The contribution of mutation and selection to multivariate quantitative genetic variance in an outbred population of Drosophila serrata

Genetic variance is not equal for all multivariate combinations of traits. This inequality, in which some combinations of traits have abundant genetic variation while others have very little, biases the rate and direction of multivariate phenotypic evolution. However, we still understand little about what causes genetic variance to differ among trait combinations. Here, we investigate the relative roles of mutation and selection in determining the genetic variance of multivariate phenotypes. We accumulated mutations in an outbred population of Drosophila serrata and analyzed wing shape and size traits for over 35,000 flies to simultaneously estimate the additive genetic and additive mutational (co)variances. This experimental design allowed us to gain insight into the phenotypic effects of mutation as they arise and come under selection in naturally outbred populations. Multivariate phenotypes associated with more (less) genetic variance were also associated with more (less) mutational variance, suggesting that differences in mutational input contribute to differences in genetic variance. However, mutational correlations between traits were stronger than genetic correlations, and most mutational variance was associated with only one multivariate trait combination, while genetic variance was relatively more equal across multivariate traits. Therefore, selection is implicated in breaking down trait covariance and resulting in a different pattern of genetic variance among multivariate combinations of traits than that predicted by mutation and drift. Overall, while low mutational input might slow evolution of some multivariate phenotypes, stabilizing selection appears to reduce the strength of evolutionary bias introduced by pleiotropic mutation.

The presence of genetic variation in natural populations is a basic tenet of biology, and yet we still understand very little about how evolutionary processes interact to maintain genetic variance (1). One reason this presents such a difficult challenge is the ubiquitous, but poorly understood, nature of pleiotropy (2). The presence of pleiotropy substantially changes two fundamental aspects of genetic variation: the relative magnitude of genetic variance associated with multivariate trait combinations (3), and the efficacy of processes such as mutation–selection balance in maintaining genetic variation (4–9).While genetic variation in individual traits is ubiquitous (6, 10), the genetic variance of multivariate trait combinations gives a very different picture (3, 11). For sets of morphological, behavioral, or life-history traits, genetic variation tends to be concentrated in a few multivariate trait combinations, leaving other trait combinations with very little genetic variation (11, 12). This inequality in the magnitude of multivariate genetic variances plays a crucial role in determining short-term evolutionary responses (13–15). However, the genetic (co)variance matrix, G, itself can evolve (e.g., ref. 16), and understanding whether G is a good predictor of divergence over longer evolutionary timescales (17) ultimately depends on understanding how evolutionary processes shape G.Empirically, the contributions of mutation and selection to shaping G can be inferred by comparing G to M, the mutational (co)variance matrix, or comparing G to γ, the matrix describing multivariate nonlinear selection. If G is shaped by mutation and drift, then G should be proportional to M, and multivariate traits with relatively large mutational variances will have relatively large genetic variances, although individual populations are likely to deviate from this null expectation due to the sampling variance of the evolutionary process itself (18, 19). If new mutations have similar pleiotropic effects to standing genetic variants, then mutation might act to stabilize G (20, 21) and constrain evolution over the long-term (17). Conversely, if G is primarily shaped by selection, then G should be proportional to γ, and the long-term stability of G depends on the stability of the adaptive landscape (22). Under some genetic architectures, long-term consistency of γ might lead to the evolution of M and result in the proportionality of γ and M and the long-term stasis of G (23, 24).The magnitude of mutational correlation among pairs of life-history traits (e.g., refs. 25 to 26), as well as multivariate analyses of larger trait sets (e.g., refs. 27 and 28), suggest that mutational variance, like genetic variance, may typically vary markedly in magnitude among different trait combinations. However, few studies have directly compared G to M to infer the roles of mutation and selection in shaping G. Standing genetic correlations among life-history traits tend to be less positive than mutational correlations (e.g., refs. 29 and 30). Similarly, some multivariate analyses suggest differences between G and M. For example, Latimer et al. (31, 32) showed that there was little additive genetic variance along the major axis of mutational variance in Drosophila serrata thermal activity curves, implicating selection in shaping G. Similarly, Camara et al. (33) demonstrated that, as mutation rate (higher mutagen dose) increased in Arabidopsis thaliana, the phenotypic covariance matrices became increasingly dissimilar between the ancestral and mutated populations. In contrast, Houle et al. (17) found G and M to be markedly similar for wing traits in Drosophila melanogaster. Moreover, M predicted patterns of wing divergence across drosophilids (17), suggesting a major role for mutation in determining long-term adaptation, as well as G.Inference of selection via comparison of standing genetic to mutational variance is difficult for several reasons. Classical inbred-line experimental designs for estimating mutational parameters cannot provide estimates of standing genetic variance, and, while outbred populations provide estimates of standing genetic variance, it has proven difficult to estimate mutational variance in these populations (34). Thus, differences between G and M might arise due to the varying experimental conditions under which each is measured. Furthermore, the majority of studies estimating M have utilized inbred mutation accumulation (MA) lines or mutagens. Whether these studies are representative of the contribution of naturally occurring mutations to standing genetic variance in outbred populations is unknown. Wray (35) proposed a modification of the animal (mixed) model to account for the effect of mutations, allowing for the simultaneous estimation of standing genetic and mutational variance in an outbred population. This approach provides a way of estimating both G and M from the same sample of outbred individuals, allowing for a direct comparison of G and M without differences in experimental conditions, populations, or levels of inbreeding, that have, to date, confounded most comparisons of genetic and mutational variances (36). The substantial potential of Wray’s (35) mixed model has not yet been widely appreciated, with only three studies applying the method, each reporting estimates for individual traits only (36–38).Here, we implement Wray’s (35) approach in a multivariate model to simultaneously estimate G and M for a set of wing traits in an outbred population of D. serrata. We maintained the population under a middle-class neighborhood (MCN) breeding design, where the population size is fixed and family sizes are equalized, thereby minimizing between-family selection and allowing nonlethal mutations to accumulate in the population via random genetic drift (39). McGuigan et al. (36) demonstrated the utility of combining an MCN breeding design with Wray’s (35) approach, but their study presented estimates for individual traits only. In this paper, we report results from a large MCN population, comprising nearly 48,000 individuals from ∼7,900 families (

