Journal Articles

Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915

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    A new mechanism for a familiar mutation - bovine DGAT1 K232A modulates gene expression through multi-junction exon splice enhancement
    (BioMed Central Ltd, 2020-08-26) Fink T; Lopdell TJ; Tiplady K; Handley R; Johnson TJJ; Spelman RJ; Davis SR; Snell RG; Littlejohn MD
    BACKGROUND: The DGAT1 gene encodes an enzyme responsible for catalysing the terminal reaction in mammary triglyceride synthesis, and underpins a well-known pleiotropic quantitative trait locus (QTL) with a large influence on milk composition phenotypes. Since first described over 15 years ago, a protein-coding variant K232A has been assumed as the causative variant underlying these effects, following in-vitro studies that demonstrated differing levels of triglyceride synthesis between the two protein isoforms. RESULTS: We used a large RNAseq dataset to re-examine the underlying mechanisms of this large milk production QTL, and hereby report novel expression-based functions of the chr14 g.1802265AA > GC variant that encodes the DGAT1 K232A substitution. Using expression QTL (eQTL) mapping, we demonstrate a highly-significant mammary eQTL for DGAT1, where the K232A mutation appears as one of the top associated variants for this effect. By conducting in vitro expression and splicing experiments in bovine mammary cell culture, we further show modulation of splicing efficiency by this mutation, likely through disruption of an exon splice enhancer as a consequence of the allele encoding the 232A variant. CONCLUSIONS: The relative contributions of the enzymatic and transcription-based mechanisms now attributed to K232A remain unclear; however, these results suggest that transcriptional impacts contribute to the diversity of lactation effects observed at the DGAT1 locus.
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    Transcriptomic Identification of a Unique Set of Nodule-Specific Cysteine-Rich Peptides Expressed in the Nitrogen-Fixing Root Nodule of Astragalus sinicus
    (The American Phytopathological Society in cooperation with the International Society for Molecular Plant-Microbe Interactions, 2022-10-08) Wei F; Liu Y; Zhou D; Zhao W; Chen Z; Chen D; Li Y; Zhang X-X
    Legumes in the inverted repeat-lacking clade (IRLC) each produce a unique set of nodule-specific cysteine-rich (NCR) peptides, which act in concert to determine the terminal differentiation of nitrogen-fixing bacteroid. IRLC legumes differ greatly in their numbers of NCR and sequence diversity. This raises the significant question how bacteroid differentiation is collectively controlled by the specific NCR repertoire of an IRLC legume. Astragalus sinicus is an IRLC legume that forms indeterminate nodules with its microsymbiont Mesorhizobium huakuii 7653R. Here, we performed transcriptome analysis of root and nodule samples at 3, 7, 14, 28 days postinoculation with M. huakuii 7653R and its isogenic ∆bacA mutant. BacA is a broad-specificity peptide transporter required for the host-derived NCRs to target rhizobial cells. A total of 167 NCRs were identified in the RNA transcripts. Comparative sequence and electrochemical analysis revealed that A. sinicus NCRs (AsNCRs) are dominated by a unique cationic group (termed subgroup C), whose mature portion is relatively long (>60 amino acids) and phylogenetically distinct and possessing six highly conserved cysteine residues. Subsequent functional characterization showed that a 7653R variant harboring AsNCR083 (a representative of subgroup C AsNCR) displayed significant growth inhibition in laboratory media and formed ineffective white nodules on A. sinicus with irregular symbiosomes. Finally, bacterial two-hybrid analysis led to the identification of GroEL1 and GroEL3 as the molecular targets of AsNCR067 and AsNCR076. Together, our data contribute to a systematic understanding of the NCR repertoire associated with the A. sinicus and M. huakuii symbiosis.
