Asia harbors substantial cultural and linguistic diversity, but the geographic structure of genetic variation across the continent remains enigmatic. Here we report a large-scale survey of autosomal variation from a broad geographic sample of Asian human populations. Our results show that genetic ancestry is strongly correlated with linguistic affiliations as well as geography. Most populations show relatedness within ethnic/linguistic groups, despite prevalent gene flow among populations. More than 90% of East Asian (EA) haplotypes could be found in either Southeast Asian (SEA) or Central-South Asian (CSA) populations and show clinal structure with haplotype diversity decreasing from south to north. Furthermore, 50% of EA haplotypes were found in SEA only and 5% were found in CSA only, indicating that SEA was a major geographic source of EA populations.

Plant leaves, harvesting light energy and fixing CO 2 , are a major source of foods on the earth. Leaves undergo developmental and physiological shifts during their lifespan, ending with senescence and death. We characterized the key regulatory features of the leaf transcriptome during aging by analyzing total-and small-RNA transcriptomes throughout the lifespan of Arabidopsis (Arabidopsis thaliana) leaves at multidimensions, including age, RNA-type, and organelle. Intriguingly, senescing leaves showed more coordinated temporal changes in transcriptomes than growing leaves, with sophisticated regulatory networks comprising transcription factors and diverse small regulatory RNAs. The chloroplast transcriptome, but not the mitochondrial transcriptome, showed major changes during leaf aging, with a strongly shared expression pattern of nuclear transcripts encoding chloroplast-targeted proteins. Thus, unlike animal aging, leaf senescence proceeds with tight temporal and distinct interorganellar coordination of various transcriptomes that would be critical for the highly regulated degeneration and nutrient recycling contributing to plant fitness and productivity.Most organisms undergo age-dependent developmental changes during their lifespans. The timely decision of developmental changes during the lifespan is a critical evolutionary characteristic that maximizes fitness in a given ecological setting (Leopold, 1961;Fenner, 1998;Samach and Coupland, 2000). Plants use unique developmental strategies throughout their lifespans as opposed to animals. In plants, most organs are formed postnatally from sets of stem cells in the seed. In addition, plants are sessile and cope with encountering environments physiologically, rather than behaviorally. Thus, they have developed highly plastic and interactive developmental programs to incorporate environmental changes into their developmental decisions (Pigliucci, 1998;Sultan, 2000).The leaf is an organ that characterizes the fundamental aspects of plants. Leaves harvest light energy, fix CO 2 to produce carbohydrates, and, as primary producers in our ecosystem, serve as a major food source on the earth. Leaves undergo a series of developmental and physiological shifts during their lifespans. A leaf is initially formed as a leaf primordium derived from the stem cells at the shoot apical meristem and develops into a photosynthetic organ through biogenesis processes involving cell division, differentiation, and expansion (Tsukaya, 2013). In the later stages of their lifespans, leaves undergo organ-level senescence and eventually death. Organlevel senescence in plants involves postmitotic senescence and is a term used similarly as "aging" in animals. During the senescence stage, leaf cells undergo dramatic shifts in physiology from biogenesis to the sequential 1 This research was supported by the Institute for Basic Science (IBS-R013-D1 and IBS-R013-G1), the DGIST R&D Program (2014010043, 2015010004, 2015010011, 20150100012, and 15-01-HRLA-01), Basic Science Research Program (2010-0...

We present the analysis of the evolution of tumors in a case of hepatocellular carcinoma. This case is particularly informative about cancer growth dynamics and the underlying driving mutations. We sampled nine different sections from three tumors and seven more sections from the adjacent nontumor tissues. Selected sections were subjected to exon as well as whole-genome sequencing. Putative somatic mutations were then individually validated across all 9 tumor and 7 nontumor sections. Among the mutations validated, 24 were amino acid changes; in addition, 22 large indels/copy number variants (>1 Mb) were detected. These somatic mutations define four evolutionary lineages among tumor cells. Separate evolution and expansion of these lineages were recent and rapid, each apparently having only one lineage-specific protein-coding mutation. Hence, by using a cell-population genetic definition, this approach identified three coding changes (CCNG1, P62, and an indel/fusion gene) as tumor driver mutations. These three mutations, affecting cell cycle control and apoptosis, are functionally distinct from mutations that accumulated earlier, many of which are involved in inflammation/immunity or cell anchoring. These distinct functions of mutations at different stages may reflect the genetic interactions underlying tumor growth.cell genealogy | cellular evolution | foreground mutation T umorigenesis is generally believed to be the consequence of mutation accumulation, including single nucleotide substitutions, structural variations, and epigenetic changes, in somatic cells (1). A typical cancer may have thousands of somatic mutations, of which 10-100 may be in coding regions (2-7). A central issue in cancer genomics is then the dynamics of tumor growth in relation to the accumulation of these mutations. Given any individual case of cancer, the questions are hence: (i) how many adaptive mutations drive the tumor growth; (ii) how strongly each mutation drives the growth; and (iii) what their molecular nature is vis-à-vis that of the background mutations. To answer these questions, we treat each tumor as a population of cells and apply population genetic principles to infer adaptive mutations (8).Cancer mutations are often divided into drivers and passengers (9). Driver mutations are those that contribute directly to tumorigenesis and their identification is crucial for understanding the molecular biology of cancers. An important issue is how driver mutations should be defined operationally. Candidate driver mutation in the literature often refers to coding changes in genes that are commonly mutated, for example, in multiple cases of hepatocellular carcinoma (HCC). Adaptive mutation proposed here is an alternative definition of candidate driver mutation, inferred from the dynamics of cell proliferation in its natural setting within a single patient.In this report, we analyze a case of HCC, the fifth most common cancer worldwide, by such an approach. We regard HCC as particularly favorable for identifying candidate driver mutatio...

