细胞类型特异的遗传学操作：揭开复杂神经环路的面纱

现代神经科学的一个重要课题足阐明复杂神经环路及其细胞组成形成行为的机制。我们希望可以通过对特定神经元群体的区分和操作在引发行为的神经计算和特定神经元群体活性之间建立一种因果联系。运用BAC重组工程技术,我们建立了超过20个“敲入”驱动品系。在这些驱动品系中,Cre或者是可诱导的CreER能够在特定类掣的GABA能细胞中表达。另外,我们还建立了一些Cre报告小鼠品系和一。个基于病毒转染的蛋白表达系统。这些病毒包含一个Cre-激活的表达元件,可以将一些荧光蛋白或分了开关在体内以很高的效率表达。这种基因操作的策略可以使我们进行如下的一些观察和操作：（1）在突触水平观察中间神经元的形态和他们之间的联系;（2）观察中间神经元的活性及其过往的活动;（3）在生理的时间分辨率上操纵特定细胞群的发放和突触传递。这将使我们对复杂神经环路功能和组织的认识进入。个全新的领域。     

植物开花时间的遗传调控通路研究进展

开花时间对植物的繁殖成功至关重要。广泛分布的物种经常发生开花时间的分化, 从而能够更好地适应不同的环境条件。为了探索植物开花行为发生适应性分化的分子机制, 首先要明确调控开花行为的遗传通路。本文梳理了植物各类群调控开花时间的遗传通路, 以期为开花时间适应性分化的分子机制研究提供依据。 植物从营养生长向繁殖转变时, 其开花行为主要受到光照、温度、水分等外界环境因子和赤霉素等内在因素的影响。通过对模式植物拟南芥(Arabidopsis thaliana)和其他类群的研究, 总结出了调控植物开花时间的6条通路, 包括日照长度和光质影响开花的光依赖通路, 长时间冷暴露后促进植物开花的春化通路, 高温或低温环境影响开花的温度通路, 以及赤霉素通路、年龄通路和自主通路3条内部调节过程。植物开花时间调控的6条上游通路信号传递到下游的开花整合基因FT(FLOWERING LOCUS T)和SOC1(SUPPRESSOR OF OVEREXPRESSION OF CONSTANS 1), 整合基因将这些复杂的调节因子整合后进一步传递到下游花分生组织, 从而启动开花。此外, 非编码RNA、转座子对开花时间的调控也具有重要作用。部分遗传通路被证实在植物适应环境的过程中起到了重要作用。目前对植物开花调控的研究已经有一百多年历史, 理论相对成熟。然而, 仍然存在许多具有争议和未解决的问题, 如开花基因的表达方式、开花行为的特殊调控机制、开花时间变异的适应性意义等等, 需要更进一步的研究。     

A population genetic approach to mapping neurological disorder genes using deep resequencing

Deep resequencing of functional regions in human genomes is key to identifying potentially causal rare variants for complex disorders. Here, we present the results from a large-sample resequencing (n = 285 patients) study of candidate genes coupled with population genetics and statistical methods to identify rare variants associated with Autism Spectrum Disorder and Schizophrenia. Three genes, MAP1A, GRIN2B, and CACNA1F, were consistently identified by different methods as having significant excess of rare missense mutations in either one or both disease cohorts. In a broader context, we also found that the overall site frequency spectrum of variation in these cases is best explained by population models of both selection and complex demography rather than neutral models or models accounting for complex demography alone. Mutations in the three disease-associated genes explained much of the difference in the overall site frequency spectrum among the cases versus controls. This study demonstrates that genes associated with complex disorders can be mapped using resequencing and analytical methods with sample sizes far smaller than those required by genome-wide association studies. Additionally, our findings support the hypothesis that rare mutations account for a proportion of the phenotypic variance of these complex disorders.     