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    Small-Angle X-ray Scattering (SAXS) Measurements of APOBEC3G Provide Structural Basis for Binding of Single-Stranded DNA and Processivity
    (MDPI (Basel, Switzerland), 2022-09-06) Barzak FM; Ryan TM; Mohammadzadeh N; Harjes S; Kvach MV; Kurup HM; Krause KL; Chelico L; Filichev VV; Harjes E; Jameson GB; De la Torre JC; Andrei G
    APOBEC3 enzymes are polynucleotide deaminases, converting cytosine to uracil on single-stranded DNA (ssDNA) and RNA as part of the innate immune response against viruses and retrotransposons. APOBEC3G is a two-domain protein that restricts HIV. Although X-ray single-crystal structures of individual catalytic domains of APOBEC3G with ssDNA as well as full-length APOBEC3G have been solved recently, there is little structural information available about ssDNA interaction with the full-length APOBEC3G or any other two-domain APOBEC3. Here, we investigated the solution-state structures of full-length APOBEC3G with and without a 40-mer modified ssDNA by small-angle X-ray scattering (SAXS), using size-exclusion chromatography (SEC) immediately prior to irradiation to effect partial separation of multi-component mixtures. To prevent cytosine deamination, the target 2'-deoxycytidine embedded in 40-mer ssDNA was replaced by 2'-deoxyzebularine, which is known to inhibit APOBEC3A, APOBEC3B and APOBEC3G when incorporated into short ssDNA oligomers. Full-length APOBEC3G without ssDNA comprised multiple multimeric species, of which tetramer was the most scattering species. The structure of the tetramer was elucidated. Dimeric interfaces significantly occlude the DNA-binding interface, whereas the tetrameric interface does not. This explains why dimers completely disappeared, and monomeric protein species became dominant, when ssDNA was added. Data analysis of the monomeric species revealed a full-length APOBEC3G-ssDNA complex that gives insight into the observed "jumping" behavior revealed in studies of enzyme processivity. This solution-state SAXS study provides the first structural model of ssDNA binding both domains of APOBEC3G and provides data to guide further structural and enzymatic work on APOBEC3-ssDNA complexes.
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    Computational identification of four spliceosomal snRNAs from the Deep-Branching Eukaryote Giardia intestinalis
    (PloS ONE, 2008) Chen XS; White WT; Collins LJ; Penny D
    RNAs processing other RNAs is very general in eukaryotes, but is not clear to what extent it is ancestral to eukaryotes. Here we focus on pre-mRNA splicing, one of the most important RNA-processing mechanisms in eukaryotes. In most eukaryotes splicing is predominantly catalysed by the major spliceosome complex, which consists of five uridine-rich small nuclear RNAs (U-snRNAs) and over 200 proteins in humans. Three major spliceosomal introns have been found experimentally in Giardia; one Giardia U-snRNA (U5) and a number of spliceosomal proteins have also been identified. However, because of the low sequence similarity between the Giardia ncRNAs and those of other eukaryotes, the other U-snRNAs of Giardia had not been found. Using two computational methods, candidates for Giardia U1, U2, U4 and U6 snRNAs were identified in this study and shown by RT-PCR to be expressed. We found that identifying a U2 candidate helped identify U6 and U4 based on interactions between them. Secondary structural modelling of the Giardia U-snRNA candidates revealed typical features of eukaryotic U-snRNAs. We demonstrate a successful approach to combine computational and experimental methods to identify expected ncRNAs in a highly divergent protist genome. Our findings reinforce the conclusion that spliceosomal small-nuclear RNAs existed in the last common ancestor of eukaryotes.
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    Antigen-specific cytotoxic T lymphocytes target airway CD103+ and CD11b+ dendritic cells to suppress allergic inflammation
    (Nature Publishing Group, 2016-01) Daniels NJ; Hyde E; Ghosh S; Seo K; Price KM; Hoshino K; Kaisho T; Okada T; Ronchese F
    Allergic airway inflammation is driven by the recognition of inhaled allergen by T helper type 2 (Th2) cells in the airway and lung. Allergen-specific cytotoxic T lymphocytes (CTLs) can strongly reduce airway inflammation, however, the mechanism of their inhibitory activity is not fully defined. We used mouse models to show that allergen-specific CTLs reduced early cytokine production by Th2 cells in lung, and their subsequent accumulation and production of interleukin (IL)-4 and IL-13. In addition, treatment with specific CTLs also increased the proportion of caspase+ dendritic cells (DCs) in mediastinal lymph node (MLN), and decreased the numbers of CD103+ and CD11b+ DCs in the lung. This decrease required expression of the cytotoxic mediator perforin in CTLs and of the appropriate MHC-antigen ligand on DCs, suggesting that direct CTL-DC contact was necessary. Lastly, lung imaging experiments revealed that in airway-challenged mice XCR1-GFP+ DCs, corresponding to the CD103+ DC subset, and XCR1-GFP− CD11c+ cells, which include CD11b+ DCs and alveolar macrophages, both clustered in the areas surrounding the small airways and were closely associated with allergen-specific CTLs. Thus, allergen-specific CTLs reduce allergic airway inflammation by depleting CD103+ and CD11b+ DC populations in the lung, and may constitute a mechanism through which allergic immune responses are regulated.
