Biology subjects

Xu, Y.-C.

Publications and source records attributed to Xu, Y.-C..

6 recordsLinked to original sources

bioRxiv2025

Tryptophanol, a novel auxin analog found in marine diatoms, enhances nitrogen assimilation

Diatoms exhibit superior competitive capacity in nitrogen assimilation, largely contributing to their growth, although the mechanisms underpinning their success have not been completely understood. Here, a non-ribosomal peptide synthase-like (PtNRPS1, with an unusual domain structure A-T-R1-R2) gene was found to play a vital role in short-term nitrogen assimilation in the marine diatom Phaeodactylum tricornutum. In vitro biochemical assays and in vivo overexpression confirmed that PtNRPS1 catalyzed two sequential two-electron reductions of L-tryptophan to tryptophanol. Tryptophanol exhibits high structural and functional similarities to indole-3-acetic acid (IAA), the most typical phytohormone auxin. Surprisingly, the effective concentration of tryptophanol was lower than that of IAA by as much as 2-5 orders of magnitude for P. tricornutum. Compared with the action of IAA, a distinct molecular mode for tryptophanol was revealed by transcriptomic analysis, resulting mainly in enhanced short-term nitrogen assimilation, which was also confirmed by the elevated nitrogen uptake rates determined by stable-isotope tracking. Finally, global distribution of PtNRPS1 homologues from stramenopiles was found to be positively correlated with the abundance of genes involved in nitrogen assimilation pathways. Overall, our study provides evidence of an auxin-like derivative synthesized by an NRPS in a diatom. We speculate that tryptophanol may accelerate nitrogen assimilation, conferring advantages in the competition for nitrogen in the ocean.

plant biology↗

bioRxiv2025

Development of Surface Enhanced Raman Spectra coupled with Machine Learning Analysis for Differentiation of Closely Related Species within Enterobacter cloacae Complex

BackgroundEnterobacter cloacae complex (ECC) is an important nosocomial pathogen and consists of multiple similar species. The taxonomy of ECC has been consecutively updated, adding to its identification difficulty. MethodsA total of 92 ECC strains isolated from bloodstream infections during 2015-2020 were collected from a tertiary hospital in China. All the strains were identified by Vitek 2 Compact and Vitek MS and then subjected to whole genome sequencing (WGS) for average nucleotide identity (ANI) analysis. Surfaced-enhanced Raman spectroscopy (SERS) with a set of machine learning algorithms was applied in identifying species within ECC. ResultsSeven species were identified through ANI, including 28 E. hormaechei subsp. steigerwaltii, 17 E. hormaechei subsp. xiangfangensis, 12 E. cloacae, 11 each of E. hormaechei subsp. hoffmannii and E. bugandensis, seven E. kobei and six E. roggenkampii. The Vitek 2 compact indistinguishably identified all the strains as ECC and Vitek MS correctly identified one strain of E. kobei while achieving ambiguous results for all the other isolates. SERS combined with XGBoost model achieved 97.75% accuracy with an area under the ROC curve value of 0.9982 in the identification of ECC. ConclusionSERS coupled with machine learning algorithms holds a promising potential to acquire early prediction of ECC, outperforming the capabilities of other methods.

microbiology↗

bioRxiv2025

Mutations of short tandem repeats explain abundant trait heritability in Arabidopsis

Short tandem repeats (STRs) mutations are one of the most important fuels of evolution, but are often largely ignored despite their huge effects on phenotypic variations. Here, we leveraged seven Arabidopsis mutation accumulation lines to assess the STR mutation rate, which ranged from 1.93x10-2 to 4.40x10-3 per STR locus per generation and was much higher than the common SNP mutation rate at the level of 10-9-10-8. Interspecific comparison revealed rapid STR turnover, with a large proportion (71.8%) only present in Arabidopsis. In addition, intraspecific comparison of the ten assembled Arabidopsis genomes revealed that 29.3% of STRs were presence/absence variation (PAV), 36.5% of STRs were length variation (LV), 13.0% of STRs were no variation (NV) and the 21.1% STR loci with both LV and PAV. Based on 413 RNA-seq datasets, we identified that length variation of 3,871 STR loci (eSTRs) was associated with gene expression and 651 STR loci (sSTRs) were associated with splicing variation, and over one thousand eSTRs or sSTRs were linked with published genome-wide association study signals. In addition, a direct association between STR length variation and phenotypic variation uncovered a series of STR-trait associations. More notably, using the expression levels of 24,175 genes, the splice strength values of 12,784 splice sites, and 16 phenotypes of natural Arabidopsis populations, we revealed that the STR heritability is 0.111, 0.143 and 0.101, respectively. Overall, our results not only revealed the evolutionary dynamics of STRs, but highlighted the importance of STR variations as an important contributor to missing heritability in the regulation of complex traits.