肺隐球菌病的CT表现

目的 分析肺隐球菌病的CT表现，提高对该病的认识。方法 回顾分析1980年1月至2003年2月收治经病理或细菌学证实的21例肺隐球菌病的CT表现，其中有基础疾患史者9例。结果 CT表现：(1)孤立病变11例，多发单叶病变5例，双侧病变5例。(2)结节团块型16例，共32个病灶，直径最小1cm，最大10cm。空洞型1例；大叶实变型2例；弥漫混合病变2例。(3)21例中伴少量胸水1例，肺门或纵隔淋巴结肿大4例。结论 肺隐球菌病的CT表现各异，形态多样，影像诊断困难，及时行病原菌、病理学检查是提高早期诊断率的关键。     

表皮生长因子含量表达与骨折愈合的关系

目的:观察骨折后表皮生长因子含量表达的变化,分析表皮生长因子浓度变化与骨折愈合之间的关系。方法:实验于2003-10在山东大学齐鲁医院动物实验室完成。选用成年雄性家兔30只,以随机数字表法分成骨折固定组、骨折组、创伤组,各10只。骨折固定组制作左第一跖骨骨折模型,然后用管型石膏将左下肢固定;骨折组造模后不给予任何固定;创伤组仅用止血钳在家兔左大腿的中部钳夹1次。在造模前、造模后24,48,96h,2,4周,分别采静脉血,采用放射免疫分析法对家兔血清表皮生长因子浓度进行测定,进行组间、组内对照,观察骨折对家兔血清表皮生长因子浓度变化的影响。并通过X射线检测骨折愈合的情况,对家兔血清表皮生长因子浓度升高是否影响骨折愈合的速度进行评估。结果:所有30只实验动物均纳入实验动物数量分析,无脱失。①骨折固定组与骨折组骨折后24h血清表皮生长因子浓度开始升高[(45.98±3.36),(43.64±3.11)μg/L];到48h达到高峰[(51.02±3.11),(49.31±2.94)μg/L];96h已开始下降[(47.18±5.08),(45.41±4.73)μg/L];2～4周可维持较正常稍高水平[(43.50±3.78),(39.15±4.20)μg/L];4周时接近正常值[(42.26±3.14),(37.64±3.93)μg/L]。②骨折后24,48,96h骨折固定组、骨折组与创伤组家兔血清表皮生长因子浓度差异均有显著性意义(P<0.05);此间骨折固定组和骨折组差异无显著性意义(P>0.05)。③X射线检查结果,4周时骨折固定组愈合5例;骨折组愈合2例。6周时骨折固定组愈合8例;骨折组愈合5例。结论:骨折可导致家兔血清表皮生长因子浓度的升高,高表皮生长因子浓度可能有利于骨折的愈合。     

