Biology subjects

Kao, H.-W.

Publications and source records attributed to Kao, H.-W..

3 recordsLinked to original sources

bioRxiv2023

Phylogeny and evolution of the SARS-CoV-2 spike gene from December 2022 to February 2023

BackgroundBy the end of 2022, new variants of SARS-CoV-2, such as BQ.1.1.10, BA.4.6.3, XBB, and CH.1.1, emerged with higher fitness than BA.5. MethodsThe file (spikeprot0304), which contains spike protein sequences, isolates collected before March, 4, 2023, was downloaded from Global Initiative on Sharing All Influenza Data (GISAID). A total of 188 different spike protein sequences were chosen, of which their isolates were collected from December 2022 to February 2023. These sequences did not contain undetermined amino acid X, and each spike protein sequence had at least 100 identical isolate sequences in GISAID. Phylogenetic trees were reconstructed using IQ-TREE and MrBayes softwares. A median-join network was reconstructed using PopART software. Selection analyses were conducted using site model of PAML software. ResultsThe phylogenetic tree of the spike DNA sequences revealed that the majority of variants belonged to three major lineages: BA.2 (BA.1.1.529.2), BA.5 (BA.1.1.529.5), and XBB. The median network showed that these lineages had at least six major diversifying centers. The spike DNA sequences of these diversifying centers had the representative accession IDs (EPI_ISL_) of 16040256 (BN.1.2), 15970311 (BA.5), 16028739 (BA.5.11), 16028774 (BQ.1), 16027638 (BQ.1.1.23), and 16044705 (XBB.1.5). Selection analyses revealed 26 amino-acid sites under positive selection. These sites included L5, V83, W152, G181, N185, V213, H245, Y248, D253, S255, S256, G257, R346, R408, K444, V445, G446, N450, L452, N460, F486, Q613, Q675, T883, P1162, and V1264. ConclusionThe spike proteins of SARS-CoV-2 from December 2022 to February 2023 were characterized by a swarm of variants that were evolved from three major lineages: BA.2 (BA.1.1.529.2), BA.5 (BA.1.1.529.5), and XBB. These lineages had at least six diversifying centers. Selection analysis identified 26 amino acid sites were under positive selection. Continued surveillance and research are necessary to monitor the evolution and potential impact of these variants on public health.

evolutionary biology↗

bioRxiv2020

Mycena genomes resolve the evolution of fungal bioluminescence

Mushroom-forming fungi in the order Agaricales represent an independent origin of bioluminescence in the tree of life, yet the diversity, evolutionary history, and timing of the origin of fungal luciferases remain elusive. We sequenced the genomes and transcriptomes of five bonnet mushroom species (Mycena spp.), a diverse lineage comprising the majority of bioluminescent fungi. Two species with haploid genome assemblies [~]150Mb are amongst the largest in Agaricales, and we found that a variety of repeats between Mycena species were differentially mediated by DNA methylation. We show that bioluminescence evolved in the last common ancestor of mycenoid and the marasmioid clade of Agaricales and was maintained through at least 160 million years of evolution. Analyses of synteny across genomes of bioluminescent species resolved how the luciferase cluster was derived by duplication and translocation, frequently rearranged and lost in most Mycena species, but conserved in the Armillaria lineage. Luciferase cluster members were co-expressed across developmental stages, with highest expression in fruiting body caps and stipes, suggesting fruiting-related adaptive functions. Our results contribute to understanding a de novo origin of bioluminescence and the corresponding gene cluster in a diverse group of enigmatic fungal species. SignificanceWe present the genomes of five new bonnet mushroom Mycena species, formerly the last fungal bioluminescent lineage lacking reference genomes. These genome-scale datasets allowed us to construct an evolutionary model pinpointing all possible changes in the luciferase cluster across all fungi and additional genes involved in bioluminescence. We show that luciferase clusters were differentially lost in different fungal lineages and in particular a substantial loss was observed in the Mycena lineage. This can be attributed to genome regions of Mycena underwent different evolutionary dynamics. Our findings offer insights into the evolution of how a gene cluster that emerged 160 million years ago and was frequently lost or maintained due to differences in genome plasticity.

genomics↗

bioRxiv2019

The Impact of Edema and Fiber Crossing on Diffusion MRI Metrics: DBSI vs. Diffusion ODF

PurposeDiffusion tensor imaging (DTI) has been employed for over two decades to noninvasively quantify central nervous system (CNS) diseases/injuries. However, DTI is an inadequate simplification of diffusion modeling in the presence of co-existing inflammation, edema, and crossing nerve fibers.\n\nMethodsWe employed a tissue phantom using fixed mouse trigeminal nerves coated with various amounts of agarose gel to mimic crossing fibers in the presence of vasogenic edema. Diffusivity measures derived by DTI and diffusion basis spectrum imaging (DBSI) were compared at increasing levels of simulated edema and degrees of fiber crossing. Further, we assessed the ability of DBSI, diffusion kurtosis imaging (DKI), generalized q-sampling imaging (GQI), q-ball imaging (QBI), and neurite orientation dispersion and density imaging (NODDI) to resolve fiber crossing, in reference to the gold standard angles measured from structural images.\n\nResultsDTI-computed diffusivities and fractional anisotropy (FA) were significantly confounded by gelmimicked edema and crossing fibers. Conversely, DBSI calculated accurate diffusivities of individual fibers regardless of the extent of simulated edema and degrees of fiber crossing angles. Additionaly, DBSI accurately and consistently estimated crossing angles in various conditions of gel-mimicked edema when comparing with gold standard (r2=0.92, p=1.9x10-9, bias=3.9{degrees}). Small crossing angles and edema sinficantly impact dODF, making DKI, GQI and QBI less accurate in detecting and estimating fibers corrsing angles. Lastly, we demonstrate DBSIs superiority over DTI for recovering and delineating white matter tracts in peritumoral edema for preoperative planning of surgical resection.\n\nConclusionsDBSI is able to separate two crossing fibers and accurately recover their diffusivities in a complex environment characterized by increasing crossing angles and amounts of gel-mimicked edema. DBSI also indicated better angular resolution capability compared with DKI, QBI and GQI.

neuroscience↗