Biology subjects

ZHU, H.

Publications and source records attributed to ZHU, H..

3 recordsLinked to original sources

bioRxiv2023

Free long chain fatty acid solitarily primes early postembryonic development in Caenorhabditis elegans under starvation

Postembryonic development of animals is long considered an internal predetermined program, while macronutrient is essential only because they provide biomatters and energy to support this process. However, in this study, by using a nematode Caenorhabditis elegans model, we surprisingly found that dietary supplementation of palmitic acid alone, but not other essential nutrients of abundance such as glucose or amino acid mixture, sufficiently initiated the early postembryonic development under complete macronutrient deprivation. Such a development was indicated by changes in morphology, cellular markers in multiple tissues, behaviors and the global transcription pattern. Mechanistically, palmitate doesnt function as a biomatter/energy provider, but as a ligand to activate the nuclear hormone receptor NHR-49/80 and generate an obscure peroxisome-derived secretive hormone in the intestine. Such a hormonal signal was received by chemosensory neurons in the head in regulating the insulin-like neuropeptide secretion and its downstream nuclear receptor to orchestrate the global development. Moreover, the nutrient-sensing hub mTORC1 played a negative role in this process. In conclusion, our data indicate that free fatty acid acts as a prime nutrient signal to launch the early development in C. elegans; and implicate that specific nutrient rather than the internal genetic program is the first impetus of postembryonic development.

developmental biology↗

bioRxiv2022

Multi-dimensional protein solubility optimization with an ultra-high-throughput microfluidic platform

Protein-based biologics are highly suitable for drug development, as they exhibit low toxicity and high specificity for their targets. However, for therapeutic applications, biologics must often be formulated to very high concentrations, making insufficient solubility a critical bottleneck in drug development pipelines. Here, we report an ultra-high-throughput microfluidic platform for protein solubility screening. In comparison with previous methods, this microfluidic platform can make, incubate, and measure samples in a few minutes, uses just 20 micrograms of protein (> 10-fold improvement) and yields 10,000 data points (1000-fold improvement). This allows quantitative comparison of formulation additives, such as salt, polysorbate, histidine, arginine and sucrose. Additionally, we can measure how solubility is affected by different concentrations of multiple additives, find a suitable pH for the formulation, and measure the impact of single mutations on solubility, thus enabling the screening of large libraries. By reducing material and time costs, this approach makes detailed multi-dimensional solubility optimization experiments possible, streamlining drug development and increasing our understanding of biotherapeutic solubility and the effects of excipients.

biophysics↗

bioRxiv2022

CausalCell: applying causal discovery to single-cell analyses

Correlation between objects does not answer many scientific questions because of the lack of causal but the excess of spurious information and is prone to happen by coincidence. Causal discovery infers causal relationships from data upon conditional independence test between objects without prior assumptions (e.g., variables have linear relationships and data follow the Gaussian distribution). Causal interactions within and between cells provide valuable information for investigating gene regulation, identifying diagnostic and therapeutic targets, and designing experimental and clinical studies. The rapid increase of single-cell data permits inferring causal interactions in many cell types. However, because no algorithms have been designed for handling abundant variables and few algorithms have been evaluated using real data, how to apply causal discovery to single-cell data remains a challenge. We report a pipeline and web server (http://www.gaemons.net/causalcell/causalDiscovery/) for accurately and conveniently performing causal discovery. The pipeline has been developed upon the benchmarking of 18 algorithms and the analyses of multiple datasets. Our applications indicate that only complicated algorithms can generate satisfactorily reliable results. Critical issues are discussed, and tips for best practices are provided.

bioinformatics↗