Heritability and components of phenotypic expression in skin reflectance of Mestizos from the Peruvian Lowlands

Skin reflectance was measured on the inner upper arm and forehead of a sample of 209 Mestizos ranging in age from 2 to 64 years living in the town of Lamas in the Eastern Peruvian Lowlands. The sample consisted of 43 father-son, 42 father-daughter, 62 mother-son, and 70 mother-daughter pairs. The sample also consisted of 57 brother-brother, 60 sister-sister and 139 brother-sister pairs. The reflectance measurements were made with a Photovolt Reflection Meter, model 670. Stepwise polynomial regression techniques were used to derive standardized residual values. Then using these residual values parent-offspring, sibling intraclass correlations and components of the phenotypic expression of skin reflectance were calculated. The study indicates that 1) the parent-offspring and sibling correlation coefficients conformed with the theoretical correlations expected assuming polygenic inheritance; 2) the husband-wife correlations indicate a high degree of assortative mating for skin color, but despite this effect the parent-offspring and sibling correlation coefficients are lower than the values expected under the influence of autosomal genes; 3) estimates of heritability and components of phenotypic expression indicate that about 55% of the total variability in skin reflectance could be attributed to the influence of additive genetic factors; and 4) there is no evidence of X-linkage in the inheritance of skin color.     

Differential effects of phosphotyrosine phosphatase expression on hormone-dependent and independent pp60 c-src activity

pp60 ^c-src kinase activity can be increased by phosphotyrosine dephosphorylation or growth factor-dependent phosphorylation reactions. Expression of the transmembrane phosphotyrosine phosphatase (PTPase) CD45 has been shown to inhibit growth factor receptor signal transduction (Mooney, RA, Freund, GG, Way, BA and Bordwell, KL (1992) J Biol Chem 267, 23443–23446). Here it is shown that PTPase expression decreased platelet-derived growth factor (PDGF)-dependant activation of pp60 ^c-src but failed to increase hormone independent (basal) pp60 ^c-src activity. PDGF-dependent tyrosine phosphorylation of its receptor was reduced by approximately 60% in cells expressing the PTPase. In contrast, a change in phosphotyrosine content of pp60 ^c-src was not detected in response to PDGF or in PTPase+cells. PDGF increased the intrinsic tyrosine kinase activity of pp60 ^c-src in both control and PTPase+cells but the effect was smaller in PTPase+cells. In anin vitro assay, hormone-stimulate pp60 ^c-src autophosphorylation from PTPase+ cells was decreased 64±22%, and substrate phosphorylation by pp60 ^c-src was reduced 54±16% compared to controls. Hormone-independent pp60 ^c-src kinase activity was unchanged by expression of the PTPase. pp60 ^c-src was, however, anin vitro substrate for CD45, being dephosphorylated at both the regulatory (Tyr⁵²⁷) and kinase domain (Tyr⁴¹⁶) residues. In addition,in vitro dephosphorylation by CD45 increased pp60 ^c-src activity. These findings suggest that the PDGF receptor was anin vivo substrate of CD45 but pp60 ^c-src was not. The lack of activation of pp60 ^c-src in the presence of expressed PTPase may demonstrate the importance of compartmentalization and/or accessory proteins to PTPase-substrate interactions.Abbreviations PTPase phosphotyrosine phosphatase - PDGF platelet-derived growth factor - PMSF phenylmethylsulfonyl fluoride - LCA, CD45 leukocyte common antigen - PBS phosphate buffered saline - SDS sodium dodecyl sulfate - PAGE polyacrylamide gel electrophoresis - DTT dithiothreitol - Na₃VO₄ sodium orthovanadate - PV pervanadate - -ME -mercaptoethanol     

The induction of preterm labor in rhesus macaques is determined by the strength of immune response to intrauterine infection

