. The SMMC-7721 cells were subject to AFM-based creep tests, and a corresponding poroelastic indentation model was used to determine the poroelastic parameters by curve fitting. Comparative analyses indicated that the both permeability and diffusion of fullerenol-treated cells increased significantly while their elastic modulus decreased by a small amount. From the change in the trend of the determined parameter, we verified the corresponding alternations of cytoskeleton (mainly filaments actin), which was reported by the previous study using confocal imaging method. Our investigation on SMMC-7721 cell reveals that the poroelastic properties could provide a better understanding how the cancer cells are affected by fullerenol or potentially other drugs which could find possible applications in drug efficacy test, cancer diagnosis and secure therapies.     

Fungal Diversity in Deep Sea Sediments from East Yap Trench and Their Denitrification Potential

Abstract

In recent years, the hadal trenches have been recognized as biological hot spots for deep sea researchers. Due to high hydrostatic pressure, low temperatures, high salinity and low nutrients, the microorganisms in hadal trenches may have unique community structure with potential for biotechnical application. Compared with bacteria and archaea, the diversity and ecological roles of fungi in hadal trenches remain largely unknown. The purpose of this study was to explore fungal diversity in deep-sea sediments of the Yap trench and their denitrification potential. In the present study, a total of 106 fungal strains were isolated from six sediment samples collected in the East Yap Trench. These fungi belonged to five classes (Dothideomycetes, Eurotiomycetes, Sordariomycetes, Cystobasidiomycetes, and Microbotryomycetes), thirteen genera (Acremonium, Alternaria, Aureobasidium, Aspergillus, Cladosporium, Cystobasidium, Engyodontium, Gliomastix, Lecanicillium, Penicillium, Phoma, Rhodotorula and Trichoderma) and eighteen species, based on morphological identification and ITS-rDNA sequence analysis. Among them, the dominant genus is Cladosporium, which accounting for 42.45% of the total fungal strains. Meanwhile, the denitrification potential of the fungal strains was also examined with two different denitrifying media (nitrate and nitrite as sole substrate, respectively). Two fungal strains (Acremonium sp. and Aspergillus versicolor), were found to be able to produce N₂O ex situ in the presence of nitrite. No fungus was found to produce N₂O by using nitrate. Our results suggest that fungi in hadal sediments, play important roles in nitrogen cycles.     

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.