基因治疗与心力衰竭

目的:探讨基因治疗方案的有效性和安全性。资料来源:应用计算机检索PUBMED1980-01/2006-10和EMCC 1994-10/2006-10与心力衰竭的基因治疗相关文章,检索词“Gen Therapy,Heart Failure”,并限定文章语言种类为“English”;同时计算机检索CMCC 1994-01/2006-10的相关文章,限定文章语言种类为中文,检索词“基因治疗、心力衰竭”。资料选择:对资料进行初审,选取试验包括上述治疗组和对照组的文献,然后筛除明显不随机临床试验的研究,对剩余的文献开始查找全文,进一步判断是否为RCT。纳入标准为:①试验包含平行对照组,即一般药物治疗。②治疗组为转基因治疗。资料提炼:共收集到49篇相关的随机和未随机试验,31个试验符合纳入标准。其余的排除。资料综合:31个试验包括478例动物模型,分别对应用不同靶点的转基因或基因敲除等方案进行治疗者予以评价。31篇文章中有10篇未达到预期的治疗目标,主要是在目的基因的选择及转染载体的选择方面出现了偏差。结论:虽动物试验取得了一定的成果,但尚无人体基因治疗的文献报道,随着基因技术的进步,基因治疗有望成为治疗心力衰竭的新途径。     

自体游离骨膜移植修复儿童髋关节软骨大面积缺损：动物实验及临床应用

目的:通过自体游离骨膜移植修复儿童髋关节软骨大面积缺损的实验研究和临床应用,观察关节软骨缺损修复的组织形态学变化和临床应用疗效。方法:①动物实验:实验于2002-03/10在南京医科大学附属南京儿童医院儿科研究所完成。选用新西兰幼兔24只,将幼兔股骨头全层关节软骨用利刀切除其表面积的20%以上,制造髋关节软骨大面积缺损模型。按随机数字表法分为2组,每组12只,自体游离骨膜移植组取同侧股骨全层游离骨膜,将骨膜生发层朝向关节腔移植于软骨缺损区;对照组仅同法切除股骨头关节软骨。分别于术后第4,8,12,24周取其股骨头制成标本,对关节软骨缺损修复情况进行大体及组织形态学观察,并用Wston-blot法检测修复组织中Ⅱ型胶原蛋白的表达情况。②临床实验:选择2000-01/2005-06在南京医科大学附属南京儿童医院骨科手术治疗髋关节脱位时,采用自体游离骨膜移植修复髋关节软骨大面积缺损的患儿39例(48髋),监护人均知情同意。术后定期随访检查。髋关节脱位术后疗效根据临床功能及X射线检查结果进行评定:积分16-20分为优,11-15分为良,6-10分为可,<5分为差。结果:①动物实验:自体游离骨膜移植组术后第4周幼兔股骨头软骨缺损被光滑、不透明的类软骨组织替代;12周后软骨缺损由透明的软骨样组织修复;24周后则完全被近似正常的透明软骨修复。对照组术后24周股骨头软骨缺损仍由纤维样组织覆盖。自体游离骨膜移植组术后第4周起软骨缺损修复组织中Ⅱ型胶原蛋白呈持续高表达。②临床实验:39例患儿48髋均获随访,随访18-36个月18例22髋,37-54个月14例16髋,55-72个月7例10髋,平均32个月。髋关节脱位术后疗效优19例25髋(52.1%);良12例10髋(25.0%);可7例8髋(16.7%);差3例3髋(6.2%),优良率达77.1%。结论:动物实验结果显示幼兔自体游离骨膜移植3个月后基本完成了游离骨膜向关节软骨的分化,可为临床应用提供客观依据。临床实验证实,在手术治疗儿童髋关节脱位时,移植自体游离骨膜修复关节软骨大面积缺损,能够提高手术疗效。     