Intrauterine infection/inflammation (IUI) is a major contributor to preterm labor (PTL). However, IUI does not invariably cause PTL. We hypothesized that quantitative and qualitative differences in immune response exist in subjects with or without PTL. To define the triggers for PTL, we developed rhesus macaque models of IUI driven by lipopolysaccharide (LPS) or live Escherichia coli. PTL did not occur in LPS challenged rhesus macaques, while E. coli–infected animals frequently delivered preterm. Although LPS and live E. coli both caused immune cell infiltration, E. coli–infected animals showed higher levels of inflammatory mediators, particularly interleukin 6 (IL-6) and prostaglandins, in the chorioamnion-decidua and amniotic fluid (AF). Neutrophil infiltration in the chorio-decidua was a common feature to both LPS and E. coli. However, neutrophilic infiltration and IL6 and PTGS2 expression in the amnion was specifically induced by live E. coli. RNA sequencing (RNA-seq) analysis of fetal membranes revealed that specific pathways involved in augmentation of inflammation including type I interferon (IFN) response, chemotaxis, sumoylation, and iron homeostasis were up-regulated in the E. coli group compared to the LPS group. Our data suggest that the intensity of the host immune response to IUI may determine susceptibility to PTL.     

Mortality event in freshwater drum Aplodinotus grunniens from Lake Ontario, Canada, associated with viral haemorrhagic septicemia virus, type IV

A mortality event primarily affecting freshwater drum Aplodinotus grunniens was noted during April and May 2005 in the Bay of Quinte, Lake Ontario, Canada. A conservative estimate of the number of dead drum was approximately 100 metric tonnes. Large numbers of dead round goby Neogobius melanostomus were also seen, as well as a few muskellunge Esox masquinongy. In the drum, there was a consistent histological pattern of variably severe panvasculitis, a necrotising myocarditis, meningoencephalitis and a segmental enteritis. Moderate numbers of bullet-shaped viral particles consistent with a rhabdovirus were identified by transmission electron microscopy (TEM) in affected heart tissue. Following primary isolation from pooled tissues on fathead minnow (FHM) cells, a morphologically similar virus, approximately 165 x 60 nm in size, was visualised. Identification of the isolate as viral haemorrhagic septicemia virus (VHSV) was confirmed by enzyme immunoassay and by polymerase chain reaction. An appropriately sized product (468 bp) of the G-glycoprotein gene (nucleotides [nt] 340 to 807) was generated with RNA extracted from FHM cell supernatant. Analysis of a 360 nt partial glycoprotein gene sequence (nt 360 to 720) indicated a 96.4 to 97.2% nucleotide identity with known strains of North American (NA) VHSV. Analysis using Neighbour-joining distance methods assigned the isolate to the same lineage as the NA and Japanese isolates (Genogroup IV). However, there was sufficient sequence divergence from known NA VHSV isolates to suggest that this isolate may represent a distinct subgroup. The effects of ongoing mortality in freshwater drum and in multiple species during spring 2006 suggest that this newly recognised virus in the Great Lakes will have continued impact in the near future.     

两种标记方法对60mer寡核苷酸芯片背景信号影响的初步分析

研究两种不同的样本标记方法对人全基因组高密度60mer寡核苷酸芯片背景信号的影响。收集5对患病与健康人外周血单个核细胞,分别提取总RNA后,采用限制性显示技术（restriction display,RD）进行样本双色（Cy3/Cy5）荧光标记,与5张Agilent 60mer高密度（22K）Human 1B寡核苷酸芯片进行杂交。芯片全部杂交点分3组：基因探针组、阳性对照组和阴性对照组。阳性对照采用荧光标记寡核苷酸直接掺入法进行标记。对全部杂交信号点的Cy3和Cy5背景信号值,用SPSS软件进行数据转换、正态性检验、方差齐性检验、变异系数分析和重复数据的方差分析。数据分析结果显示,Cy3 标记的背景信号值均高于 Cy5标记的背景信号值。重复测量数据的方差分析表明,在Cy3 和Cy5标记中,两种不同标记方法间的背景信号值的差异极显著（PCy3＜0.01, PCy5＜0.01）,且RD标记点的背景信号平均值低于荧光标记寡核苷酸直接掺入标记法标记的阳性对照点。RD标记方法是一种有用的低背景信号的高密度长链寡核苷酸芯片样本标记方法。     

一类捕食-被捕食控制反应扩散方程非线性奇摄动问题

研究了一类非线性反应扩散方程奇摄动问题．在适当的条件下,首先求出了原问题的外部解, 然后利用伸长变量和幂级数展开理论构造出解的形式渐近展开式．最后利用微分不等式理论,讨论了问题解的一致有效性和渐近性态．     

