Biology subjects

Xin, Z.

Publications and source records attributed to Xin, Z..

3 recordsLinked to original sources

bioRxiv2018

GIGANTEA promotes sorghum flowering by stimulating floral activator gene expression

iv. FundingThis work was supported by USDA-ARS CRIS projects 2030-21000-039-00D and 2030-21000-049-00D to F.G.H.\n\nv. AbstractThe C4 grass Sorghum bicolor is an important grain and subsistence crop, animal forage, and cellulosic biofuel feedstock that is tolerant of abiotic stresses and marginal soils. Sorghum is short-day flowering, an obstacle for adaptation as a grain crop but a benefit as a biofuel feedstock. To identify genes underlying sorghum photoperiodic flowering behavior this study characterized the Sbgi-ems1 nonsense mutation in the sorghum GIGANTEA (SbGI) gene from a sequenced M4 EMS-mutagenized BTx623 population. Sbgi-ems1 plants had reduced stature and leaf blades exhibiting increased lateral growth combined with reduced proximal-distal growth. Mutant plants flowered later than normal siblings under long-day conditions provided by greenhouse or field. Delayed flowering in Sbgi-ems1 plants accompanied by an increase in internode number, indicating an extended vegetative growth phase prior to flowering. Sbgi-ems1 plants had reduced expression of floral activator genes SbCO and SbEhd1 and downstream FT-like florigen genes SbFT, SbCN8, and SbCN12. Therefore, SbGI accelerates flowering by promotion of SbCO and SbEhd1 expression. Circadian clock-associated genes SbTOC1 and SbLHY had disrupted expression in Sbgi-ems1 plants. This work demonstrates SbGI is a key upstream activator in the regulatory networks dictating sorghum flowering time and growth, as well as gene expression regulation within the circadian clock.\n\nSummary StatementSorghum GIGANTEA contributes to flowering time, growth, and the circadian clock with activities opposite to its maize homolog. GI occupies a conserved position within regulatory networks but has plastic activity.

plant biology↗

bioRxiv2018

How do wind speed, release height, seed morphology interact to determine seed dispersal trajectory of Calligonum (Polygonaceae) species

How seed dispersal trajectory shifts with abiotic and biotic factors and what is the relationship between seed dispersal distance and dispersal trajectory are remain unclear. We used wind tunnel and video camera to track the seed dispersal trajectory of 7 Calligonum species with different appendages under the different wind speeds and the release heights. Dispersal trajectories and distances were determined by video analysis and spatial coordinate transformation. Based on perspective principle, 4 modes of trajectories were determined. Wind speed, seed mass and release height were the key factors determining seed dispersal trajectory modes. Release height and wind speed tended to have the strongest explanatory power on seeds with bristles and wings, respectively. Different trajectory modes lead to different dispersal distance, while the same dispersal distance can be the result of different trajectory modes. The proportion of species trajectory modes formed its trajectory spectrum. Wind speed tends to have strong influence on light and low-wind-loading seeds, release height tends to have that on heavy and high-wind-loading seeds. Species with high proportion of horizontal projectile and projectile have high dispersal capacity, vice versa. Therefore, trajectory spectrum of a species reveals its primary dispersal strategies and evolutionary consequences.

ecology↗

bioRxiv2017

HAPDeNovo: a haplotype-based approach for filtering and phasing de novo mutations in linked read sequencing data

BackgroundDe novo mutations (DNMs) are associated with neurodevelopmental and congenital diseases, and their detection can contribute to understanding disease pathogenicity. However, accurate detection is challenging because of their small number relative to the genome-wide false positives in next generation sequencing (NGS) data. Software such as DeNovoGear and TrioDeNovo have been developed to detect DNMs, but at good sensitivity they still produce many false positive calls.\n\nResultsTo address this challenge, we develop HAPDeNovo, a program that leverages phasing information from linked read sequencing, to remove false positive DNMs from candidate lists generated by DNM-detection tools. Short reads from each phasing block are allocated to each of the two haplotypes followed by generating a haploid genotype for each putative DNM.HAPDeNovo removes variants that are called as heterozygous in one of the haplotypes because they are almost certainly false positives. Our experiments on 10X Chromium linked read sequencing trio data reveal that HAPDeNovo eliminates 80% to 99% of false positives regardless of how large the candidate DNM set is.\n\nConclusionsHAPDeNovo leverages the haplotype information from linked read sequencing to remove spurious false positive DNMs effectively, and it increases accuracy of DNM detection dramatically without sacrificing sensitivity.

genomics↗