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    De novo transcriptome assembly, functional annotation and differential gene expression analysis of juvenile and adult E. fetida, a model oligochaete used in ecotoxicological studies
    (Sociedad de Biología de Chile, 17/02/2017) Thunders MC; Cavanagh J; Li Y
    BACKGROUND: Earthworms are sensitive to toxic chemicals present in the soil and so are useful indicator organisms for soil health. Eisenia fetida are commonly used in ecotoxicological studies; therefore the assembly of a baseline transcriptome is important for subsequent analyses exploring the impact of toxin exposure on genome wide gene expression. RESULTS: This paper reports on the de novo transcriptome assembly of E. fetida using Trinity, a freely available software tool. Trinotate was used to carry out functional annotation of the Trinity generated transcriptome file and the transdecoder generated peptide sequence file along with BLASTX, BLASTP and HMMER searches and were loaded into a Sqlite3 database. To identify differentially expressed transcripts; each of the original sequence files were aligned to the de novo assembled transcriptome using Bowtie and then RSEM was used to estimate expression values based on the alignment. EdgeR was used to calculate differential expression between the two conditions, with an FDR corrected P value cut off of 0.001, this returned six significantly differentially expressed genes. Initial BLASTX hits of these putative genes included hits with annelid ferritin and lysozyme proteins, as well as fungal NADH cytochrome b5 reductase and senescence associated proteins. At a cut off of P = 0.01 there were a further 26 differentially expressed genes. CONCLUSION: These data have been made publicly available, and to our knowledge represent the most comprehensive available transcriptome for E. fetida assembled from RNA sequencing data. This provides important groundwork for subsequent ecotoxicogenomic studies exploring the impact of the environment on global gene expression in E. fetida and other earthworm species.
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    DeepPN: a deep parallel neural network based on convolutional neural network and graph convolutional network for predicting RNA-protein binding sites.
    (29/06/2022) Zhang J; Liu B; Wang Z; Lehnert K; Gahegan M
    BACKGROUND: Addressing the laborious nature of traditional biological experiments by using an efficient computational approach to analyze RNA-binding proteins (RBPs) binding sites has always been a challenging task. RBPs play a vital role in post-transcriptional control. Identification of RBPs binding sites is a key step for the anatomy of the essential mechanism of gene regulation by controlling splicing, stability, localization and translation. Traditional methods for detecting RBPs binding sites are time-consuming and computationally-intensive. Recently, the computational method has been incorporated in researches of RBPs. Nevertheless, lots of them not only rely on the sequence data of RNA but also need additional data, for example the secondary structural data of RNA, to improve the performance of prediction, which needs the pre-work to prepare the learnable representation of structural data. RESULTS: To reduce the dependency of those pre-work, in this paper, we introduce DeepPN, a deep parallel neural network that is constructed with a convolutional neural network (CNN) and graph convolutional network (GCN) for detecting RBPs binding sites. It includes a two-layer CNN and GCN in parallel to extract the hidden features, followed by a fully connected layer to make the prediction. DeepPN discriminates the RBP binding sites on learnable representation of RNA sequences, which only uses the sequence data without using other data, for example the secondary or tertiary structure data of RNA. DeepPN is evaluated on 24 datasets of RBPs binding sites with other state-of-the-art methods. The results show that the performance of DeepPN is comparable to the published methods. CONCLUSION: The experimental results show that DeepPN can effectively capture potential hidden features in RBPs and use these features for effective prediction of binding sites.