Biology subjects

Tamiru, M.

Publications and source records attributed to Tamiru, M..

3 recordsLinked to original sources

bioRxiv2020

Population structure and transmission of Mycobacterium bovis in Ethiopia

Bovine tuberculosis (bTB) is endemic in cattle in Ethiopia, a country that hosts the largest national cattle herd in Africa. The intensive dairy sector, most of which is peri-urban, has the highest prevalence of disease. Previous studies in Ethiopia have demonstrated that the main cause is Mycobacterium bovis (M. bovis), which has been investigated using conventional molecular tools including deletion typing, spoligotyping and Mycobacterial interspersed repetitive unit-variable number tandem repeat (MIRU-VNTR). Here we use whole genome sequencing (WGS) to examine the population structure of M. bovis in Ethiopia. A total of 134 M. bovis isolates were sequenced including 128 genomes from 85 mainly dairy cattle and six genomes isolated from humans, originating from 12 study sites across Ethiopia. These genomes provided a good representation of the previously described population structure of M. bovis, based on spoligotyping and demonstrated that the population is dominated by the clonal complexes African 2 (Af2) and European 3 (Eu3). A range of within-host diversity was observed amongst the isolates and evidence was found for both short- and long-distance transmission. Detailed analysis of available genomes from the Eu3 clonal complex combined with previously published genomes revealed two distinct introductions of this clonal complex into Ethiopia between 1950 and 1987, likely from Europe. This work is important to help better understand bTB transmission in cattle in Ethiopia and can potentially inform national strategies for bTB control in Ethiopia and beyond.

genomics↗

bioRxiv2019

Identification of candidate flowering and sex genes in white Guinea yam (D. rotundata Poir.) by SuperSAGE transcriptome profiling

Dioecy (distinct male and female individuals) combined with scarce to non-flowering are common features of cultivated yam (Dioscorea spp.). However, the molecular mechanisms underlying flowering and sex determination in Dioscorea are unknown. We conducted SuperSAGE transcriptome profiling of male, female and monoecious individuals to identify flowering and sex-related genes in white Guinea yam (D. rotundata). SuperSAGE analysis generated a total of 20,236 unique tags, of which 13,901 were represented by a minimum of 10 tags. Of these, 88 tags were significantly differentially expressed in male, female and monoecious plants. Of the 88 differentially expressed SuperSAGE tags, 18 corresponded to genes previously implicated in flower development and sex determination in multiple plant species. We validated the SuperSAGE data with quantitative real-time PCR (qRT-PCR)-based analysis of the expression of four candidate genes. Our findings suggest that mechanisms of flowering and sex determination are likely conserved in Dioscorea. We further investigated the flowering patterns of 1938 D. rotundata accessions representing diverse geographical origins over two years, revealing that over 85% of the accessions are either male or non-flowering, and that less than 15% are female, while monoecious plants are rare. Intensity of flowering appeared to be a function of sex, with male plants flowering more abundantly than female ones. Candidate genes identified in this study can be targeted with the aim to induce regular flowering in poor to non-flowering cultivars. Findings of the study provide important inputs for further studies aiming to overcome the challenge of flowering in yams and to improve the efficiency of yam breeding.

plant biology↗

bioRxiv2018

AgriSeqDB: an online RNA-Seq database for functional studies in agriculturally relevant plant species

BackgroundThe genome-wide expression profile of genes in different tissues/cell types and developmental stages is a vital component of many functional genomic studies. Transcriptome data obtained by RNA-sequencing (RNA-Seq) is often deposited in public databases that are made available via data portals. Data visualization is one of the first steps in assessment and hypothesis generation. However, these databases do not typically include visualization tools and establishing one is not trivial for users who are not computational experts. This, as well as the various formats in which data is commonly deposited, makes the processes of data access, sharing and utility more difficult. Our goal was to provide a simple and user-friendly repository that meets these needs for datasets from major agricultural crops.\n\nDescriptionAgriSeqDB (https://expression.latrobe.edu.au/agriseqdb), is a database for viewing, analysing and interpreting developmental and tissue/cell-specific transcriptome data from several species, including major agricultural crops such as wheat, rice, maize, barley and tomato. The disparate manner in which public transcriptome data is often warehoused and the challenge of visualizing raw data are both major hurdles to data reuse. The popular eFP browser does an excellent job of presenting transcriptome data in an easily interpretable view, but previous implementation has been mostly on a case-by-case basis. Here we present an integrated visualisation database of transcriptome datasets from six species that did not previously have public-facing visualisations. We combine the eFP browser, for gene-by-gene investigation, with the Degust browser, which enables visualisation of all transcripts across multiple samples. The two visualisation interfaces launch from the same point, enabling users to easily switch between analysis modes. The tools allow users, even those without bioinformatics expertise, to mine into datasets and understand the behaviour of transcripts of interest across samples and time. We have also incorporated an additional graphic download option to simplify incorporation into presentations or publications.\n\nConclusionPowered by eFP and Degust browsers, AgriSeqDB is a quick and easy-to-use platform for data analysis and visualization in five crops and Arabidopsis. Furthermore, it provides a tool that makes it easy for researchers to share their datasets, promoting research collaborations and dataset reuse.

genomics↗