A genetic approach to inactivating chemokine receptors using a modified viral protein

We have developed a genetic system, called degrakine, that specifically and stably inactivates chemokine receptors (CKR) by redirecting the ability of the HIV-1 protein, Vpu, to degrade CD4 in the endoplasmic reticulum (ER) via the host proteasome machinery. To harness Vpu's proteolytic targeting capability to degrade new receptors, we fused a chemokine with the C terminal region of Vpu. The fusion protein, or degrakine, accumulates in the ER, trapping and functionally inactivating its target CKR. We have demonstrated that degrakines based on SDF-1 (CXCL12), MDC (CCL22) and RANTES (CCL5) specifically inactivate their respective receptor functions. Using a retroviral vector expressing the SDF-1 degrakine, we have established that CXCR4 is required for the homing of hematopoietic stem/progenitor cells (HSPC) to the bone marrow immediately after transplantation. Thus the degrakine provides an effective genetic tool to dissect receptor functions in a number of biological systems in vitro and in vivo.     

A proposal for using the ensemble approach to understand genetic regulatory networks

Understanding the genetic regulatory network comprising genes, RNA, proteins and the network connections and dynamical control rules among them, is a major task of contemporary systems biology. I focus here on the use of the ensemble approach to find one or more well-defined ensembles of model networks whose statistical features match those of real cells and organisms. Such ensembles should help explain and predict features of real cells and organisms. More precisely, an ensemble of model networks is defined by constraints on the "wiring diagram" of regulatory interactions, and the "rules" governing the dynamical behavior of regulated components of the network. The ensemble consists of all networks consistent with those constraints. Here I discuss ensembles of random Boolean networks, scale free Boolean networks, "medusa" Boolean networks, continuous variable networks, and others. For each ensemble, M statistical features, such as the size distribution of avalanches in gene activity changes unleashed by transiently altering the activity of a single gene, the distribution in distances between gene activities on different cell types, and others, are measured. This creates an M-dimensional space, where each ensemble corresponds to a cluster of points or distributions. Using current and future experimental techniques, such as gene arrays, these M properties are to be measured for real cells and organisms, again yielding a cluster of points or distributions in the M-dimensional space. The procedure then finds ensembles close to those of real cells and organisms, and hill climbs to attempt to match the observed M features. Thus obtains one or more ensembles that should predict and explain many features of the regulatory networks in cells and organisms.     

A genetic and metabolic approach to redirection of biochemical pathways of Clostridium butyricum for enhancing hydrogen production

Clostridium butyricum, a well known H₂ producing bacterium, produces lactate, butyrate, acetate, ethanol, and CO₂ as its main by‐products from glucose. The conversion of pyruvate to lactate, butyrate and ethanol involves oxidation of NADH. It was hypothesized that the NADH could be increased if the formation of these by‐products could be eliminated, resulting in enhancing H₂ yield. Herein, this study aimed to establish a genetic and metabolic approach for enhancing H₂ yield via redirection of metabolic pathways of a C. butyricum strain. The ethanol formation pathway was blocked by disruption of aad (encoding aldehyde‐alcohol dehydrogenase) using a ClosTron plasmid. Although elimination of ethanol formation alone did not increase hydrogen production, the resulting aad‐deficient mutant showed approximately 20% enhanced performance in hydrogen production with the addition of sodium acetate. This work demonstrated the possibility of improving hydrogen yield by eliminating the unfavorable by‐products ethanol and lactate. Biotechnol. Bioeng. 2013; 110: 338–342. © 2012 Wiley Periodicals, Inc.     

A quantum-theoretic approach to genetic problems

A quantum-theoretic picture of the transfer of genetic information is described. The advantage of such an approach is that a number of genetic effects appear to be explicable on the basis of general microphysical laws, independent of any specific model (such as DNA-protein coding) for the transmission of genetic information. It is assumed that the genetic information is carried by a family of numerical observables belonging to a specific microphysical system; it is shown that a single observable is theoretically sufficient to carry this information. The various types of structure that this observable can possess are then described in detail, and the possible genetic effects which can airse from each such structure are discussed. For example, it is shown how the assumption that the genetic observable possesses degenerate eigenvalues may lead to a theory of allelism. To keep the treatment self-contained, the basic quantum-theoretical principles to be used are discussed in some detail. Finally, the relation of the present approach to current biochemical ideas and to earlier quantum-theoretic treatments of genetic systems is discussed.     

