蛋白质自组装条件筛选形成的透明液滴中存在的自组装现象

微-纳尺度的蛋白质自组装体具有形貌多样性与良好的生物相容性,因而成为蛋白质自组装领域的研究热点。以蛋白质结晶条件的筛选手段高通量筛选不同类型蛋白质于不同尺度、不同形貌的自组装过程,是一种新兴的研究方法,具有重要研究意义。利用该方法进行蛋白质自组装条件筛选时,常会形成一些表观透明的液滴,其中是否有自组装现象的发生尚不明确。文中以β-乳球蛋白与蛋白质结晶试剂盒IndexTM C10相互作用为例进行探索,实验结果表明透明液滴中存在微-纳尺度的蛋白质自组装体。进一步通过扫描电镜观察不同初始浓度β-乳球蛋白与IndexTM C10混合形成的透明液滴中微-纳自组装体的形貌有所差别;通过激光共聚焦显微镜连续拍摄添加荧光标签的β-乳球蛋白形成自组装体的过程,可实时观察到液液相分离现象及最终形成的自组装体的形貌;通过原位X-射线衍射手段,可观察到自组装体内部结构随时间推移逐渐有序化的过程。以上研究表明,在以结晶条件筛选手段为基础的蛋白质自组装条件筛选实验中,透明液滴内的自组装现象具有深入探索的必要和价值。     

Haplotype block partition with limited resources and applications to human chromosome 21 haplotype data

Recent studies have shown that the human genome has a haplotype block structure such that it can be decomposed into large blocks with high linkage disequilibrium (LD) and relatively limited haplotype diversity, separated by short regions of low LD. One of the practical implications of this observation is that only a small fraction of all the single-nucleotide polymorphisms (SNPs) (referred as "tag SNPs") can be chosen for mapping genes responsible for human complex diseases, which can significantly reduce genotyping effort, without much loss of power. Algorithms have been developed to partition haplotypes into blocks with the minimum number of tag SNPs for an entire chromosome. In practice, investigators may have limited resources, and only a certain number of SNPs can be genotyped. In the present article, we first formulate this problem as finding a block partition with a fixed number of tag SNPs that can cover the maximal percentage of the whole genome, and we then develop two dynamic programming algorithms to solve this problem. The algorithms are sufficiently flexible to permit knowledge of functional polymorphisms to be considered. We apply the algorithms to a data set of SNPs on human chromosome 21, combining the information of coding and noncoding regions. We study the density of SNPs in intergenic regions, introns, and exons, and we find that the SNP density in intergenic regions is similar to that in introns and is higher than that in exons, results that are consistent with previous studies. We also calculate the distribution of block break points in intergenic regions, genes, exons, and coding regions and do not find any significant differences.     

Detecting susceptibility genes in case-control studies using set association

Complex diseases are generally caused by intricate interactions of multiple genes and environmental factors. Most available linkage and association methods are developed to identify individual susceptibility genes assuming a simple disease model blind to any possible gene - gene and gene - environmental interactions. We used a set association method that uses single-nucleotide polymorphism markers to locate genetic variation responsible for complex diseases in which multiple genes are involved. Here we extended the set association method from bi-allelic to multiallelic markers. In addition, we studied the type I error rates and power for both approaches using simulations based on the coalescent process. Both bi-allelic set association (BSA) and multiallelic set association (MSA) tests have the correct type I error rates. In addition, BSA and MSA can have more power than individual marker analysis when multiple genes are involved in a complex disease. We applied the MSA approach to the simulated data sets from Genetic Analysis Workshop 13. High cholesterol level was used as the definitive phenotype for a disease. MSA failed to detect markers with significant linkage disequilibrium with genes responsible for cholesterol level. This is due to the wide spacing between the markers and the lack of association between the marker loci and the simulated phenotype.     

Sequence-based prioritization of nonsynonymous single-nucleotide polymorphisms for the study of disease mutations

The increasing demand for the identification of genetic variation responsible for common diseases has translated into a need for sophisticated methods for effectively prioritizing mutations occurring in disease-associated genetic regions. In this article, we prioritize candidate nonsynonymous single-nucleotide polymorphisms (nsSNPs) through a bioinformatics approach that takes advantages of a set of improved numeric features derived from protein-sequence information and a new statistical learning model called "multiple selection rule voting" (MSRV). The sequence-based features can maximize the scope of applications of our approach, and the MSRV model can capture subtle characteristics of individual mutations. Systematic validation of the approach demonstrates that this approach is capable of prioritizing causal mutations for both simple monogenic diseases and complex polygenic diseases. Further studies of familial Alzheimer diseases and diabetes show that the approach can enrich mutations underlying these polygenic diseases among the top of candidate mutations. Application of this approach to unclassified mutations suggests that there are 10 suspicious mutations likely to cause diseases, and there is strong support for this in the literature.     

