Evaluation of various membranes at different fluxes to enable large-volume single-use perfusion bioreactors.

The growing demand for biological therapeutics has increased interest in large-volume perfusion bioreactors, but the operation and scalability of perfusion membranes remain a challenge. This study evaluates perfusion cell culture performance and monoclonal antibody (mAb) productivity at various membrane fluxes (1.5-5 LMH), utilizing polyvinylidene difluoride (PVDF), polyethersulfone (PES), or polysulfone (PS) membranes in tangential flow filtration mode. At low flux, culture with PVDF membrane maintained higher cell culture growth, permeate titer (1.06-1.34 g/L) and sieving coefficients (≥83%) but showed lower permeate volumetric throughput and higher transmembrane pressure (TMP) (>1.50 psi) in the later part of the run compared to cultures with PES and PS membrane. However, as permeate flux increased, the total mass of product decreased by around 30% for cultures with PVDF membrane, while it remained consistent with PES and PS membrane, and at the highest flux studied, PES membrane generated 12% more product than PVDF membrane. This highlights that membrane selection for large-volume perfusion bioreactors depends on the productivity and permeate flux required. Since operating large-volume perfusion bioreactors at low flux would require several cell retention devices and a complex setup, PVDF membranes are suitable for low-volume operations at low fluxes whereas PES membranes can be a desirable alternative for large-volume higher demand products at higher fluxes.

Engineering bacterial warriors: harnessing microbes to modulate animal physiology.

A central goal of synthetic biology is the reprogramming of living systems for predetermined biological functions. While many engineering efforts have been made in living systems, these innovations have been mainly employed with microorganisms or cell lines. The engineering of multicellular organisms including animals remains challenging owing to the complexity of these systems. In this context, microbes, with their intricate impact on animals, have opened new opportunities. Through the utilization of the symbiotic relationships between microbes and animals, researchers have effectively manipulated animals in various ways using engineered microbes. This focused approach has demonstrated its significance in scientific exploration and engineering with model animals, coral preservation and restoration, and advancements in human health.

Bacterial vitamin B6 is required for post-embryonic development in C. elegans.

Nutritional intake influences animal growth, reproductive capacity, and survival of animals. Under nutrition deficiency, animal developmental arrest occurs as an adaptive strategy to survive. However, the nutritional basis and the underlying nutrient sensing mechanism essential for animal regrowth after developmental arrest remain to be explored. In Caenorhabditis elegans, larvae undergo early developmental arrest are stress resistant, and they require certain nutrients to recover postembryonic development. Here, we investigated the developmental arrest in C. elegans feeding on Lactiplantibacillus plantarum, and the rescue of the diapause state with trace supplementation of Escherichia coli. We performed a genome-wide screen using 3983 individual gene deletion E. coli mutants and identified E. coli genes that are indispensable for C. elegans larval growth on originally not nutritionally sufficient bacteria L. plantarum. Among these crucial genes, we confirmed E. coli pdxH, and the downstream metabolite pyridoxal 5-P (PLP, Vitamin B6) as important nutritional factors for C. elegans postembryonic development. Transcriptome results suggest that bacterial pdxH affects host development by coordinating host metabolic processes and PLP binding. Additionally, the developmental arrest induced by the L. plantarum diet in worm does not depend on the activation of FoxO/DAF-16. Altogether, these results highlight the role of microbial metabolite PLP as a crucial cofactor to restore postembryonic development in C. elegans.

Nucleic Transformer: Classifying DNA Sequences with Self-Attention and Convolutions.

Much work has been done to apply machine learning and deep learning to genomics tasks, but these applications usually require extensive domain knowledge, and the resulting models provide very limited interpretability. Here, we present the Nucleic Transformer, a conceptually simple but effective and interpretable model architecture that excels in the classification of DNA sequences. The Nucleic Transformer employs self-attention and convolutions on nucleic acid sequences, leveraging two prominent deep learning strategies commonly used in computer vision and natural language analysis. We demonstrate that the Nucleic Transformer can be trained without much domain knowledge to achieve high performance in Escherichia coli promoter classification, viral genome identification, enhancer classification, and chromatin profile predictions.

