RNA and protein-based nanodevices for mammalian post-transcriptional circuits

Synthetic biological neural networks: From current implementations to future perspectives

Artificial neural networks, inspired by the biological networks of the human brain, have become game-changing computing models in modern computer science. Inspired by their wide scope of applications, synthetic biology strives to create their biological counterparts, which we denote synthetic biological neural networks (SYNBIONNs). Their use in the fields of medicine, biosensors, biotechnology, and many more shows great potential and presents exciting possibilities. So far, many different synthetic biological networks have been successfully constructed, however, SYNBIONN implementations have been sparse. The latter are mostly based on neural networks pretrained in silico and being heavily dependent on extensive human input. In this paper, we review current implementations and models of SYNBIONNs. We briefly present the biological platforms that show potential for designing and constructing perceptrons and/or multilayer SYNBIONNs. We explore their future possibilities along with the challenges that must be overcome to successfully implement a scalable in vivo biological neural network capable of online learning.

Harnessing synthetic biology for advancing RNA therapeutics and vaccine design

Recent global events have drawn into focus the diversity of options for combatting disease across a spectrum of prophylactic and therapeutic approaches. The recent success of the mRNA-based COVID-19 vaccines has paved the way for RNA-based treatments to revolutionize the pharmaceutical industry. However, historical treatment options are continuously updated and reimagined in the context of novel technical developments, such as those facilitated through the application of synthetic biology. When it comes to the development of genetic forms of therapies and vaccines, synthetic biology offers diverse tools and approaches to influence the content, dosage, and breadth of treatment with the prospect of economic advantage provided in time and cost benefits. This can be achieved by utilizing the broad tools within this discipline to enhance the functionality and efficacy of pharmaceutical agent sequences. This review will describe how synthetic biology principles can augment RNA-based treatments through optimizing not only the vaccine antigen, therapeutic construct, therapeutic activity, and delivery vector. The enhancement of RNA vaccine technology through implementing synthetic biology has the potential to shape the next generation of vaccines and therapeutics.

Unleashing the potential of catalytic RNAs to combat mis-spliced transcripts

Human transcriptome can undergo RNA mis-splicing due to spliceopathies contributing to the increasing number of genetic diseases including muscular dystrophy (MD), Alzheimer disease (AD), Huntington disease (HD), myelodysplastic syndromes (MDS). Intron retention (IR) is a major inducer of spliceopathies where two or more introns remain in the final mature mRNA and account for many intronic expansion diseases. Potential removal of such introns for therapeutic purposes can be feasible when utilizing bioinformatics, catalytic RNAs, and nano-drug delivery systems. Overcoming delivery challenges of catalytic RNAs was discussed in this review as a future perspective highlighting the significance of utilizing synthetic biology in addition to high throughput deep sequencing and computational approaches for the treatment of mis-spliced transcripts.

Synthetic biology approaches for dynamic CHO cell engineering

Fed-batch culture of Chinese hamster ovary (CHO) cells remains the most commonly used method for producing biopharmaceuticals. Static CHO cell-line engineering approaches have incrementally improved productivity, growth and product quality through permanent knockout of genes with a negative impact on production, or constitutive overexpression of genes with a positive impact. However, during fed-batch culture, conditions (such as nutrient availability) are continually changing. Therefore, traits that are most beneficial during early-phase culture (such as high growth rate) may be less desirable in late phase. Unlike with static approaches, dynamic cell line engineering strategies can optimise such traits by implementing synthetic sense-and-respond programmes. Here, we review emerging synthetic biology tools that can be used to build dynamic, self-regulating CHO cells, capable of detecting intra-/extracellular cues and generating user-defined responses tailored to the stage-specific needs of the production process.

A Network Approach to Genetic Circuit Designs

As genetic circuits become more sophisticated, the size and complexity of data about their designs increase. The data captured goes beyond genetic sequences alone; information about circuit modularity and functional details improves comprehension, performance analysis, and design automation techniques. However, new data types expose new challenges around the accessibility, visualization, and usability of design data (and metadata). Here, we present a method to transform circuit designs into networks and showcase its potential to enhance the utility of design data. Since networks are dynamic structures, initial graphs can be interactively shaped into subnetworks of relevant information based on requirements such as the hierarchy of biological parts or interactions between entities. A significant advantage of a network approach is the ability to scale abstraction, providing an automatic sliding level of detail that further tailors the visualization to a given situation. Additionally, several visual changes can be applied, such as coloring or clustering nodes based on types (e.g., genes or promoters), resulting in easier comprehension from a user perspective. This approach allows circuit designs to be coupled to other networks, such as metabolic pathways or implementation protocols captured in graph-like formats. We advocate using networks to structure, access, and improve synthetic biology information.

