Structural Mapping of Protein Aggregates in Live Cells Modeling Huntington's Disease.

While protein aggregation is a hallmark of many neurodegenerative diseases, acquiring structural information on protein aggregates inside live cells remains challenging. Traditional microscopy does not provide structural information on protein systems. Routinely used fluorescent protein tags, such as Green Fluorescent Protein (GFP), might perturb native structures. Here, we report a counter-propagating mid-infrared photothermal imaging approach enabling mapping of secondary structure of protein aggregates in live cells modeling Huntington's disease. By comparing mid-infrared photothermal spectra of label-free and GFP-tagged huntingtin inclusions, we demonstrate that GFP fusions indeed perturb the secondary structure of aggregates. By implementing spectra with small spatial step for dissecting spectral features within sub-micrometer distances, we reveal that huntingtin inclusions partition into a ß-sheet-rich core and a ɑ-helix-rich shell. We further demonstrate that this structural partition exists only in cells with the [RNQ+] prion state, while [rnq-] cells only carry smaller ß-rich non-toxic aggregates. Collectively, our methodology has the potential to unveil detailed structural information on protein assemblies in live cells, enabling high-throughput structural screenings of macromolecular assemblies.

Brainwide silencing of prion protein by AAV-mediated delivery of an engineered compact epigenetic editor.

Prion disease is caused by misfolding of the prion protein (PrP) into pathogenic self-propagating conformations, leading to rapid-onset dementia and death. However, elimination of endogenous PrP halts prion disease progression. In this study, we describe Coupled Histone tail for Autoinhibition Release of Methyltransferase (CHARM), a compact, enzyme-free epigenetic editor capable of silencing transcription through programmable DNA methylation. Using a histone H3 tail-Dnmt3l fusion, CHARM recruits and activates endogenous DNA methyltransferases, thereby reducing transgene size and cytotoxicity. When delivered to the mouse brain by systemic injection of adeno-associated virus (AAV), Prnp-targeted CHARM ablates PrP expression across the brain. Furthermore, we have temporally limited editor expression by implementing a kinetically tuned self-silencing approach. CHARM potentially represents a broadly applicable strategy to suppress pathogenic proteins, including those implicated in other neurodegenerative diseases.

Outcomes of a novel algorithm for levator muscle plication surgery in congenital blepharoptosis.

AIM: to assess the outcomes of a novel algorithm for the calculation of the amount levator muscle plication in congenital blepharoptosis surgery. METHODS: this retrospective comparative study included 34 patients with congenital ptosis subjected to levator muscle plication surgery during the period from October 2021 to November 2022. They were divided into two groups. Group A: the amount of levator muscle plication was calculated by a traditional formula [(amount of ptosis x 3) + 9 mm in cases with good levator function or (amount of ptosis x 3) + 11 mm in cases with fair levator function]. Group B: the amount of levator muscle plication was calculated by a novel nomogram [the result of the traditional formula was modified by subtracting 4 mm if the calculated amount was ≥ 15 mm or subtracting 3 mm if the calculated amount was < 15 mm]. Demographic data, baseline ptosis characteristics and postoperative results at 1st week, 1st month, 3rd month and 6th month were compared between the groups. Primary outcome measure was postoperative Marginal Reflex Distance (MRD1). Secondary outcome measures were lid contour, lid crease and any reported complications. RESULTS: Group A included 20 eyes of 18 patients while Group B included 20 eyes of 16 patients. The mean amount of levator muscle plication was 16.98 ± 2.44 mm and 13.48 ± 2.42 mm in group A and group B respectively. The difference between the two groups was highly statistically significant (p < 0.001). Mean MRD1 at the 1st postoperative week was 4.95 ± 0.37 mm in group A and 4.08 ± 0.64 mm in group B. This difference was highly statistically significant (P < 0.001). Overcorrection was seen in 8 (40%) eyes in group A and 1 (5%) eye in group B. The difference was statistically significant between the two groups (p = 0.008). Undercorrection was seen in only 1 (5%) eye in group B. No other complications were reported. Surgical success was achieved in 12 (60%) eyes in group A versus 18 (90%) eyes in group B. The difference between the two groups was statistically significant (p = 0.03). CONCLUSION: our novel nomogram for the calculation of the amount levator muscle plication in congenital blepharoptosis surgery is effective in achieving a satisfactory postoperative MRD1.