Accurate genome relative abundance estimation based on shotgun metagenomic reads

Accurate estimation of microbial community composition based on metagenomic sequencing data is fundamental for subsequent metagenomics analysis. Prevalent estimation methods are mainly based on directly summarizing alignment results or its variants; often result in biased and/or unstable estimates. We have developed a unified probabilistic framework (named GRAMMy) by explicitly modeling read assignment ambiguities, genome size biases and read distributions along the genomes. Maximum likelihood method is employed to compute Genome Relative Abundance of microbial communities using the Mixture Model theory (GRAMMy). GRAMMy has been demonstrated to give estimates that are accurate and robust across both simulated and real read benchmark datasets. We applied GRAMMy to a collection of 34 metagenomic read sets from four metagenomics projects and identified 99 frequent species (minimally 0.5% abundant in at least 50% of the data-sets) in the human gut samples. Our results show substantial improvements over previous studies, such as adjusting the over-estimated abundance for Bacteroides species for human gut samples, by providing a new reference-based strategy for metagenomic sample comparisons. GRAMMy can be used flexibly with many read assignment tools (mapping, alignment or composition-based) even with low-sensitivity mapping results from huge short-read datasets. It will be increasingly useful as an accurate and robust tool for abundance estimation with the growing size of read sets and the expanding database of reference genomes.     

Local similarity analysis reveals unique associations among marine bacterioplankton species and environmental factors

MOTIVATION: Characterizing the diversity of microbial communities and understanding the environmental factors that influence community diversity are central tenets of microbial ecology. The development and application of cultivation independent molecular tools has allowed for rapid surveying of microbial community composition at unprecedented resolutions and frequencies. There is a growing need to discern robust patterns and relationships within these datasets which provide insight into microbial ecology. Pearson correlation coefficient (PCC) analysis is commonly used for identifying the linear relationship between two species, or species and environmental factors. However, this approach may not be able to capture more complex interactions which occur in situ; thus, alternative analyses were explored. RESULTS: In this paper we introduced local similarity analysis (LSA), which is a technique that can identify more complex dependence associations among species as well as associations between species and environmental factors without requiring significant data reduction. To illustrate its capability of identifying relationships that may not otherwise be identified by PCC, we first applied LSA to simulated data. We then applied LSA to a marine microbial observatory dataset and identified unique, significant associations that were not detected by PCC analysis. LSA results, combined with results from PCC analysis were used to construct a theoretical ecological network which allows for easy visualization of the most significant associations. Biological implications of the significant associations detected by LSA were discussed. We also identified additional applications where LSA would be beneficial. AVAILABILITY: The algorithms are implemented in Splus/R and they are available upon request from the corresponding author.     

A dynamic programming algorithm for binning microbial community profiles

MOTIVATION: A number of community profiling approaches have been widely used to study the microbial community composition and its variations in environmental ecology. Automated Ribosomal Intergenic Spacer Analysis (ARISA) is one such technique. ARISA has been used to study microbial communities using 16S-23S rRNA intergenic spacer length heterogeneity at different times and places. Owing to errors in sampling, random mutations in PCR amplification, and probably mostly variations in readings from the equipment used to analyze fragment sizes, the data read directly from the fragment analyzer should not be used for down stream statistical analysis. No optimal data preprocessing methods are available. A commonly used approach is to bin the reading lengths of the 16S-23S intergenic spacer. We have developed a dynamic programming algorithm based binning method for ARISA data analysis which minimizes the overall differences between replicates from the same sampling location and time. RESULTS: In a test example from an ocean time series sampling program, data preprocessing identified several outliers which upon re-examination were found to be because of systematic errors. Clustering analysis of the ARISA from different times based on the dynamic programming algorithm binned data revealed important features of the biodiversity of the microbial communities.     

Attenuation of Notch and Hedgehog signaling is required for fate specification in the spinal cord

During the development of the spinal cord, proliferative neural progenitors differentiate into postmitotic neurons with distinct fates. How cells switch from progenitor states to differentiated fates is poorly understood. To address this question, we studied the differentiation of progenitors in the zebrafish spinal cord, focusing on the differentiation of Kolmer-Agduhr″ (KA″) interneurons from lateral floor plate (LFP) progenitors. In vivo cell tracking demonstrates that KA″ cells are generated from LFP progenitors by both symmetric and asymmetric cell divisions. A photoconvertible reporter of signaling history (PHRESH) reveals distinct temporal profiles of Hh response: LFP progenitors continuously respond to Hh, while KA″ cells lose Hh response upon differentiation. Hh signaling is required in LFP progenitors for KA″ fate specification, but prolonged Hh signaling interferes with KA″ differentiation. Notch signaling acts permissively to maintain LFP progenitor cells: activation of Notch signaling prevents differentiation, whereas inhibition of Notch signaling results in differentiation of ectopic KA″ cells. These results indicate that neural progenitors depend on Notch signaling to maintain Hh responsiveness and rely on Hh signaling to induce fate identity, whereas proper differentiation depends on the attenuation of both Notch and Hh signaling.