The Origin of the White Roman Goose

In order to avoid interference from nuclear copies of mitochondrial DNA (numts), mtDNA of the white Roman goose (domestic goose) was extracted from liver mitochondria. The mtDNA control region was amplified using a long PCR strategy and then sequenced. Neighbor-joining, maximum parsimony, and maximum-likelihood approaches were implemented using the 1,177 bp mtDNA control region sequences to compute the phylogenetic relationships of the domestic goose with other geese. The resulting identity values for the white Roman geese were 99.1% (1,166/1,177) with western graylag geese and 98.8% (1,163/1,177) with eastern graylag geese. In molecular phylogenetic trees, the white Roman goose was grouped in the graylag lineage, indicating that the white Roman goose came from the graylag goose (Anser anser). Thus, the scientific name of the white Roman goose should be Anser anser ‘White Roman.’     

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.

     

鼎湖山南亚热带常绿阔叶林碳素积累和分配特征

研究了鼎湖山南亚热带常绿阔叶林 40 0多年林龄的锥栗 (Castanopsis chinensis)、黄果厚壳桂 (Crypto-carya concinna)和 50多年林龄的黄果厚壳桂、鼎湖钓樟 (Lindera chunii)两个群落碳素积累和分配特征。结果表明 ,两林分间植物碳素含量在不同器官和不同层中的分配格局均十分相似 ,总平均分别为 41 .980 %(锥栗、黄果厚壳桂群落 )和 40 .377% (黄果厚壳桂、鼎湖钓樟群落 )。锥栗、黄果厚壳桂群落生态系统碳总贮量为 2 4 4 .998t/ hm2 ,其中植被部分为 1 54.2 89t/ hm2 ,土壤为 89.1 2 8t/ hm2 ,地表凋落物层为 1 .581 t/ hm2。黄果厚壳桂、鼎湖钓樟群落植被碳总贮量为 84.1 51 t/ hm2。在两林分植被碳总贮量中 ,乔木层分别占了97.47% (锥栗、黄果厚壳桂群落 )和 98.0 4 % (黄果厚壳桂、鼎湖钓樟群落 ) ,而在乔木层碳总贮量中 ,干器官则分别占 47.93% (锥栗、黄果厚壳桂群落 )和 44.66% (黄果厚壳桂、鼎湖钓樟群落 )。锥栗、黄果厚壳桂群落植被碳年积累量为 3.1 4 9t/ (hm2· a) ,黄果厚壳桂、鼎湖钓樟群落植被碳年积累量则为 3.42 5 t/ (hm2· a)。     

Overexpression of Arabidopsis acyl‐CoA‐binding protein ACBP2 enhances drought tolerance

Arabidopsis thaliana acyl‐CoA‐binding protein 2 (ACBP2) is a stress‐responsive protein that is also important in embryogenesis. Here, we assign a role for ACBP2 in abscisic acid (ABA) signalling during seed germination, seedling development and the drought response. ACBP2 was induced by ABA and drought, and transgenic Arabidopsis overexpressing ACBP2 (ACBP2‐OXs) showed increased sensitivity to ABA treatment during germination and seedling development. ACBP2‐OXs also displayed improved drought tolerance and ABA‐mediated reactive oxygen species (ROS) production in guard cells, thereby promoting stomatal closure, reducing water loss and enhancing drought tolerance. In contrast, acbp2 mutant plants showed decreased sensitivity to ABA in root development and were more sensitive to drought stress. RNA analyses revealed that ACBP2 overexpression up‐regulated the expression of Respiratory Burst Oxidase Homolog D (AtrbohD) and AtrbohF, two NAD(P)H oxidases essential for ABA‐mediated ROS production, whereas the expression of Hypersensitive to ABA1 (HAB1), an important negative regulator in ABA signalling, was down‐regulated. In addition, transgenic plants expressing ACBP2pro:GUS showed beta‐glucuronidase (GUS) staining in guard cells, confirming a role for ACBP2 at the stomata. These observations support a positive role for ACBP2 in promoting ABA signalling in germination, seedling development and the drought response.