Engineering synthetic RNA devices for cell control

The versatility of RNA in sensing and interacting with small molecules, proteins and other nucleic acids while encoding genetic instructions for protein translation makes it a powerful substrate for engineering biological systems. RNA devices integrate cellular information sensing, processing and actuation of specific signals into defined functions and have yielded programmable biological systems and novel therapeutics of increasing sophistication. However, challenges centred on expanding the range of analytes that can be sensed and adding new mechanisms of action have hindered the full realization of the field's promise. Here, we describe recent advances that address these limitations and point to a significant maturation of synthetic RNA-based devices.

Directed evolution of orthogonal RNA-RBP pairs through library-vs-library in vitro selection

RNA-binding proteins (RBPs) and their RNA ligands play many critical roles in gene regulation and RNA processing in cells. They are also useful for various applications in cell biology and synthetic biology. However, re-engineering novel and orthogonal RNA-RBP pairs from natural components remains challenging while such synthetic RNA-RBP pairs could significantly expand the RNA-RBP toolbox for various applications. Here, we report a novel library-vs-library in vitro selection strategy based on Phage Display coupled with Systematic Evolution of Ligands by EXponential enrichment (PD-SELEX). Starting with pools of 1.1 × 1012 unique RNA sequences and 4.0 × 108 unique phage-displayed L7Ae-scaffold (LS) proteins, we selected RNA-RBP complexes through a two-step affinity purification process. After six rounds of library-vs-library selection, the selected RNAs and LS proteins were analyzed by next-generation sequencing (NGS). Further deconvolution of the enriched RNA and LS protein sequences revealed two synthetic and orthogonal RNA-RBP pairs that exhibit picomolar affinity and >4000-fold selectivity.

Optimizing Calcium Detection Methods in Animal Systems: A Sandbox for Synthetic Biology

Since the 1970s, the emergence and expansion of novel methods for calcium ion (Ca2+) detection have found diverse applications in vitro and in vivo across a series of model animal systems. Matched with advances in fluorescence imaging techniques, the improvements in the functional range and stability of various calcium indicators have significantly enhanced more accurate study of intracellular Ca2+ dynamics and its effects on cell signaling, growth, differentiation, and regulation. Nonetheless, the current limitations broadly presented by organic calcium dyes, genetically encoded calcium indicators, and calcium-responsive nanoparticles suggest a potential path toward more rapid optimization by taking advantage of a synthetic biology approach. This engineering-oriented discipline applies principles of modularity and standardization to redesign and interrogate endogenous biological systems. This review will elucidate how novel synthetic biology technologies constructed for eukaryotic systems can offer a promising toolkit for interfacing with calcium signaling and overcoming barriers in order to accelerate the process of Ca2+ detection optimization.

Posttranscriptional modifications at the 37th position in the anticodon stem-loop of tRNA: structural insights from MD simulations

Transfer RNA (tRNA) is the most diversely modified RNA. Although the strictly conserved purine position 37 in the anticodon stem-loop undergoes modifications that are phylogenetically distributed, we do not yet fully understand the roles of these modifications. Therefore, molecular dynamics simulations are used to provide molecular-level details for how such modifications impact the structure and function of tRNA. A focus is placed on three hypermodified base families that include the parent i6A, t6A, and yW modifications, as well as derivatives. Our data reveal that the hypermodifications exhibit significant conformational flexibility in tRNA, which can be modulated by additional chemical functionalization. Although the overall structure of the tRNA anticodon stem remains intact regardless of the modification considered, the anticodon loop must rearrange to accommodate the bulky, dynamic hypermodifications, which includes changes in the nucleotide glycosidic and backbone conformations, and enhanced or completely new nucleobase-nucleobase interactions compared to unmodified tRNA or tRNA containing smaller (m1G) modifications at the 37th position. Importantly, the extent of the changes in the anticodon loop is influenced by the addition of small functional groups to parent modifications, implying each substituent can further fine-tune tRNA structure. Although the dominant conformation of the ASL is achieved in different ways for each modification, the molecular features of all modified tRNA drive the ASL domain to adopt the functional open-loop conformation. Importantly, the impact of the hypermodifications is preserved in different sequence contexts. These findings highlight the likely role of regulating mRNA structure and translation.

ALS, a cellular whodunit on motor neuron degeneration

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease that primarily targets motor neurons. Motor neurons from ALS patients show cytoplasmic inclusions that are reflective of an altered RNA metabolism and protein degradation. Causal gene mutations are found in all cell types even though patient motor neurons are by far the most susceptible to the degeneration. Using induced pluripotent stem cell (iPSC) technology, researchers have generated motor neurons with the same genotype as the patient including sporadic ones. They have also generated other cell types associated with the disease such as astrocytes, microglia and oligodendrocytes. These cells provide not only new insights on the mechanisms of the disease from the early stage, but also a platform for drug screening that has led to several clinical trials. This review examines the knowledge gained from iPSC studies using patient cells on the gene mutations and cellular networks in ALS and relevant experimental therapies.