BackgroundPolyploid species contribute to Oryza diversity. However, the mechanisms underlying gene and genome evolution in Oryza polyploids remain largely unknown. The allotetraploid Oryza minuta, which is estimated to have formed less than one million years ago, along with its putative diploid progenitors (O. punctata and O. officinalis), are quite suitable for the study of polyploid genome evolution using a comparative genomics approach.ResultsHere, we performed a comparative study of a large genomic region surrounding the Shattering4 locus in O. minuta, as well as in O. punctata and O. officinalis. Duplicated genomes in O. minuta have maintained the diploid genome organization, except for several structural variations mediated by transposon movement. Tandem duplicated gene clusters are prevalent in the Sh4 region, and segmental duplication followed by random deletion is illustrated to explain the gene gain-and-loss process. Both copies of most duplicated genes still persist in O. minuta. Molecular evolution analysis suggested that these duplicated genes are equally evolved and mostly manipulated by purifying selection. However, cDNA-SSCP analysis revealed that the expression patterns were dramatically altered between duplicated genes: nine of 29 duplicated genes exhibited expression divergence in O. minuta. We further detected one gene silencing event that was attributed to gene structural variation, but most gene silencing could not be related to sequence changes. We identified one case in which DNA methylation differences within promoter regions that were associated with the insertion of one hAT element were probably responsible for gene silencing, suggesting a potential epigenetic gene silencing pathway triggered by TE movement.ConclusionsOur study revealed both genetic and epigenetic mechanisms involved in duplicated gene silencing in the allotetraploid O. minuta.

BackgroundProteomics research is enabled with the high-throughput technologies, but our ability to identify expressed proteome is limited in small samples. The coverage and consistency of proteome expression are critical problems in proteomics. Here, we propose pathway analysis and combination of microproteomics and transcriptomics analyses to improve mass-spectrometry protein identification from small size samples.ResultsMultiple proteomics runs using MCF-7 cell line detected 4,957 expressed proteins. About 80% of expressed proteins were present in MCF-7 transcripts data; highly expressed transcripts are more likely to have expressed proteins. Approximately 1,000 proteins were detected in each run of the small sample proteomics. These proteins were mapped to gene symbols and compared with gene sets representing canonical pathways, more than 4,000 genes were extracted from the enriched gene sets. The identified canonical pathways were largely overlapping between individual runs. Of identified pathways 182 were shared between three individual small sample runs.ConclusionsCurrent technologies enable us to directly detect 10% of expressed proteomes from small sample comprising as few as 50 cells. We used knowledge-based approaches to elucidate the missing proteome that can be verified by targeted proteomics. This knowledge-based approach includes pathway analysis and combination of gene expression and protein expression data for target prioritization. Genes present in both the enriched gene sets (canonical pathways collection) and in small sample proteomics data correspond to approximately 50% of expressed proteomes in larger sample proteomics data. In addition, 90% of targets from canonical pathways were estimated to be expressed. The comparison of proteomics and transcriptomics data, suggests that highly expressed transcripts have high probability of protein expression. However, approximately 10% of expressed proteins could not be matched with the expressed transcripts.

BackgroundAlthough the needs for analyses of secondary structures of RNAs are increasing, prediction of the secondary structures of RNAs are not always reliable. Because an RNA may have a complicated energy landscape, comprehensive representations of the whole ensemble of the secondary structures, such as the probability distributions of various features of RNA secondary structures are required.ResultsA general method to efficiently compute the distribution of any integer scalar/vector function on the secondary structure is proposed. We also show two concrete algorithms, for Hamming distance from a reference structure and for 5ʹ − 3ʹ distance, which can be constructed by following our general method. These practical applications of this method show the effectiveness of the proposed method.ConclusionsThe proposed method provides a clear and comprehensive procedure to construct algorithms for distributions of various integer features. In addition, distributions of integer vectors, that is a combination of different integer scores, can be also described by applying our 2D expanding technique.