evolutionary biology↗

bioRxiv2022

Forces driving transposable element load variation during Arabidopsis range expansion

Genetic load refers to the accumulated and potentially life-threatening deleterious mutations in populations. Understanding the mechanisms underlying genetic load variation of transposable elements (TEs), one major large-effect mutations, during range expansion is an intriguing question in biology. Here, we used 1,115 globally natural accessions of Arabidopsis thaliana, to study the driving forces of TE load variation during its range expansion. The TE load increased with range expansion, especially in the recently established Yangtze River basin population. The effective population size explained 62.0% of the variance in TE load, and high transposition rate and positive selection or hitch-hiking effect contributed to the accumulation of TEs in the expanded populations. We genetically mapped the candidate causal genes or TEs and revealed the genetic architecture of TE load. Overall, this study reveals the variation in the genetic load of TEs during Arabidopsis expansion and highlights the causes of TE load variation.

evolutionary biology↗

bioRxiv2022

Chlamydomonas mutant hpm91 lacking PGR5 is a scalable and valuable strain for algal hydrogen (H2) production

Clean and sustainable H2 production is essential toward a carbon-neutral world. H2 generation by Chlamydomonas reinhardtii is an attractive approach for solar-H2 from H2O. However, it is currently not scalable because of lacking ideal strains. Here, we explore hpm91, a previously reported PGR5-deletion mutant with remarkable H2 production, that possesses numerous valuable attributes towards large-scale application and in-depth study issues. We show that hpm91 is at least 100-fold scalable (upto 10 liter) with H2 collection sustained for averagely 26 days and 7287 ml H2/10L-HPBR. Also, hpm91 is robust and active over the period of sulfur-deprived H2 production, most likely due to decreased intracellular ROS relative to wild type. Moreover, quantitative proteomic analysis revealed its features in photosynthetic antenna, primary metabolic pathways and anti-ROS responses. Together with success of new high-H2-production strains derived from hpm91, we highlight that hpm91 is a potent strain toward basic and applied research of algal-H2 photoproduction.

biochemistry↗

bioRxiv2021

Accurate prediction of protein torsion angles using evolutionary signatures and recurrent neural network

The amino acid sequence of a protein contains all the necessary information to specify its shape, which dictates its biological activities. However, it is challenging and expensive to experimentally determine the three-dimensional structure of proteins. The backbone torsion angles, as an important structural constraint, play a critical role in protein structure prediction, and accurately predicting the angles can considerably advance the tertiary structure prediction by accelerating efficient sampling of the large conformational space for low energy structures. On account of the rapid growth of protein databases and striking breakthroughs in deep learning algorithms, computational advances allow us to extract knowledge from large-scale data to address key biological questions. Here we propose evolutionary signatures that are computed from protein sequence profiles, and a deep neural network, termed ESIDEN, that adopts a straightforward architecture of recurrent neural networks with a small number of learnable parameters. The proposed ESIDEN is validated on three benchmark datasets, including D2020, TEST2016/2018, and CASPs datasets. On the D2020, using the combination of the four novel features and basic features, the ESIDEN achieves the mean absolute error (MAE) of 15.7 and 19.8 for{phi} and{psi} , respectively. Comparing to the best-so-far methods, we show that the ESIDEN significantly improves the angle{psi} by the MAE decrements of more than 3.5 degrees on both TEST2016 and TEST2018 and achieves better MAE of the angle{phi} by decrements of at least 0.3 degrees although it adopts simple architecture and fewer learnable parameters. On fifty-nine template-free modeling targets, the ESIDEN achieves high accuracy by reducing the MAEs by 0.6 and more than 2.3 degrees on average for the torsion angles{phi} and{psi} in the CASPs, respectively. Using the predicted torsion angles, we infer the tertiary structures of four representative template-free modeling targets that achieve high precision with regard to the root-mean-square deviation and TM-score by comparing them to the native structures. The results demonstrate that the ESIDEN can make accurate predictions of the torsion angles by leveraging the evolutionary signatures. The proposed evolutionary signatures would be also used as alternative features in predicting residue-residue distance, protein structure, and protein-ligand binding sites. Moreover, the high-precision torsion angles predicted by the ESIDEN can be used to accurately infer protein tertiary structures, and the ESIDEN would potentially pave the way to improve protein structure prediction.

bioinformatics↗