Targeted transgene integration in plant cells using designed zinc finger nucleases

Targeted transgene integration in plants remains a significant technical challenge for both basic and applied research. Here it is reported that designed zinc finger nucleases (ZFNs) can drive site-directed DNA integration into transgenic and native gene loci. A dimer of designed 4-finger ZFNs enabled intra-chromosomal reconstitution of a disabled gfp reporter gene and site-specific transgene integration into chromosomal reporter loci following co-transformation of tobacco cell cultures with a donor construct comprised of sequences necessary to complement a non-functional pat herbicide resistance gene. In addition, a yeast-based assay was used to identify ZFNs capable of cleaving a native endochitinase gene. Agrobacterium delivery of a Ti plasmid harboring both the ZFNs and a donor DNA construct comprising a pat herbicide resistance gene cassette flanked by short stretches of homology to the endochitinase locus yielded up to 10% targeted, homology-directed transgene integration precisely into the ZFN cleavage site. Given that ZFNs can be designed to recognize a wide range of target sequences, these data point toward a novel approach for targeted gene addition, replacement and trait stacking in plants.     

A novel approach to predicting human ingress motion using an artificial neural network

Due to the increased availability of digital human models, the need for knowing human movement is important in product design process. If the human motion is derived rapidly as design parameters change, a developer could determine the optimal parameters. For example, the optimal design of the door panel of an automobile can be obtained for a human operator to conduct the easiest ingress and egress motion. However, acquiring motion data from existing methods provides only unrealistic motion or requires a great amount of time. This not only leads to an increased time consumption for a product development, but also causes inefficiency of the overall design process. To solve such problems, this research proposes an algorithm to rapidly and accurately predict full-body human motion using an artificial neural network (ANN) and a motion database, as the design parameters are varied. To achieve this goal, this study refers to the processes behind human motor learning procedures. According to the previous research, human generate new motion based on past motion experience when they encounter new environments. Based on this principle, we constructed a motion capture database. To construct the database, motion capture experiments were performed in various environments using an optical motion capture system. To generate full-body human motion using this data, a generalized regression neural network (GRNN) was used. The proposed algorithm not only guarantees rapid and accurate results but also overcomes the ambiguity of the human motion objective function, which has been pointed out as a limitation of optimization-based research. Statistical criteria were utilized to confirm the similarity between the generated motion and actual human motion. Our research provides the basis for a rapid motion prediction algorithm that can include a variety of environmental variables. This research contributes to an increase in the usability of digital human models, and it can be applied to various research fields.     

A systems genetics approach provides a bridge from discovered genetic variants to biological pathways in rheumatoid arthritis

Genome-wide association studies (GWAS) have yielded novel genetic loci underlying common diseases. We propose a systems genetics approach to utilize these discoveries for better understanding of the genetic architecture of rheumatoid arthritis (RA). Current evidence of genetic associations with RA was sought through PubMed and the NHGRI GWAS catalog. The associations of 15 single nucleotide polymorphisms and HLA-DRB1 alleles were confirmed in 1,287 cases and 1,500 controls of Japanese subjects. Among these, HLA-DRB1 alleles and eight SNPs showed significant associations and all but one of the variants had the same direction of effect as identified in the previous studies, indicating that the genetic risk factors underlying RA are shared across populations. By receiver operating characteristic curve analysis, the area under the curve (AUC) for the genetic risk score based on the selected variants was 68.4%. For seropositive RA patients only, the AUC improved to 70.9%, indicating good but suboptimal predictive ability. A simulation study shows that more than 200 additional loci with similar effect size as recent GWAS findings or 20 rare variants with intermediate effects are needed to achieve AUC = 80.0%. We performed the random walk with restart (RWR) algorithm to prioritize genes for future mapping studies. The performance of the algorithm was confirmed by leave-one-out cross-validation. The RWR algorithm pointed to ZAP70 in the first rank, in which mutation causes RA-like autoimmune arthritis in mice. By applying the hierarchical clustering method to a subnetwork comprising RA-associated genes and top-ranked genes by the RWR, we found three functional modules relevant to RA etiology: “leukocyte activation and differentiation”, “pattern-recognition receptor signaling pathway”, and “chemokines and their receptors”.These results suggest that the systems genetics approach is useful to find directions of future mapping strategies to illuminate biological pathways.     

