Taking duckweed populations collected from some countries and regions as materials,using morphological classification,microscope observation method,the samples were identified through observeation of size and vein of leaf,numbers of root,color of back of leaf,root sheath and root tip,number of chromosome was observated.The results showed that duckweed genome was less,variation of chromosome and their number were more,from 2n=20,30,40,60.The results made a simple systematics of duckweeds inferenced from morphological data found.It was important for the research of cytology and systematics of duckweed.

Duckweed, with rapid growth rate and high starch content, is a new alternate feedstock for bioethanol production. The genetic diversity among 27 duckweed populations of seven species in genus Lemna and Spirodela from China and Vietnam was analyzed by ISSR-PCR. Eight ISSR primers generating a reproducible amplification banding pattern had been screened. 89 polymorphic bands were scored out of the 92 banding patterns of 16 Lemna populations, accounting for 96.74% of the polymorphism. 98 polymorphic bands of 11 Spirodela populations were scored out of 99 banding patterns, and the polymorphism was 98.43%. The genetic distance of Lemna varied from 0.127 to 0.784, and from 0.138 to 0.902 for Spirodela, which indicated a high level of genetic variation among the populations studied. The unweighted pair group method with arithmetic average (UPGMA) cluster analysis corresponded well with the genetic distance. Populations from Sichuan China grouped together and so did the populations from Vietnam, which illuminated populations collected from the same region clustered into one group. Especially, the only one population from Tibet was included in subgroup A2 alone. Clustering analysis indicated that the geographic differentiation of collected sites correlated closely with the genetic differentiation of duckweeds. The results suggested that geographic differentiation had great influence on genetic diversity of duckweed in China and Vietnam at the regional scale. This study provided primary guidelines for collection, conservation, characterization of duckweed resources for bioethanol production etc.

Pediatric acute lymphoblastic leukemia (ALL) contains cytogenetically distinct subtypes that respond differently to cytotoxic drugs. Subtype classification can be also achieved through gene expression profiling. However, how to apply such classifiers to a single patient and correctly diagnose the disease subtype in an independent patient group has not been addressed. Furthermore, the underlying regulatory mechanisms responsible for the subtype-specific gene expression patterns are still largely unknown. Here, by combining 3 published microarray datasets on 535 mostly white children's samples and generating a new dataset on 100 Chinese children's ALL samples, we were able to (1) identify a 62-gene classifier with 97.6% accuracy from the white children's samples and validated it on the completely independent set of 100 Chinese samples, and (2) uncover potential regulatory networks of ALL subtypes. The classifier we identified was, thus far, the only one that could be applied directly to a single sample and that sustained validation in a large independent patient group. Our results also suggest that the etiology of ALL is largely the same among different ethnic groups, and that the transcription factor hubs in the predicted regulatory network might play important roles in regulating gene expression and development of ALL.

MicroRNAs have been suggested to modulate a variety of cellular events. Here we report that miR-24 regulates erythroid differentiation by influencing the expression of human activin type I receptor ALK4 (hALK4). Ectopic expression of miR-24 reduces the mRNA and protein levels of hALK4 by targeting the 3'-untranslated region of hALK4 mRNA and interferes with activin-induced Smad2 phosphorylation and reporter expression. Furthermore, miR-24 represses the activin-mediated accumulation of hemoglobin, an erythroid differentiation marker, in erythroleukemic K562 cells and decreases erythroid colony-forming and burst-forming units of CD34+ hematopoietic progenitor cells. ALK4 expression is inversely correlated with miR-24 expression during the early stages of erythroid differentiation, and the forced expression of miR-24 leads to a delay of activin-induced maturation of hematopoietic progenitor cells in liquid culture. Thus, our findings define a regulation mode of miR-24 on erythropoiesis by impeding ALK4 expression.

Histone modifications are major epigenetic factors regulating gene expression. They play important roles in maintaining stem cell pluripotency and in cancer pathogenesis. Different modifications may combine to form complex "histone codes." Recent high-throughput technologies, such as "ChIP-chip" and "ChIP-seq," have generated high-resolution maps for many histone modifications on the human genome. Here we use these maps to build a Bayesian network to infer causal and combinatorial relationships among histone modifications and gene expression. A pilot network derived by the same method among polycomb group (PcG) genes and H3K27 trimethylation is accurately supported by current literature. Our unbiased network model among histone modifications is also well supported by cross-validation results. It not only confirmed already known relationships, such as those of H3K27me3 to gene silencing, H3K4me3 to gene activation and the effect of bivalent modification of both H3K4me3 and H3K27me3, but also identified many other relationships that may predict new epigenetic interactions important in epigenetic gene regulation. Our automated inference method, which is potentially applicable to other ChIP-chip or ChIP-seq data analyses, provides a much-needed guide to deciphering the complex histone codes.

Organism development is a systems level process. It has benefited greatly from the recent technological advances in the field of systems biology. DNA microarray, phenome, interactome and transcriptome mapping, the new generation of deep sequencing technologies, and faster and better computational and modeling approaches have opened new frontiers for both systems biologists and developmental biologists to reexamine the old developmental biology questions, such as pattern formation, and to tackle new problems, such as stem cell reprogramming. As showcased in the International Developmental Systems Biology Symposium organized by Chinese Academy of Sciences, developmental systems biology is flourishing in many perspectives, from the evolution of developmental systems, to the underlying genetic and molecular pathways and networks, to the genomic, epigenomic and noncoding levels, to the computational analysis and modeling. We believe that the field will continue to reap rewards into the future with these new approaches.

Many fundamental questions on aging are still unanswered or are under intense debate. These questions are frequently not addressable by examining a single gene or a single pathway, but can best be addressed at the systems level. Here we examined the modular structure of the protein protein interaction (PPI) networks during fruitfly and human brain aging. In both networks, there are two modules associated with the cellular proliferation to differentiation temporal switch that display opposite aging-related changes in expression. During fly aging, another couple of modules are associated with the oxidative-reductive metabolic temporal switch. These network modules and their relationships demonstrate (1) that aging is largely associated with a small number, instead of many network modules, (2) that some modular changes might be reversible and (3) that genes connecting different modules through PPIs are more likely to affect aging/longevity, a conclusion that is experimentally validated by Caenorhabditis elegans lifespan analysis. Network simulations further suggest that aging might preferentially attack key regulatory nodes that are important for the network stability, implicating a potential molecular basis for the stochastic nature of aging.

The protein-protein interaction networks, or interactome networks, have been shown to have dynamic modular structures, yet the functional connections between and among the modules are less well understood. Here, using a new pipeline to integrate the interactome and the transcriptome, we identified a pair of transcriptionally anticorrelated modules, each consisting of hundreds of genes in multicellular interactome networks across different individuals and populations. The two modules are associated with cellular proliferation and differentiation, respectively. The proliferation module is conserved among eukaryotic organisms, whereas the differentiation module is specific to multicellular organisms. Upon differentiation of various tissues and cell lines from different organisms, the expression of the proliferation module is more uniformly suppressed, while the differentiation module is upregulated in a tissue- and species-specific manner. Our results indicate that even at the tissue and organism levels, proliferation and differentiation modules may correspond to two alternative states of the molecular network and may reflect a universal symbiotic relationship in a multicellular organism. Our analyses further predict that the proteins mediating the interactions between these modules may serve as modulators at the proliferation/differentiation switch.