Biology subjects

Hsu, T.-C.

Publications and source records attributed to Hsu, T.-C..

4 recordsLinked to original sources

bioRxiv2026

The missing part of the DMSP cycle in coral holobionts: Endozoicomonas exports acetate derived from DMSP degradation

Endozoicomonas, a dominant symbiotic bacterium in coral holobionts, is noted for its ability to degrade dimethylsulfoniopropionate (DMSP) so as to generate acetate. While acetate is a well-known short-chain fatty acid in metabolic cross-feeding relationships, it remains unclear whether acetate derived from bacterial DMSP degradation is available to corals and their other symbionts. In this study, we employed Endozoicomonas ruthgatesiae strain 8E (herein referred as 8E) as a model to examine availability of DMSP-derived acetate for other symbionts. Using gas chromatography-mass spectrometry (GC-MS), we observed a significant increase in acetate excretion in 8E upon exposure to DMSP. Stable isotope labeling further confirmed that this elevated acetate efflux originated directly from DMSP, suggesting a complete cycle of DMSP-derived carbon among coral symbionts. Transcriptomic analysis revealed that DMSP exposure upregulated dddD expression and triggered a systemic reconfiguration of metabolism, characterized by down-regulation of the TCA cycle and the Pta-AckA pathway, with carbon flux redirected to the glyoxylate shunt. These findings suggest that upon exposure to DMSP, metabolism of 8E shifts from biomass production to DMSP catabolism, resulting in acetate efflux. Notably, we found that elevated temperature diminishes DMSP cleavage activity of 8E, indicating thermal sensitivity of this bacterial metabolic activity. ImportanceEndozoicomonas is known for its dominance in coral holobionts and its ability to degrade DMSP, an important compound in the marine sulfur cycle. Acetate is one resulting product in microbial DMSP metabolism and a common cross-feeding molecule. Whether DMSP-derived acetate in coral-associated DMSP-degrading bacteria is employed for cross-feeding stands a critical step in making a complete carbon cycle of DMSP metabolism within coral holobionts. In this study, we employed GC-MS and RNA-sequencing techniques to offer the first evidence of acetate excretion in Endozoicomonas while metabolizing DMSP, as well as its underlying genetic mechanism. Furthermore, we demonstrate reduced genetic response and DMSP-degrading capability under an elevated temperature in Endozoicomonas ruthgatesiae strain 8E, the model bacterium employed in this study. These findings provide the missing puzzle of DMSP metabolism in coral holobionts and suggest a potential role of DMSP in modulating symbiotic interactions within coral holobionts.

microbiology↗

bioRxiv2024

StomaVision: stomatal trait analysis through deep learning

StomaVision is an automated tool designed for high-throughput detection and measurement of stomatal traits, such as stomatal number, pore size, and closure rate. It provides insights into plant responses to environmental cues, streamlining the analysis of micrographs from field-grown plants across various species, including monocots and dicots. Enhanced by a novel collection method that utilizes video recording, StomaVision increases the number of captured images for robust statistical analysis. Accessible via an intuitive web interface at and available for local use in a containerized environment at , this tool ensures long-term usability by minimizing the impact of software updates and maintaining functionality with minimal setup requirements. The application of StomaVision has provided significant physiological insights, such as variations in stomatal density, opening rates, and total pore area under heat stress. These traits correlate with critical physiological processes, including gas exchange, carbon assimilation, and water use efficiency, demonstrating the tools utility in advancing our understanding of plant physiology. The ability of StomaVision to identify differences in responses to varying durations of heat treatment highlights its value in plant science research. Plain language summaryStomaVision is a tool that automatically counts and measures tiny openings on plant leaves, helping us learn how plants deal with their surroundings. It is easy to use and works well with various plant species. This tool helps scientists see how plants change under stress, making plant research easier and more accurate.

plant biology↗

bioRxiv2023

Hematopoietic Transfer of the Anti-Cancer and Lifespan-Extending Capabilities of A Genetically Engineered Blood System

A causal relationship exists among the aging process, organ decay and dis-function, and the occurrence of various diseases including cancer. A genetically engineered mouse model, termed Klf1K74R/K74R or Klf1(K74R), carrying mutation on the well-conserved sumoylation site of the hematopoietic transcription factor KLF1/ EKLF has been generated that possesses extended lifespan and healthy characteristics including cancer resistance. We show that the healthy longevity characteristics of the Klf1(K74R) mice, as exemplified by their higher anti- cancer capability, are likely gender-, age- and genetic background-independent. Significantly, the anti-cancer capability, in particular that against melanoma as well as hepatocellular carcinoma, and lifespan-extending property of Klf1(K74R) mice could be transferred to wild-type mice via transplantation of their bone marrow mononuclear cells at young age of the latter. Furthermore, NK(K74R) cells carry higher in vitro cancer cell-killing ability than wild type NK cells. Targeted/global gene expression profiling analysis has identified changes of the expression of specific proteins, including the immune checkpoint factors PDCD and CD274, and cellular pathways in the leukocytes of the Klf1(K74R) that are in the directions of anti-cancer and/or anti-aging. This study demonstrates the feasibility of developing a transferable hematopoietic/ blood system for long-term anti-cancer and, potentially, for anti-aging.

cancer biology↗

bioRxiv2019

Predicting the prognosis of non-small cell lung cancer by integrating microarray and clinical data with deep learning

AO_SCPLOWBSTRACTC_SCPLOWNon-small cell lung cancer (NSCLC) is one of the most common lung cancers worldwide. Accurate prognostic stratification of NSCLC can become an important clinical reference when designing therapeutic strategies for cancer patients. With this clinical application in mind, we developed a deep neural network (DNN) combining heterogeneous data sources of gene expression and clinical data to accurately predict the prognosis of NSCLC patients. Based on microarray data from a cohort set (614 patients), seven well-known NSCLC markers were used to group patients into marker- and marker+ subgroups. Using a systems biology approach, prognosis relevance values (PRV) were then calculated to select eight additional novel prognostic gene markers. Gene markers along with clinical data were then used to develop an integrative DNN via bimodal learning to predict the 5-year survival rate of NSCLC patients with tremendously high accuracy (AUC: 0.8163, accuracy: 75.44%), which is superior to all other existing methods based on AUC. Using the capability of deep learning, we believe that our predicted cancer prognosis can be a promising index helping oncologists and physicians develop personalized therapy and build the foundation of precision medicine in the future.

bioinformatics↗