A synergic approach to plant pattern generation

The paper shows convergences between the results found in various models of phyllotaxis. It shows that a synergic approach is needed to deal with the problems of phyllotaxis. An algorithm, called the phi-model, based on the observation of the meaningful and symmetry-generating presence of the golden ratio phi in all types of spiral patterns, and consequently in all types of regular patterns in phyllotaxis, is proposed. The model is suggested by a property of the allometry-type model for pattern recognition in phyllotaxis. It extends recent morphological models developed around the idea of packing efficiency of plant primordia, models that yield the noble numbers, among which are the divergence angles of spiral patterns. The phi-model also gives the noble numbers and moreover orders them in a way that establishes connections with the morphogenetic principles used in models for pattern generation; the order has to do with the relative frequencies of the spiral patterns in nature. The phi-model is a link between the two entropy models in phyllotaxis and offers a nice correspondence with the minimal entropy model generated by a systemic and holistic approach. This latter type of approach is put forward as being able to give a general framework in which to organize the concepts, results, and models in phyllotaxis in a way that produces a synergy of efforts. The necessity of doing so is seen clearly when one considers that phyllotaxis-like patterns are encountered in other fields of research, so that the problem appears to transcend the strict botanical substratum.     

A systems approach to plant bioprocess optimization

A dynamic model for plant cell metabolism was used as a basis for a rational analysis of plant production potential in in vitro cultures. The model was calibrated with data from 3-L bioreactor cultures. A dynamic sensitivity analysis framework was developed to analyse the response curves of secondary metabolite production to metabolic and medium perturbations. Simulation results suggest that a straightforward engineering of cell metabolism or medium composition might only have a limited effect on productivity. To circumvent the problem of the dynamic allocation of resources between growth and production pathways, the sensitivity analysis framework was used to assess the effect of stabilizing intracellular nutrient concentrations. Simulations showed that a stabilization of intracellular glucose and nitrogen reserves could lead to a 116% increase in the specific production of secondary metabolites compared with standard culture protocol. This culture strategy was implemented experimentally using a perfusion bioreactor. To stabilize intracellular concentrations, adaptive medium feeding was performed using model mass balances and estimations. This allowed for a completely automated culture, with controlled conditions and pre-defined decision making algorithm. The proposed culture strategy leads to a 73% increase in specific production and a 129% increase in total production, as compared with a standard batch culture protocol. The sensitivity analysis on a mathematical model of plant metabolism thus allowed producing new insights on the links between intracellular nutritional management and cell productivity. The experimental implementation was also a significant improvement on current plant bioprocess strategies.     

Optimum DNA curvature using a hybrid approach involving an artificial neural network and genetic algorithm

In the present paper, a hybrid technique involving artificial neural network (ANN) and genetic algorithm (GA) has been proposed for performing modeling and optimization of complex biological systems. In this approach, first an ANN approximates (models) the nonlinear relationship(s) existing between its input and output example data sets. Next, the GA, which is a stochastic optimization technique, searches the input space of the ANN with a view to optimize the ANN output. The efficacy of this formalism has been tested by conducting a case study involving optimization of DNA curvature characterized in terms of the RL value. Using the ANN-GA methodology, a number of sequences possessing high RL values have been obtained and analyzed to verify the existence of features known to be responsible for the occurrence of curvature. A couple of sequences have also been tested experimentally. The experimental results validate qualitatively and also near-quantitatively, the solutions obtained using the hybrid formalism. The ANN-GA technique is a useful tool to obtain, ahead of experimentation, sequences that yield high RL values. The methodology is a general one and can be suitably employed for optimizing any other biological feature.     

A mathematical approach to multiple genetic relationships

A fundamental concept in the treatment of genetic relationships is that of gene identity which first was introduced by Cotterman (1940). Based on this notion several measures of relationship evolved such as the inbreeding coefficient, the coefficient of kinship, and the identity coefficients; by means of these quantities joint and conditional phenotype probabilities could be derived. This paper is an attempt at a general mathematical treatment of genetic relationships: Identity states are defined for any number of individuals, a method is given for the calculation of the corresponding identity coefficients by means of generalized coefficients of kinship, and applications are emphasized.