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We propose a novel, efficient and intuitive approach of estimating mRNA abundances from the whole transcriptome shotgun sequencing (RNA-Seq) data. Our method, NEUMA (Normalization by Expected Uniquely Mappable Area), is based on effective length normalization using uniquely mappable areas of gene and mRNA isoform models. Using the known transcriptome sequence model such as RefSeq, NEUMA pre-computes the numbers of all possible gene-wise and isoform-wise informative reads: the former being sequences mapped to all mRNA isoforms of a single gene exclusively and the latter uniquely mapped to a single mRNA isoform. The results are used to estimate the effective length of genes and transcripts, taking experimental distributions of fragment size into consideration. Quantitative RT–PCR based on 27 randomly selected genes in two human cell lines and computer simulation experiments demonstrated superior accuracy of NEUMA over other recently developed methods. NEUMA covers a large proportion of genes and mRNA isoforms and offers a measure of consistency (‘consistency coefficient’) for each gene between an independently measured gene-wise level and the sum of the isoform levels. NEUMA is applicable to both paired-end and single-end RNA-Seq data. We propose that NEUMA could make a standard method in quantifying gene transcript levels from RNA-Seq data.

BackgroundDeep sequencing techniques provide a remarkable opportunity for comprehensive understanding of tumorigenesis at the molecular level. As omics studies become popular, integrative approaches need to be developed to move from a simple cataloguing of mutations and changes in gene expression to dissecting the molecular nature of carcinogenesis at the systemic level and understanding the complex networks that lead to cancer development.ResultsHere, we describe a high-throughput, multi-dimensional sequencing study of primary lung adenocarcinoma tumors and adjacent normal tissues of six Korean female never-smoker patients. Our data encompass results from exome-seq, RNA-seq, small RNA-seq, and MeDIP-seq. We identified and validated novel genetic aberrations, including 47 somatic mutations and 19 fusion transcripts. One of the fusions involves the c-RET gene, which was recently reported to form fusion genes that may function as drivers of carcinogenesis in lung cancer patients. We also characterized gene expression profiles, which we integrated with genomic aberrations and gene regulations into functional networks. The most prominent gene network module that emerged indicates that disturbances in G2/M transition and mitotic progression are causally linked to tumorigenesis in these patients. Also, results from the analysis strongly suggest that several novel microRNA-target interactions represent key regulatory elements of the gene network.ConclusionsOur study not only provides an overview of the alterations occurring in lung adenocarcinoma at multiple levels from genome to transcriptome and epigenome, but also offers a model for integrative genomics analysis and proposes potential target pathways for the control of lung adenocarcinoma.

The plant-specific Teosinte-branched 1/Cycloidea/Proliferating (TCP) transcription factor genes are involved in plants’ development, hormonal pathways, and stress response but their evolutionary history is uncertain. The genome-wide analysis performed here for 47 plant species revealed 535 TCP candidates in terrestrial plants and none in aquatic plants, and that TCP family genes originated early in the history of land plants. Phylogenetic analysis divided the candidate genes into Classes I and II, and Class II was further divided into CYCLOIDEA (CYC) and CINCINNATA (CIN) clades; CYC is more recent and originated from CIN in angiosperms. Protein architecture, intron pattern, and sequence characteristics were conserved in each class or clade supporting this classification. The two classes significantly expanded through whole-genome duplication during evolution. Expression analysis revealed the conserved expression of TCP genes from lower to higher plants. The expression patterns of Class I and CIN genes in different stages of the same tissue revealed their function in plant development and their opposite effects in the same biological process. Interaction network analysis showed that TCP proteins tend to form protein complexes, and their interaction networks were conserved during evolution. These results contribute to further functional studies on TCP family genes.

Main conclusion Genome-wide identification, classification, expression analyses, and functional characterization of GRAS genes in oil crop, Brassica napus, indicate their importance in root development and stress response.