Biology subjects

Hsu, R. H.

Publications and source records attributed to Hsu, R. H..

5 recordsLinked to original sources

bioRxiv2026

RNA trans-splicing treatment for Duchenne muscular dystrophy

Duchenne muscular dystrophy (DMD) is a fatal disorder caused by dystrophin mutations, leading to progressive muscle degeneration and cardiac failure. Although AAV-based DMD therapies with innovative designs to overcome the cargo limits of the virus are effective in animal models, these current systems may face clinical translation challenges related to efficiency, off-target effects, and immunogenicity. We developed a multi-vector RNA End Joining (REJ) system to split large genes into multiple co-delivered AAVs that engage the cell's intrinsic spliceosome to precisely reassemble RNA segments that encode very large scar-free proteins. We show the system is efficient and has negligible off-target interactions. In vivo, REJ vectors expressing the adenine base editor Abe8e, mini-dystrophin Dp253, or native full-length dystrophin Dp427 each prevented muscle degeneration. Machine-learning histopathology of more than 120,000 myofibers revealed robust reductions in nuclear infiltration, improved centronucleation, and normalized hypertrophy, supported by functional, transcriptomic, and behavioral analyses. These findings establish REJ as a clinically viable AAV-based strategy for DMD that enables expression of large therapeutic proteins while avoiding potentially antigenic bacterial protein- or DNA-recombinases.

molecular biology↗

bioRxiv2026

An intersectional expression platform for gene complementation using RNA-fragment end joining (REJ)

Gene complementation is a powerful tool for genetic selection and protein functional studies but typically requires extensive screening for complementary components. Here we engineered a platform for gene complementation based on RNA end-joining (REJ) that precisely and efficiently splices separate RNA units into functional coding mRNAs. REJ is mediated by short structured modular RNA segments of ~250bp that: (a) promote RNA::RNA interaction, (b) coopt the intrinsic cell splicing machinery to facilitate RNA trans-splicing, (c) minimizes translation of protein fragments from un-spliced RNA segments, and (d) encodes scar-free protein. We demonstrate that REJ is a broadly applicable system that can reliably split nearly any gene into complementary segments regardless of protein structure. To enable the use of this system we provide a toolbox of reporters and a web-based design tool to facilitate vector design. Demonstrated REJ applications include genetic complementation, intersectional labeling of cell types, and efficient expression of large proteins.

molecular biology↗

bioRxiv2019

Rapid microbial interaction network inference in microfluidic droplets

Microbial interactions are major drivers of microbial community dynamics and functions. However, microbial interactions are challenging to decipher due to limitations in parallel culturing of sub-communities across many environments and accurate absolute abundance quantification of constituent members of the consortium. To this end, we developed Microbial Interaction Network Inference in microdroplets (MINI-Drop), a high-throughput method to rapidly infer microbial interactions in microbial consortia in microfluidic droplets. Fluorescence microscopy coupled to automated computational droplet and cell detection was used to rapidly determine the absolute abundance of each strain in hundreds to thousands of droplets per experiment. We show that MINI-Drop can accurately infer pairwise as well as higher-order interactions using a microbial interaction toolbox of defined microbial interactions mediated by distinct molecular mechanisms. MINI-Drop was used to investigate how the molecular composition of the environment alters the interaction network of a three-member consortium. To provide insight into the variation in community states across droplets, we developed a probabilistic model of cell growth modified by microbial interactions. In sum, we demonstrate a robust and generalizable method to probe cellular interaction networks by random encapsulation of sub-communities into microfluidic droplets.

systems biology↗

bioRxiv2017

Deciphering microbial interactions in synthetic human gut microbiome communities

The human gut microbiota comprises a dynamic ecological system that contributes significantly to human health and disease. The ecological forces that govern community assembly and stability in the gut microbiota remain unresolved. We developed a generalizable model-guided framework to predict higher-order consortia from time-resolved measurements of lower-order assemblages. This method was employed to decipher microbial interactions in a diverse 12-member human gut microbiome synthetic community. We show that microbial growth parameters and pairwise interactions are the major drivers of multi-species community dynamics, as opposed to context-dependent (conditional) interactions. The inferred microbial interaction network as well as a top-down approach to community assembly pinpointed both ecological driver and responsive species that were significantly modulated by microbial inter-relationships. Our model demonstrated that negative pairwise interactions could generate history-dependent responses of initial species proportions on physiological timescales that frequently does not originate from bistability. The model elucidated a topology for robust coexistence in pairwise assemblages consisting of a negative feedback loop that balances disparities in monospecies fitness levels. Bayesian statistical methods were used to evaluate the constraint of model parameters by the experimental data. Measurements of extracellular metabolites illuminated the metabolic capabilities of monospecies and potential molecular basis for competitive and cooperative interactions in the community. However, these data failed to predict influential organisms shaping community assembly. In sum, these methods defined the ecological roles of key species shaping community assembly and illuminated network design principles of microbial communities.

systems biology↗

bioRxiv2017

An interventional Soylent diet increases the Bacteroidetes to Firmicutes ratio in human gut microbiome communities: a randomized controlled trial

Our knowledge of the relationship between the gut microbiome and health has rapidly expanded in recent years. Diet has been shown to have causative effects on microbiome composition, which can have subsequent implications on health. Soylent 2.0 is a liquid meal replacement drink that satisfies nearly 20% of all recommended daily intakes per serving. This study aims to characterize the changes in gut microbiota composition resulting from a short-term Soylent diet. Fourteen participants were separated into two groups: 5 in the regular diet group and 9 in the Soylent diet group. The regular diet group maintained a diet closely resembling self-reported regular diets. The Soylent diet group underwent three dietary phases: A) a regular diet for 2 days, B) a Soylent-only diet (five servings of Soylent daily and water as needed) for 4 days, and C) a regular diet for 4 days. Daily logs self-reporting diet, Bristol stool ratings, and any abdominal discomfort were electronically submitted. Eight fecal samples per participant were collected using fecal sampling kits, which were subsequently sent to uBiome, Inc. for sample processing and V4 16S rDNA sequencing. Reads were clustered into operational taxonomic units (OTUs) and taxonomically identified against the GreenGenes 16S database. We find that an individuals alpha-diversity is not significantly altered during a Soylent-only diet. In addition, principal coordinate analysis using the unweighted UniFrac distance metric shows samples cluster strongly by individual and not by dietary phase. Among Soylent dieters, we find a significant increase in the ratio of Bacteroidetes to Firmicutes abundance, which is associated with several positive health outcomes, including reduced risks of obesity and intestinal inflammation.

clinical trials↗