Mutation of OsCAX1a Results in Panicle Degeneration in Rice

Background: Rice is one of the most common cereal crops in China. Increasing the yield of rice has always been a primary purpose of rice breeding. However, panicle degeneration in rice, a complex characteristic regulated by many genes and commonly encountered in rice production, seriously reduces the yield. Findings: In this study, we obtained a new apical panicle degeneration mutant named ym48, which exhibits a serious degeneration rate and reduced grain yield in rice. After fine mapping, the OsCAX1a gene responsible for Ca2+ selection and transportation was identified. In the ym48 mutant of the OsCAX1a gene, a A to G substitution was noted at the 190th nucleotide, and the corresponding 64th amino acid was changed from threonine to alanine. Also, the tolerance from Ca2+ stress was damaged due to the mutation. Phylogenetics, protein sequence alignment and motif identification of CAX family members in Arabidopsis and rice indicated that this mutation site was highly conserved and might play an essential role in Ca2+ transportation. Moreover, the OsCAX1a expression pattern was analyzed in rice. qRT-PCR and GUS (β-glucuronidase) staining experiments showed that OsCAX1a was highly expressed in roots, stems and panicles and that its expression increased with panicle development. Conclusions: These results demonstrated that OsCAX1a played an essential role in the regulation of panicle development for the first time and mutation of OsCAX1a would generate the panicle degeneration in rice. This study provided a new view point to explore the mechanism of panicle development and degeneration in rice.

Deep embedding and alignment of protein sequences

Protein sequence alignment is a key component of most bioinformatics pipelines to study the structures and functions of proteins. Aligning highly divergent sequences remains, however, a difficult task that current algorithms often fail to perform accurately, leaving many proteins or open reading frames poorly annotated. Here, we leverage recent advances in deep learning for language modelling and differentiable programming to propose DEDAL, a flexible model to align protein sequences and detect homologs. DEDAL is a machine learning-based model that learns to align sequences by observing large datasets of raw protein sequences and of correct alignments. Once trained, we show that DEDAL improves by up to two- or three-fold the alignment correctness over existing methods on remote homologs, and better discriminates remote homologs from evolutionarily unrelated sequences, paving the way to improvements on many downstream tasks relying on sequence alignment in structural and functional genomics.

A Framework for Executing Protein Sequence Alignment in Cloud Computing Services

Protein sequence alignment is a task of great relevance in Bioinformatics and the Hirschberg algorithm is widely used for this task. This work proposes a framework for executing sequence alignment with the Hirschberg algorithm in different cloud computing services. In experiments, our framework was used to align HIV-1 protease sequences using different instances of AWS EC2 and different configurations of AWS Lambda functions.The results show that, for this application, there is a tradeoff between the expected execution time and the cost, e.g., in most cases AWS Lambda provides the best runtime, however at a higher USD cost. In this context, it is important to have a framework that helps in deciding which approach is most appropriate.

The Isolation and Identification of Anthocyanin-Related GSTs in Chrysanthemum

Anthocyanin is the crucial pigment for the coloration of red chrysanthemum flowers, which synthesizes in the cytosol and is transported to the vacuole for stable storage. In general, glutathione S-transferases (GSTs) play a vital role in this transport. To date, there is no functional GST reported in chrysanthemums. Here, a total of 94 CmGSTs were isolated from the chrysanthemum genome, with phylogenetic analysis suggesting that 16 members of them were clustered into the Phi subgroup which was related to anthocyanin transport. Among them, the expression of CmGST1 was positively correlated with anthocyanin accumulation. Protein sequence alignment revealed that CmGST1 included anthocyanin-related GST-specific amino acid residues. Further transient overexpression experiments in tobacco leaves showed that CmGST1 could promote anthocyanin accumulation. In addition, a dual-luciferase assay demonstrated that CmGST1 could be regulated by CmMYB6, CmbHLH2 and CmMYB#7, which was reported to be related to anthocyanin biosynthesis. Taken together, we suggested that CmGST1 played a key role in anthocyanin transport and accumulation in chrysanthemums.

SIMILE enables alignment of fragmentation mass spectra with statistical significance

Interrelating compounds according to their aligned fragmentation spectra is central to tandem mass spectrometry-based metabolomics. However, current alignment algorithms do not provide statistical significance and compounds that have multiple delocalized structural differences often fail to have their fragment ions aligned. Significant Interrelation of MS/MS Ions via Laplacian Embedding (SIMILE) is a new tool inspired by protein sequence alignment for aligning fragmentation spectra with statistical significance and allowance for multiple chemical differences. We found SIMILE yields 550% more pairs of structurally similar compounds than commonly used cosine-based scoring algorithms, and anticipate SIMILE will fill an important role by also providing p-values for fragmentation spectra alignments to explore structural relationships between compounds.

Current strategic limitations of phylogenetic tools badly impact the inference of an evolutionary tree

For drawing an evolutionary relationship among several protein sequences, the phylogenetic tree is usually constructed through maximum likelihood-based algorithms. To improve the accuracy of these methodologies, many parameters like bootstrap methods, correlation coefficient and residue-substitution models are presumably over-ranked to derive biologically credible relationships. Although the accuracy of protein sequence alignment and the substitution matrix are preliminary constraints to define the biological accuracy of the overlapped sequences/residues, the alignment is not iteratively optimized through the statistical testing of residue-substitution models. The study majorly highlights the potential pitfalls that significantly affect the accuracy of an evolutionary protocol. It emphasizes the need for a more accurate scrutiny of the entire phylogenetic methodology. The need of iterative optimizations is illustrated to construct a biologically credible and not mathematically optimal tree for a sequence dataset.