BackgroundThe rapid growth of un-annotated missense variants poses challenges requiring novel strategies for their interpretation. From the thermodynamic point of view, amino acid changes can lead to a change in the internal energy of a protein and induce structural rearrangements. This is of great relevance for the study of diseases and protein design, justifying the development of prediction methods for variant-induced stability changes.ResultsHere we propose NeEMO, a tool for the evaluation of stability changes using an effective representation of proteins based on residue interaction networks (RINs). RINs are used to extract useful features describing interactions of the mutant amino acid with its structural environment. Benchmarking shows NeEMO to be very effective, allowing reliable predictions in different parts of the protein such as β-strands and buried residues. Validation on a previously published independent dataset shows that NeEMO has a Pearson correlation coefficient of 0.77 and a standard error of 1 Kcal/mol, outperforming nine recent methods. The NeEMO web server can be freely accessed from URL: http://protein.bio.unipd.it/neemo/.ConclusionsNeEMO offers an innovative and reliable tool for the annotation of amino acid changes. A key contribution are RINs, which can be used for modeling proteins and their interactions effectively. Interestingly, the approach is very general, and can motivate the development of a new family of RIN-based protein structure analyzers. NeEMO may suggest innovative strategies for bioinformatics tools beyond protein stability prediction.

BackgroundLong-term space travel simulation experiments enabled to discover different aspects of human metabolism such as the complexity of NaCl salt balance. Detailed proteomics data were collected during the Mars105 isolation experiment enabling a deeper insight into the molecular processes involved.ResultsWe studied the abundance of about two thousand proteins extracted from urine samples of six volunteers collected weekly during a 105-day isolation experiment under controlled dietary conditions including progressive reduction of salt consumption. Machine learning using Self Organizing maps (SOM) in combination with different analysis tools was applied to describe the time trajectories of protein abundance in urine. The method enables a personalized and intuitive view on the physiological state of the volunteers. The abundance of more than one half of the proteins measured clearly changes in the course of the experiment. The trajectory splits roughly into three time ranges, an early (week 1-6), an intermediate (week 7-11) and a late one (week 12-15). Regulatory modes associated with distinct biological processes were identified using previous knowledge by applying enrichment and pathway flow analysis. Early protein activation modes can be related to immune response and inflammatory processes, activation at intermediate times to developmental and proliferative processes and late activations to stress and responses to chemicals.ConclusionsThe protein abundance profiles support previous results about alternative mechanisms of salt storage in an osmotically inactive form. We hypothesize that reduced NaCl consumption of about 6 g/day presumably will reduce or even prevent the activation of inflammatory processes observed in the early time range of isolation. SOM machine learning in combination with analysis methods of class discovery and functional annotation enable the straightforward analysis of complex proteomics data sets generated by means of mass spectrometry.

BackgroundIt is widely known that castration has a significant effect on the accumulation of adipose tissue. microRNAs (miRNAs) are known to be involved in fat deposition and to be regulated by the androgen-induced androgen receptor (AR). However, there is little understanding of the relationship between miRNAs and fat deposition after castration. In this study, the high-throughput SOLiD sequencing approach was used to identify and characterize miRNA expression in backfat from intact and castrated full-sib male 23-week-old pigs. The patterns of adipogenesis and fat deposition were compared between castrated and intact male pigs.ResultsA total of 366 unique miRNA genes were identified, comprising 174 known pre-miRNAs and 192 novel pre-miRNAs. One hundred and sixty-seven pre-miRNAs were common to both castrated (F3) and intact (F4) male pig small RNA libraries. The novel pre-miRNAs encoded 153 miRNAs/miRNA*s and 141 miRNAs/miRNA*s in the F3 and F4 libraries, respectively. One hundred and seventy-seven miRNAs, including 45 up- and 132 down-regulated, had more than 2-fold differential expression between the castrated and intact male pigs (p-value < 0.001). Thirty-five miRNAs were further selected, based on the expression abundance and differentiation between the two libraries, to predict their targets in KEGG pathways. KEGG pathway analyses suggested that miRNAs differentially expressed between the castrated and intact male pigs are involved in proliferation, apoptosis, differentiation, migration, adipose tissue development and other important biological processes. The expression patterns of eight arbitrarily selected miRNAs were validated by stem-loop reverse-transcription quantitative polymerase chain reaction. These data confirmed the expression tendency observed with SOLiD sequencing. miRNA isomiRs and mirtrons were also investigated in this study. Mirtrons are a recently described category of miRNA relying on splicing rather than processing by the microprocessor complex to generate the RNAi pathway. The functions of miRNAs important for regulating fat deposition were also investigated in this study.ConclusionsThis study expands the number of fat-deposition-related miRNAs in pig. The results also indicate that castration can significantly affect the expression patterns of fat-related miRNAs. The differentially expressed miRNAs may play important roles in fat deposition after castration.