Anti-CRISPR Anopheles mosquitoes inhibit gene drive spread under challenging behavioural conditions in large cages.

CRISPR-based gene drives have the potential to spread within populations and are considered as promising vector control tools. A doublesex-targeting gene drive was able to suppress laboratory Anopheles mosquito populations in small and large cages, and it is considered for field application. Challenges related to the field-use of gene drives and the evolving regulatory framework suggest that systems able to modulate or revert the action of gene drives, could be part of post-release risk-mitigation plans. In this study, we challenge an AcrIIA4-based anti-drive to inhibit gene drive spread in age-structured Anopheles gambiae population under complex feeding and behavioural conditions. A stochastic model predicts the experimentally-observed genotype dynamics in age-structured populations in medium-sized cages and highlights the necessity of large-sized cage trials. These experiments and experimental-modelling framework demonstrate the effectiveness of the anti-drive in different scenarios, providing further corroboration for its use in controlling the spread of gene drive in Anopheles.

Mapping the dynamics of epigenetic adaptation during heterochromatin misregulation.

A classical and well-established mechanism that enables cells to adapt to new and adverse conditions is the acquisition of beneficial genetic mutations. Much less is known about epigenetic mechanisms that allow cells to develop novel and adaptive phenotypes without altering their genetic blueprint. It has been recently proposed that histone modifications, such as heterochromatin-defining H3K9 methylation (H3K9me), normally reserved to maintain genome integrity, can be redistributed across the genome to establish new and potentially adaptive phenotypes. To uncover the dynamics of this process, we developed a precision engineered genetic approach to trigger H3K9me redistribution on-demand in fission yeast. This enabled us to trace genome-scale RNA and chromatin changes over time prior to and during adaptation in long-term continuous cultures. Establishing adaptive H3K9me occurs over remarkably slow time-scales relative to the initiating stress. During this time, we captured dynamic H3K9me redistribution events ultimately leading to cells converging on an optimal adaptive solution. Upon removal of stress, cells relax to new transcriptional and chromatin states rather than revert to their initial (ground) state, establishing a tunable memory for a future adaptive epigenetic response. Collectively, our tools uncover the slow kinetics of epigenetic adaptation that allow cells to search for and heritably encode adaptive solutions, with implications for drug resistance and response to infection.

A Multiplex MoClo Toolkit for Extensive and Flexible Engineering of Saccharomyces cerevisiae.

Synthetic biology toolkits are one of the core foundations on which the field has been built, facilitating and accelerating efforts to reprogram cells and organisms for diverse biotechnological applications. The yeast Saccharomyces cerevisiae, an important model and industrial organism, has benefited from a wide range of toolkits. In particular, the MoClo Yeast Toolkit (YTK) enables the fast and straightforward construction of multigene plasmids from a library of highly characterized parts for programming new cellular behavior in a more predictable manner. While YTK has cultivated a strong parts ecosystem and excels in plasmid construction, it is limited in the extent and flexibility with which it can create new strains of yeast. Here, we describe a new and improved toolkit, the Multiplex Yeast Toolkit (MYT), that extends the capabilities of YTK and addresses strain engineering limitations. MYT provides a set of new integration vectors and selectable markers usable across common laboratory strains, as well as additional assembly cassettes to increase the number of transcriptional units in multigene constructs, CRISPR-Cas9 tools for highly efficient multiplexed vector integration, and three orthogonal and inducible promoter systems for conditional programming of gene expression. With these tools, we provide yeast synthetic biologists with a powerful platform to take their engineering ambitions to exciting new levels.

Cooperative assembly confers regulatory specificity and long-term genetic circuit stability.

