Lung cancer is the world's leading cause of cancer death with strong ancestry disparities. By sequencing and assembling the largest genomic and transcriptomic dataset of lung adenocarcinoma (LUAD) in individuals of East Asian ancestry (EAS; n = 305) to date, we found that East Asian LUADs had more stable genomes characterized by fewer mutations and less copy number alteration than LUADs from individuals of European ancestry (EUR). This difference is much stronger in smokers as compared to non-smokers. Transcriptomic clustering identified a novel EAS-specific LUAD subgroup with a less complex genomic profile and up-regulated immune-related genes, allowing the possibility of immunotherapybased approaches. Integrative analysis across clinical and molecular features showed the importance of molecular phenotypes in patient prognostic stratification. EAS LUADs had better prediction accuracy than those of European ancestry, potentially due to the less complex genomic architecture. This study elucidated a comprehensive genomic landscape of EAS LUADs and highlighted important ancestry differences between the two cohorts.

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Transcriptome reconstruction is an important application of RNA-Seq, providing critical information for further analysis of transcriptome. Although RNA-Seq offers the potential to identify the whole picture of transcriptome, it still presents special challenges. To handle these difficulties and reconstruct transcriptome as completely as possible, current computational approaches mainly employ two strategies: de novo assembly and genome-guided assembly. In order to find the similarities and differences between them, we firstly chose five representative assemblers belonging to the two classes respectively, and then investigated and compared their algorithm features in theory and real performances in practice. We found that all the methods can be reduced to graph reduction problems, yet they have different conceptual and practical implementations, thus each assembly method has its specific advantages and disadvantages, performing worse than others in certain aspects while outperforming others in anther aspects at the same time. Finally we merged assemblies of the five assemblers and obtained a much better assembly. Additionally we evaluated an assembler using genome-guided de novo assembly approach, and achieved good performance. Based on these results, we suggest that to obtain a comprehensive set of recovered transcripts, it is better to use a combination of de novo assembly and genome-guided assembly.transcriptome reconstruction, RNA-Seq, de novo assembly, genome-guided assembly Citation:Lu B X, Zeng Z B, Shi T L. Comparative study of de novo assembly and genome-guided assembly strategies for transcriptome reconstruction based

BackgroundThe growth differentiation factor 11 (GDF11) was shown to reverse age-related hypertrophy on cardiomyocytes and considered as anti-aging rejuvenation factor. The role of GDF11 in regulating metabolic homeostasis is unclear. In this study, we investigated the functions of GDF11 in regulating metabolic homeostasis and energy balance.MethodsUsing a hydrodynamic injection approach, plasmids carrying a mouse Gdf11 gene were delivered into mice and generated the sustained Gdf11 expression in the liver and its protein level in the blood. High fat diet (HFD)-induced obesity was employed to examine the impacts of Gdf11 gene transfer on HFD-induced adiposity, hyperglycemia, insulin resistance, and hepatic lipid accumulation. The impacts of GDF11 on metabolic homeostasis of obese and diabetic mice were examined using HFD-induced obese and STZ-induced diabetic models.ResultsGdf11 gene transfer alleviates HFD-induced obesity, hyperglycemia, insulin resistance, and fatty liver development. In obese and STZ-induced diabetic mice, Gdf11 gene transfer restores glucose metabolism and improves insulin resistance. Mechanism study reveals that Gdf11 gene transfer increases the energy expenditure of mice, upregulates the expression of genes responsible for thermoregulation in brown adipose tissue, downregulates the expression of inflammatory genes in white adipose tissue and those involved in hepatic lipid and glucose metabolism. Overexpression of GDF11 also activates TGF-β/Smad2, PI3K/AKT/FoxO1, and AMPK signaling pathways in white adipose tissue.ConclusionsThese results demonstrate that GDF11 plays an important role in regulating metabolic homeostasis and energy balance and could be a target for pharmacological intervention to treat metabolic disease.

Clusters of genes acquired by lateral gene transfer in microbial genomes, are broadly referred to as genomic islands (GIs). GIs often carry genes important for genome evolution and adaptation to niches, such as genes involved in pathogenesis and antibiotic resistance. Therefore, GI prediction has gradually become an important part of microbial genome analysis. Despite inherent difficulties in identifying GIs, many computational methods have been developed and show good performance. In this mini-review, we first summarize the general challenges in predicting GIs. Then we group existing GI detection methods by their input, briefly describe representative methods in each group, and discuss their advantages as well as limitations. Finally, we look into the potential improvements for better GI prediction.

Central to tumor evolution is the generation of genetic diversity. However, the extent and patterns by which de novo karyotype alterations emerge and propagate within human tumors are not well understood, especially at single-cell resolution. Here, we present 3D Live-Seq—a protocol that integrates live-cell imaging of tumor organoid outgrowth and whole-genome sequencing of each imaged cell to reconstruct evolving tumor cell karyotypes across consecutive cell generations. Using patient-derived colorectal cancer organoids and fresh tumor biopsies, we demonstrate that karyotype alterations of varying complexity are prevalent and can arise within a few cell generations. Sub-chromosomal acentric fragments were prone to replication and collective missegregation across consecutive cell divisions. In contrast, gross genome-wide karyotype alterations were generated in a single erroneous cell division, providing support that aneuploid tumor genomes can evolve via punctuated evolution. Mapping the temporal dynamics and patterns of karyotype diversification in cancer enables reconstructions of evolutionary paths to malignant fitness.

Si-Wu-Tang (SWT) is a Traditional Chinese Medicine (TCM) formula widely used for the treatments of gynecological diseases. To explore the pharmacological mechanism of SWT, we incorporated microarray data of SWT with our herbal target database TCMID to analyze the potential activity mechanism of SWT's herbal ingredients and targets. We detected 2,405 differentially expressed genes in the microarray data, 20 of 102 proteins targeted by SWT were encoded by these DEGs and can be targeted by 2 FDA-approved drugs and 39 experimental drugs. The results of pathway enrichment analysis of the 20 predicted targets were consistent with that of 2,405 differentially expressed genes, elaborating the potential pharmacological mechanisms of SWT. Further study from a perspective of protein-protein interaction (PPI) network showed that the predicted targets of SWT function cooperatively to perform their multi-target effects. We also constructed a network to combine herbs, ingredients, targets and drugs together which bridges the gap between SWT and conventional medicine, and used it to infer the potential mechanisms of herbal ingredients. Moreover, based on the hypothesis that the same or similar effects between different TCM formulae may result from targeting the same proteins, we analyzed 27 other TCM formulae which can also treat the gynecological diseases, the subsequent result provides additional insight to understand the potential mechanisms of SWT in treating amenorrhea. Our bioinformatics approach to detect the pharmacology of SWT may shed light on drug discovery for gynecological diseases and could be utilized to investigate other TCM formulae as well.

Supplementary data are available at Bioinformatics online.