RESULTS: All HPs of B. lehensis G1 were grouped according to their predicted functions based on the presence of functional domains in their sequences. From the metal-binding group of HPs of the cluster, an HP termed Bleg1_2507 was discovered to contain a thioredoxin (Trx) domain and highly-conserved metal-binding ligands represented by Cys69, Cys73 and His159, similar to all prokaryotic and eukaryotic Sco proteins. The built 3D structure of Bleg1_2507 showed that it shared the βαβαββ core structure of Trx-like proteins as well as three flanking β-sheets, a 310 -helix at the N-terminus and a hairpin structure unique to Sco proteins. Docking simulations provided an interesting view of Bleg1_2507 in association with its putative cytochrome c oxidase subunit II (COXII) redox partner, Bleg1_2337, where the latter can be seen to hold its partner in an embrace, facilitated by hydrophobic and ionic interactions between the proteins. Although Bleg1_2507 shares relatively low sequence identity (47%) to BsSco, interestingly, the predicted metal-binding residues of Bleg1_2507 i.e. Cys-69, Cys-73 and His-159 were located at flexible active loops similar to other Sco proteins across biological taxa. This highlights structural conservation of Sco despite their various functions in prokaryotes and eukaryotes.
CONCLUSIONS: We propose that HP Bleg1_2507 is a Sco protein which is able to interact with COXII, its redox partner and therefore, may possess metallochaperone and redox functions similar to other documented bacterial Sco proteins. It is hoped that this scientific effort will help to spur the search for other physiologically relevant proteins among the so-called "orphan" proteins of any given organism.
METHODS: To discover novel pancreatic cancer risk loci and possible causal genes, we performed a pancreatic cancer transcriptome-wide association study in Europeans using three approaches: FUSION, MetaXcan, and Summary-MulTiXcan. We integrated genome-wide association studies summary statistics from 9040 pancreatic cancer cases and 12 496 controls, with gene expression prediction models built using transcriptome data from histologically normal pancreatic tissue samples (NCI Laboratory of Translational Genomics [n = 95] and Genotype-Tissue Expression v7 [n = 174] datasets) and data from 48 different tissues (Genotype-Tissue Expression v7, n = 74-421 samples).
RESULTS: We identified 25 genes whose genetically predicted expression was statistically significantly associated with pancreatic cancer risk (false discovery rate < .05), including 14 candidate genes at 11 novel loci (1p36.12: CELA3B; 9q31.1: SMC2, SMC2-AS1; 10q23.31: RP11-80H5.9; 12q13.13: SMUG1; 14q32.33: BTBD6; 15q23: HEXA; 15q26.1: RCCD1; 17q12: PNMT, CDK12, PGAP3; 17q22: SUPT4H1; 18q11.22: RP11-888D10.3; and 19p13.11: PGPEP1) and 11 at six known risk loci (5p15.33: TERT, CLPTM1L, ZDHHC11B; 7p14.1: INHBA; 9q34.2: ABO; 13q12.2: PDX1; 13q22.1: KLF5; and 16q23.1: WDR59, CFDP1, BCAR1, TMEM170A). The association for 12 of these genes (CELA3B, SMC2, and PNMT at novel risk loci and TERT, CLPTM1L, INHBA, ABO, PDX1, KLF5, WDR59, CFDP1, and BCAR1 at known loci) remained statistically significant after Bonferroni correction.
CONCLUSIONS: By integrating gene expression and genotype data, we identified novel pancreatic cancer risk loci and candidate functional genes that warrant further investigation.
RESULTS: The study generated 846,762 high quality sequence reads, with an average length of 334 bp and totalling 283 Mbp. De novo assembly generated 36,384 and 35,269 unigene sequences for M. acuminata Calcutta 4 and Cavendish Grande Naine, respectively. A total of 64.4% of the unigenes were annotated through Basic Local Alignment Search Tool (BLAST) similarity analyses against public databases.Assembled sequences were functionally mapped to Gene Ontology (GO) terms, with unigene functions covering a diverse range of molecular functions, biological processes and cellular components. Genes from a number of defense-related pathways were observed in transcripts from each cDNA library. Over 99% of contig unigenes mapped to exon regions in the reference M. acuminata DH Pahang whole genome sequence. A total of 4068 genic-SSR loci were identified in Calcutta 4 and 4095 in Cavendish Grande Naine. A subset of 95 potential defense-related gene-derived simple sequence repeat (SSR) loci were validated for specific amplification and polymorphism across M. acuminata accessions. Fourteen loci were polymorphic, with alleles per polymorphic locus ranging from 3 to 8 and polymorphism information content ranging from 0.34 to 0.82.
CONCLUSIONS: A large set of unigenes were characterized in this study for both M. acuminata Calcutta 4 and Cavendish Grande Naine, increasing the number of public domain Musa ESTs. This transcriptome is an invaluable resource for furthering our understanding of biological processes elicited during biotic stresses in Musa. Gene-based markers will facilitate molecular breeding strategies, forming the basis of genetic linkage mapping and analysis of quantitative trait loci.
RESULTS: We developed a fast Bayesian method which uses the sequencing coverage information determined from the concentration of an RNA sample to estimate the posterior distribution of a true gene count. Our method has better or comparable performance compared to NOISeq and GFOLD, according to the results from simulations and experiments with real unreplicated data. We incorporated a previously unused sequencing coverage parameter into a procedure for differential gene expression analysis with RNA-Seq data.
CONCLUSIONS: Our results suggest that our method can be used to overcome analytical bottlenecks in experiments with limited number of replicates and low sequencing coverage. The method is implemented in CORNAS (Coverage-dependent RNA-Seq), and is available at https://github.com/joel-lzb/CORNAS .