Genomic Analysis of Mycobacterium tuberculosis Isolates and Construction of a Beijing Lineage Reference Genome.

Tuberculosis (TB), an infectious disease caused by Mycobacterium tuberculosis, kills over 1 million people worldwide annually. Development of drug resistance (DR) in the pathogen is a major challenge for TB control. We conducted whole-genome analysis of seven Taiwan M. tuberculosis isolates: One drug susceptible (DS) and five DR Beijing lineage isolates and one DR Euro-American lineage isolate. Developing a new method for DR mutation identification and applying it to the next-generation sequencing (NGS) data from the 6 Beijing lineage isolates, we identified 13 known and 6 candidate DR mutations and provided experimental support for 4 of them. We assembled the genomes of one DS and two DR Beijing lineage isolates and the Euro-American lineage isolate using NGS data. Moreover, using both PacBio and NGS sequencing data, we obtained a high-quality assembly of an extensive DR Beijing lineage isolate. Comparative analysis of these five newly assembled genomes and two published complete genomes revealed a large number of genetic changes, including gene gains and losses, indels and translocations, suggesting rapid evolution of M. tuberculosis. We found the MazEF toxin-antitoxin system in all the seven isolates studied and several interesting mutations in MazEF proteins. Finally, we used the four assembled Beijing lineage genomes to construct a high-quality Beijing lineage reference genome that is DS and contains all the genes in the four genomes. It contains 212 genes not found in the standard reference H37Rv, which is Euro-American. It is therefore a better reference than H37Rv for the Beijing lineage, the predominant lineage in Asia.

Detection of QTL for traits related to adaptation to sub-optimal climatic conditions in chickens.

BACKGROUND: Growth traits can be used as indicators of adaptation to sub-optimal conditions. The current study aimed at identifying quantitative trait loci (QTL) that control performance under variable temperature conditions in chickens. METHODS: An F2 population was produced by crossing the Taiwan Country chicken L2 line (selected for body weight, comb area, and egg production) with an experimental line of Rhode Island Red layer R- (selected for low residual feed consumption). A total of 844 animals were genotyped with the 60 K Illumina single nucleotide polymorphism (SNP) chip. Whole-genome interval linkage mapping and a genome-wide association study (GWAS) were performed for body weight at 0, 4, 8, 12, and 16 weeks of age, shank length at 8 weeks of age, size of comb area at 16 weeks of age, and antibody response to sheep red blood cells at 11 weeks of age (7 and 14 days after primary immunization). Relevant genes were identified based on functional annotation of candidate genes and potentially relevant SNPs were detected by comparing whole-genome sequences of several birds between the parental lines. RESULTS: Whole-genome QTL analysis revealed 47 QTL and 714 effects associated with 178 SNPs were identified by GWAS with 5% Bonferroni genome-wide significance. Little overlap was observed between the QTL and GWAS results, with only two chromosomal regions detected by both approaches, i.e. one on GGA24 (GGA for Gallus gallus chromosome) for BW04 and one on GGAZ for six growth-related traits. Based on whole-genome sequence, differences between the parental lines based on several birds were screened in the genome-wide QTL regions and in a region detected by both methods, resulting in the identification of 106 putative candidate genes with a total of 15,443 SNPs, of which 41 were missense and 1698 were not described in the dbSNP archive. CONCLUSIONS: The QTL detected in this study for growth and morphological traits likely influence adaptation of chickens to sub-tropical climate. Using whole-genome sequence data, we identified candidate SNPs for further confirmation of QTL in the F2 design. A strong QTL effect found on GGAZ underlines the importance of sex-linked inheritance for growth traits in chickens.

Genomic and transcriptomic analyses of the medicinal fungus Antrodia cinnamomea for its metabolite biosynthesis and sexual development.

Antrodia cinnamomea, a polyporus mushroom of Taiwan, has long been used as a remedy for cancer, hypertension, and hangover, with an annual market of over $100 million (US) in Taiwan. We obtained a 32.15-Mb genome draft containing 9,254 genes. Genome ontology enrichment and pathway analyses shed light on sexual development and the biosynthesis of sesquiterpenoids, triterpenoids, ergostanes, antroquinonol, and antrocamphin. We identified genes differentially expressed between mycelium and fruiting body and 242 proteins in the mevalonate pathway, terpenoid pathways, cytochrome P450s, and polyketide synthases, which may contribute to the production of medicinal secondary metabolites. Genes of secondary metabolite biosynthetic pathways showed expression enrichment for tissue-specific compounds, including 14-α-demethylase (CYP51F1) in fruiting body for converting lanostane to ergostane triterpenoids, coenzymes Q (COQ) for antroquinonol biosynthesis in mycelium, and polyketide synthase for antrocamphin biosynthesis in fruiting body. Our data will be useful for developing a strategy to increase the production of useful metabolites.

Inferring transcriptional compensation interactions in yeast via stepwise structure equation modeling.

BACKGROUND: With the abundant information produced by microarray technology, various approaches have been proposed to infer transcriptional regulatory networks. However, few approaches have studied subtle and indirect interaction such as genetic compensation, the existence of which is widely recognized although its mechanism has yet to be clarified. Furthermore, when inferring gene networks most models include only observed variables whereas latent factors, such as proteins and mRNA degradation that are not measured by microarrays, do participate in networks in reality. RESULTS: Motivated by inferring transcriptional compensation (TC) interactions in yeast, a stepwise structural equation modeling algorithm (SSEM) is developed. In addition to observed variables, SSEM also incorporates hidden variables to capture interactions (or regulations) from latent factors. Simulated gene networks are used to determine with which of six possible model selection criteria (MSC) SSEM works best. SSEM with Bayesian information criterion (BIC) results in the highest true positive rates, the largest percentage of correctly predicted interactions from all existing interactions, and the highest true negative (non-existing interactions) rates. Next, we apply SSEM using real microarray data to infer TC interactions among (1) small groups of genes that are synthetic sick or lethal (SSL) to SGS1, and (2) a group of SSL pairs of 51 yeast genes involved in DNA synthesis and repair that are of interest. For (1), SSEM with BIC is shown to outperform three Bayesian network algorithms and a multivariate autoregressive model, checked against the results of qRT-PCR experiments. The predictions for (2) are shown to coincide with several known pathways of Sgs1 and its partners that are involved in DNA replication, recombination and repair. In addition, experimentally testable interactions of Rad27 are predicted. CONCLUSION: SSEM is a useful tool for inferring genetic networks, and the results reinforce the possibility of predicting pathways of protein complexes via genetic interactions.