A ubiquitous feature of eukaryotic transcriptional regulation is cooperative self-assembly between transcription factors (TFs) and DNA cis-regulatory motifs. It is thought that this strategy enables specific regulatory connections to be formed in gene networks between otherwise weakly interacting, low-specificity molecular components. Here, using synthetic gene circuits constructed in yeast, we find that high regulatory specificity can emerge from cooperative, multivalent interactions among artificial zinc-finger-based TFs. We show that circuits "wired" using the strategy of cooperative TF assembly are effectively insulated from aberrant misregulation of the host cell genome. As we demonstrate in experiments and mathematical models, this mechanism is sufficient to rescue circuit-driven fitness defects, resulting in genetic and functional stability of circuits in long-term continuous culture. Our naturally inspired approach offers a simple, generalizable means for building high-fidelity, evolutionarily robust gene circuits that can be scaled to a wide range of host organisms and applications.

Deep Neural Networks for Predicting Single-Cell Responses and Probability Landscapes.

Engineering biology relies on the accurate prediction of cell responses. However, making these predictions is challenging for a variety of reasons, including the stochasticity of biochemical reactions, variability between cells, and incomplete information about underlying biological processes. Machine learning methods, which can model diverse input-output relationships without requiring a priori mechanistic knowledge, are an ideal tool for this task. For example, such approaches can be used to predict gene expression dynamics given time-series data of past expression history. To explore this application, we computationally simulated single-cell responses, incorporating different sources of noise and alternative genetic circuit designs. We showed that deep neural networks trained on these simulated data were able to correctly infer the underlying dynamics of a cell response even in the presence of measurement noise and stochasticity in the biochemical reactions. The training set size and the amount of past data provided as inputs both affected prediction quality, with cascaded genetic circuits that introduce delays requiring more past data. We also tested prediction performance on a bistable auto-activation circuit, finding that our initial method for predicting a single trajectory was fundamentally ill-suited for multimodal dynamics. To address this, we updated the network architecture to predict the entire distribution of future states, showing it could accurately predict bimodal expression distributions. Overall, these methods can be readily applied to the diverse prediction tasks necessary to predict and control a variety of biological circuits, a key aspect of many synthetic biology applications.

Long-term evolution of proliferating cells using the eVOLVER platform.

Experimental evolution using fast-growing unicellular organisms is a unique strategy for deciphering the principles and mechanisms underlying evolutionary processes as well as the architecture and wiring of basic biological functions. Over the past decade, this approach has benefited from the development of powerful systems for the continuous control of the growth of independently evolving cultures. While the first devices compatible with multiplexed experimental evolution remained challenging to implement and required constant user intervention, the recently developed eVOLVER framework represents a fully automated closed-loop system for laboratory evolution assays. However, it remained difficult to maintain and compare parallel evolving cultures in tightly controlled environments over long periods of time using eVOLVER. Furthermore, a number of tools were lacking to cope with the various issues that inevitably occur when conducting such long-term assays. Here we present a significant upgrade of the eVOLVER framework, providing major modifications of the experimental methodology, hardware and software as well as a new stand-alone protocol. Altogether, these adaptations and improvements make the eVOLVER a versatile and unparalleled set-up for long-term experimental evolution.

Human herpesvirus 8 ORF57 protein is able to reduce TDP-43 pathology: network analysis identifies interacting pathways.