冠状动脉粥样硬化性心脏病患者红细胞流变性特征在红花注射液干预后的表现

目的:经红花注射液干预后,观察冠状动脉粥样性心脏病患者红细胞电泳指标及红细胞变形性的变化。方法:①选取2001-02/2002-10河北北方学院附属第一医院心内科住院的冠状动脉粥样硬化性心脏病患者50例作为冠状动脉粥样硬化性心脏病组,男31例,女19例,平均年龄(43.9±5.6)岁,对本实验知情同意,均自愿参加。②纳入标准:经心电图、超声心动图证实有典型心绞痛、心绞痛不典型或陈旧性心肌梗死史,由专科医师确诊冠脉造影狭窄率≥50%;治疗前均未进行降脂、利尿剂、促血尿酸排泄药、阿司匹林及肝素(包括低分子肝素钠)治疗;无肝肾疾病、内分泌系统疾病。③以30例自愿体检受试的健康学生及医务人员作为正常对照组,男女各15例,平均年龄(35.4±7.4)岁。两组间年龄、性别差异无显著性意义。④“得强”红花注射液,主要成分为红花黄色素,由太原华卫药业有限公司生产,批号010302。⑤冠状动脉粥样硬化性心脏病组取红花注射液6.0～8.0mL,溶于500mL葡萄糖注射液中,静点,1次/d,15d为1个疗程,共1个疗程。正常对照组未给予任何干预措施。⑥治疗前后两组空腹抽取肘静脉血5mL,应用红细胞变形分析仪检测红细胞电泳指标及红细胞变形性的变化。结果:50例冠状动脉粥样硬化性心脏病患者均进入结果分析。①治疗前后红细胞电泳指标的变化:与正常对照组比较,冠状动脉粥样硬化性心脏病组治疗前红细胞电泳时间显著延长,红细胞电泳长度与红细胞迁移率显著降低(t=3.198～6.963,P均<0.01);治疗后红细胞电泳时间缩短,红细胞电泳长度与红细胞迁移率增加(t=2.212～3.672,P<0.05或0.01),但仍与正常对照组存在显著性差异(t=1.700～4.792,P<0.05或0.01)。②治疗前后不同切变率下红细胞变形性变化:与正常对照组比较,冠状动脉粥样硬化性心脏病组治疗前在300S-1,230S-1,115S-1切变率下的红细胞变形性均显著降低(t=1.956～2.459,P<0.05或0.01);治疗后各切变率下的红细胞变形性均显著增强(t=3.162～4.187,P均<0.01),且在230S-1、115S-1切变率下冠状动脉粥样硬化性心脏病组红细胞变形性显著高于正常对照组(t=2.089～2.748,P<0.05或0.01)。结论:冠状动脉粥样硬化性心脏病患者给予红花注射液后,红细胞电泳时间、电泳长度及迁移率均有所好转,且不同切变率下的红细胞变形性亦有所增强,提示红花注射液对冠状动脉粥样硬化性心脏病的干预与改善红细胞流变性异常有关。     

不同产地和品种淫羊藿中淫羊藿苷的HPLC分析

目的 :对中药淫羊藿中淫羊藿苷的含量分析进行方法学研究 ,并测定淫羊藿的不同产地、不同品种、不同药用部位的淫羊藿苷的含量。方法 :采用RP HPLC技术对 7个品种 2 3个样品进行测定。以PERKIN EIMER SH/5C18柱为分析柱 ,流动相为乙腈 水 36%乙酸 ( 2 5∶73.5∶1 .5)流速 1 .0ml/min。UV检测波长 2 70nm。结果 :本方法测定淫羊藿苷含量在 0 .0 2 1 2～ 0 .1 2 7μg ,0 .53～ 1 .696μg范围内呈良好的线性关系。不同产地、不同品种、不同药用部位的淫羊藿其淫羊藿苷的含量为 0 .0 0 30 7%～ 1 .55%。同一植株不同部位中淫羊藿苷的含量分布叶 >叶茎 >茎 >根。结论 :淫羊藿由于产地不同、品种不同其淫羊藿苷的含量相差悬殊。本测定方法为筛选优良品种和扩大药源提供了简便易行的方法     

大豆甙元—PVP共沉淀物的制备及其体外溶出度的研究

以乙醇为溶媒用溶剂蒸发法制备了大豆甙元—PVP共沉淀物。X-衍射分析和偏光显微镜观察的结果表明,形成共沉淀物所需的PVP比例应大于5:1。体外溶出度试验结果表明,共沉淀物可以显著提高大豆甙元的体外溶出度。大豆甙元:PVP(1:10)的共沉淀物制成片剂后,在1 h内,90%以上的大豆甙元释放于蒸馏水或人工肠液中。