Microbiota-derived metabolites in regulating the development and physiology of Caenorhabditis elegans.

Microbiota consist of microorganisms that provide essential health benefits and contribute to the animal's physiological homeostasis. Microbiota-derived metabolites are crucial mediators in regulating host development, system homeostasis, and overall fitness. In this review, by focusing on the animal model Caenorhabditis elegans, we summarize key microbial metabolites and their molecular mechanisms that affect animal development. We also provide, from a bacterial perspective, an overview of host-microbiota interaction networks used for maintaining host physiological homeostasis. Moreover, we discuss applicable methodologies for profiling new bacterial metabolites that modulate host developmental signaling pathways. Microbiota-derived metabolites have the potential to be diagnostic biomarkers for diseases, as well as promising targets for engineering therapeutic interventions against animal developmental or health-related defects.

RNAdegformer: accurate prediction of mRNA degradation at nucleotide resolution with deep learning.

Messenger RNA-based therapeutics have shown tremendous potential, as demonstrated by the rapid development of messenger RNA based vaccines for COVID-19. Nevertheless, distribution of mRNA vaccines worldwide has been hampered by mRNA's inherent thermal instability due to in-line hydrolysis, a chemical degradation reaction. Therefore, predicting and understanding RNA degradation is a crucial and urgent task. Here we present RNAdegformer, an effective and interpretable model architecture that excels in predicting RNA degradation. RNAdegformer processes RNA sequences with self-attention and convolutions, two deep learning techniques that have proved dominant in the fields of computer vision and natural language processing, while utilizing biophysical features of RNA. We demonstrate that RNAdegformer outperforms previous best methods at predicting degradation properties at nucleotide resolution for COVID-19 mRNA vaccines. RNAdegformer predictions also exhibit improved correlation with RNA in vitro half-life compared with previous best methods. Additionally, we showcase how direct visualization of self-attention maps assists informed decision-making. Further, our model reveals important features in determining mRNA degradation rates via leave-one-feature-out analysis.

Programming gene expression in multicellular organisms for physiology modulation through engineered bacteria.

A central goal of synthetic biology is to predictably and efficiently reprogram living systems to perform computations and carry out specific biological tasks. Although there have been many advances in the bio-computational design of living systems, these advances have mainly been applied to microorganisms or cell lines; programming animal physiology remains challenging for synthetic biology because of the system complexity. Here, we present a bacteria-animal symbiont system in which engineered bacteria recognize external signals and modulate animal gene expression, twitching phenotype, and fat metabolism through RNA interference toward gfp, sbp-1, and unc-22 gene in C. elegans. By using genetic circuits in bacteria to control these RNA expressions, we are able to program the physiology of the model animal Caenorhabditis elegans with logic gates. We anticipate that engineered bacteria can be used more extensively to program animal physiology for agricultural, therapeutic, and basic science applications.

A peek in the micro-sized world: a review of design principles, engineering tools, and applications of engineered microbial community.

Microbial communities drive diverse processes that impact nearly everything on this planet, from global biogeochemical cycles to human health. Harnessing the power of these microorganisms could provide solutions to many of the challenges that face society. However, naturally occurring microbial communities are not optimized for anthropogenic use. An emerging area of research is focusing on engineering synthetic microbial communities to carry out predefined functions. Microbial community engineers are applying design principles like top-down and bottom-up approaches to create synthetic microbial communities having a myriad of real-life applications in health care, disease prevention, and environmental remediation. Multiple genetic engineering tools and delivery approaches can be used to 'knock-in' new gene functions into microbial communities. A systematic study of the microbial interactions, community assembling principles, and engineering tools are necessary for us to understand the microbial community and to better utilize them. Continued analysis and effort are required to further the current and potential applications of synthetic microbial communities.