Aggregation of TAR DNA-binding protein 43 kDa (TDP-43) is thought to drive the pathophysiology of amyotrophic lateral sclerosis and some frontotemporal dementias. TDP-43 is normally a nuclear protein that in neurons translocates to the cytoplasm and can form insoluble aggregates upon activation of the integrated stress response (ISR). Viruses evolved to control the ISR. In the case of Herpesvirus 8, the protein ORF57 acts to bind protein kinase R, inhibit phosphorylation of eIF2α and reduce activation of the ISR. We hypothesized that ORF57 might also possess the ability to inhibit aggregation of TDP-43. ORF57 was expressed in the neuronal SH-SY5Y line and its effects on TDP-43 aggregation characterized. We report that ORF57 inhibits TDP-43 aggregation by 55% and elicits a 2.45-fold increase in the rate of dispersion of existing TDP-43 granules. These changes were associated with a 50% decrease in cell death. Proteomic studies were carried out to identify the protein interaction network of ORF57. We observed that ORF57 directly binds to TDP-43 as well as interacts with many components of the ISR, including elements of the proteostasis machinery known to reduce TDP-43 aggregation. We propose that viral proteins designed to inhibit a chronic ISR can be engineered to remove aggregated proteins and dampen a chronic ISR.

Effects of antibiotic interaction on antimicrobial resistance development in wastewater.

While wastewater is understood to be a critically important reservoir of antimicrobial resistance due to the presence of multiple antibiotic residues from industrial and agricultural runoff, there is little known about the effects of antibiotic interactions in the wastewater on the development of resistance. We worked to fill this gap in quantitative understanding of antibiotic interaction in constant flow environments by experimentally monitoring E. coli populations under subinhibitory concentrations of combinations of antibiotics with synergistic, antagonistic, and additive interactions. We then used these results to expand our previously developed computational model to account for the effects of antibiotic interaction. We found that populations grown under synergistic and antagonistic antibiotic conditions exhibited significant differences from predicted behavior. E. coli populations grown with synergistically interacting antibiotics developed less resistance than predicted, indicating that synergistic antibiotics may have a suppressive effect on resistance development. Furthermore E. coli populations grown with antagonistically interacting antibiotics showed an antibiotic ratio-dependent development of resistance, suggesting that not only antibiotic interaction, but relative concentration is important in predicting resistance development. These results provide critical insight for quantitatively understanding the effects of antibiotic interactions in wastewater and provide a basis for future studies in modelling resistance in these environments.

Long-term evolution of proliferating cells using the eVOLVER platform.

Experimental evolution using fast-growing unicellular organisms is a unique strategy for deciphering the principles and mechanisms underlying evolutionary processes as well as the architecture and wiring of basic biological functions. Over the past decade, this approach has benefited from the development of powerful systems for the continuous control of the growth of independently evolving cultures. While the first devices compatible with multiplexed experimental evolution remained challenging to implement and required constant user intervention, the recently-developed eVOLVER framework represents a fully automated closed-loop system for laboratory evolution assays. However, it remained difficult to maintain and compare parallel evolving cultures in tightly controlled environments over long periods of time using eVOLVER. Furthermore, a number of tools were lacking to cope with the various issues that inevitably occur when conducting such long-term assays. Here we present a significant upgrade of the eVOLVER framework, providing major modifications of the experimental methodology, hardware and software as well as a new standalone protocol. Altogether, these adaptations and improvements make the eVOLVER a versatile and unparalleled setup for long-term experimental evolution.

Transcriptional kinetic synergy: A complex landscape revealed by integrating modeling and synthetic biology.

Transcription factors (TFs) control gene expression, often acting synergistically. Classical thermodynamic models offer a biophysical explanation for synergy based on binding cooperativity and regulated recruitment of RNA polymerase. Because transcription requires polymerase to transition through multiple states, recent work suggests that "kinetic synergy" can arise through TFs acting on distinct steps of the transcription cycle. These types of synergy are not mutually exclusive and are difficult to disentangle conceptually and experimentally. Here, we model and build a synthetic circuit in which TFs bind to a single shared site on DNA, such that TFs cannot synergize by simultaneous binding. We model mRNA production as a function of both TF binding and regulation of the transcription cycle, revealing a complex landscape dependent on TF concentration, DNA binding affinity, and regulatory activity. We use synthetic TFs to confirm that the transcription cycle must be integrated with recruitment for a quantitative understanding of gene regulation.

Effects of Antibiotic Interaction on Antimicrobial Resistance Development in Wastewater.

While wastewater is understood to be a critically important reservoir of antimicrobial resistance due to the presence of multiple antibiotic residues from industrial and agricultural runoff, there is little known about the effects of antibiotic interactions in the wastewater on the development of resistance. We worked to fill this gap in quantitative understanding of antibiotic interaction in constant flow environments by experimentally monitoring E. coli populations under subinhibitory concentrations of combinations of antibiotics with synergistic, antagonistic, and additive interactions. We then used these results to expand our previously developed computational model to account for the complex effects of antibiotic interaction. We found that while E. coli populations grown in additively interacting antibiotic combinations grew predictably according to the previously developed model, those populations grown under synergistic and antagonistic antibiotic conditions exhibited significant differences from predicted behavior. E. coli populations grown in the condition with synergistically interacting antibiotics developed less resistance than predicted, indicating that synergistic antibiotics may have a suppressive effect on antimicrobial resistance development. Furthermore E. coli populations grown in the condition with antagonistically interacting antibiotics showed an antibiotic ratio-dependent development of resistance, suggesting that not only antibiotic interaction, but relative concentration is important in predicting resistance development. These results provide critical insight for quantitatively understanding the effects of antibiotic interactions in wastewater and provide a basis for future studies in modelling resistance in these environments. Importance: Antimicrobial resistance (AMR) is a growing global threat to public health expected to impact 10 million people by 2050, driving mortality rates globally and with a disproportionate effect on low- and middle-income countries. Communities in proximity to wastewater settings and environmentally contaminated surroundings are at particular risk due to resistance stemming from antibiotic residues from industrial and agricultural runoff. Currently, there is a limited quantitative and mechanistic understanding of the evolution of AMR in response to multiple interacting antibiotic residues in constant flow environments. Using an integrated computational and experimental methods, we find that interactions between antibiotic residues significantly affect the development of resistant bacterial populations.

Unlocking the magic in mycelium: Using synthetic biology to optimize filamentous fungi for biomanufacturing and sustainability.

Filamentous fungi drive carbon and nutrient cycling across our global ecosystems, through its interactions with growing and decaying flora and their constituent microbiomes. The remarkable metabolic diversity, secretion ability, and fiber-like mycelial structure that have evolved in filamentous fungi have been increasingly exploited in commercial operations. The industrial potential of mycelial fermentation ranges from the discovery and bioproduction of enzymes and bioactive compounds, the decarbonization of food and material production, to environmental remediation and enhanced agricultural production. Despite its fundamental impact in ecology and biotechnology, molds and mushrooms have not, to-date, significantly intersected with synthetic biology in ways comparable to other industrial cell factories (e.g. Escherichia coli,Saccharomyces cerevisiae, and Komagataella phaffii). In this review, we summarize a suite of synthetic biology and computational tools for the mining, engineering and optimization of filamentous fungi as a bioproduction chassis. A combination of methods across genetic engineering, mutagenesis, experimental evolution, and computational modeling can be used to address strain development bottlenecks in established and emerging industries. These include slow mycelium growth rate, low production yields, non-optimal growth in alternative feedstocks, and difficulties in downstream purification. In the scope of biomanufacturing, we then detail previous efforts in improving key bottlenecks by targeting protein processing and secretion pathways, hyphae morphogenesis, and transcriptional control. Bringing synthetic biology practices into the hidden world of molds and mushrooms will serve to expand the limited panel of host organisms that allow for commercially-feasible and environmentally-sustainable bioproduction of enzymes, chemicals, therapeutics, foods, and materials of the future.

High-throughput continuous evolution of compact Cas9 variants targeting single-nucleotide-pyrimidine PAMs.

Despite the availability of Cas9 variants with varied protospacer-adjacent motif (PAM) compatibilities, some genomic loci-especially those with pyrimidine-rich PAM sequences-remain inaccessible by high-activity Cas9 proteins. Moreover, broadening PAM sequence compatibility through engineering can increase off-target activity. With directed evolution, we generated four Cas9 variants that together enable targeting of most pyrimidine-rich PAM sequences in the human genome. Using phage-assisted noncontinuous evolution and eVOLVER-supported phage-assisted continuous evolution, we evolved Nme2Cas9, a compact Cas9 variant, into variants that recognize single-nucleotide pyrimidine-PAM sequences. We developed a general selection strategy that requires functional editing with fully specified target protospacers and PAMs. We applied this selection to evolve high-activity variants eNme2-T.1, eNme2-T.2, eNme2-C and eNme2-C.NR. Variants eNme2-T.1 and eNme2-T.2 offer access to N4TN PAM sequences with comparable editing efficiencies as existing variants, while eNme2-C and eNme2-C.NR offer less restrictive PAM requirements, comparable or higher activity in a variety of human cell types and lower off-target activity at N4CN PAM sequences.

Multidimensional control of therapeutic human cell function with synthetic gene circuits.

Synthetic gene circuits that precisely control human cell function could expand the capabilities of gene- and cell-based therapies. However, platforms for developing circuits in primary human cells that drive robust functional changes in vivo and have compositions suitable for clinical use are lacking. Here, we developed synthetic zinc finger transcription regulators (synZiFTRs), which are compact and based largely on human-derived proteins. As a proof of principle, we engineered gene switches and circuits that allow precise, user-defined control over therapeutically relevant genes in primary T cells using orthogonal, US Food and Drug Administration-approved small-molecule inducers. Our circuits can instruct T cells to sequentially activate multiple cellular programs such as proliferation and antitumor activity to drive synergistic therapeutic responses. This platform should accelerate the development and clinical translation of synthetic gene circuits in diverse human cell types and contexts.

The sound of silence: Transgene silencing in mammalian cell engineering.

To elucidate principles operating in native biological systems and to develop novel biotechnologies, synthetic biology aims to build and integrate synthetic gene circuits within native transcriptional networks. The utility of synthetic gene circuits for cell engineering relies on the ability to control the expression of all constituent transgene components. Transgene silencing, defined as the loss of expression over time, persists as an obstacle for engineering primary cells and stem cells with transgenic cargos. In this review, we highlight the challenge that transgene silencing poses to the robust engineering of mammalian cells, outline potential molecular mechanisms of silencing, and present approaches for preventing transgene silencing. We conclude with a perspective identifying future research directions for improving the performance of synthetic gene circuits.

Recent progress of gene circuit designs in immune cell therapies.

The success of chimeric antigen receptor (CAR) T cell therapy against hematological cancers has convincingly demonstrated the potential of using genetically engineered cells as therapeutic agents. Although much progress has been achieved in cell therapy, more beneficial capabilities have yet to be fully explored. One of the unique advantages afforded by cell therapies is the possibility to implement genetic control circuits, which enables diverse signal sensing and logical processing for optimal response in the complex tumor microenvironment. In this perspective, we will first outline design considerations for cell therapy control circuits that address clinical demands. We will compare and contrast key design features in some of the latest control circuits developments and conclude by discussing potential future directions.

A Toolkit for Precise, Multigene Control in Saccharomyces cerevisiae.

Systems that allow researchers to precisely control the expression of genes are fundamental to biological research, biotechnology, and synthetic biology. However, few inducible gene expression systems exist that can enable simultaneous multigene control under common nutritionally favorable conditions in the important model organism and chassis Saccharomyces cerevisiae. Here we repurposed ligand binding domains from mammalian type I nuclear receptors to establish a family of up to five orthogonal synthetic gene expression systems in yeast. Our systems enable tight, independent, multigene control through addition of inert hormones and are capable of driving robust and rapid gene expression outputs, in some cases achieving up to 600-fold induction. As a proof of principle, we placed expression of four enzymes from the violacein biosynthetic pathway under independent expression control to selectively route pathway flux by addition of specific inducer combinations. Our results establish a modular, versatile, and potentially expandable toolkit for multidimensional control of gene expression in yeast that can be used to construct and control naturally occurring and synthetic gene networks.