Developing, characterizing and modeling CRISPR-based point-of-use pathogen diagnostics.

Recent years have seen intense interest in the development of point-of-care nucleic acid diagnostic technologies to address the scaling limitations of laboratory-based approaches. Chief among these are combinations of isothermal amplification approaches with CRISPR-based detection and readouts of target products. Here, we contribute to the growing body of rapid, programmable point-of-care pathogen tests by developing and optimizing a one-pot NASBA-Cas13a nucleic acid detection assay. This test uses the isothermal amplification technique NASBA to amplify target viral nucleic acids, followed by Cas13a-based detection of amplified sequences. We first demonstrate an in-house formulation of NASBA that enables optimization of individual NASBA components. We then present design rules for NASBA primer sets and LbuCas13a guide RNAs for fast and sensitive detection of SARS-CoV-2 viral RNA fragments, resulting in 20 - 200 aM sensitivity without any specialized equipment. Finally, we explore the combination of high-throughput assay condition screening with mechanistic ordinary differential equation modeling of the reaction scheme to gain a deeper understanding of the NASBA-Cas13a system. This work presents a framework for developing a mechanistic understanding of reaction performance and optimization that uses both experiments and modeling, which we anticipate will be useful in developing future nucleic acid detection technologies.

Long term rescue of Alzheimer's deficits in vivo by one-time gene-editing of App C-terminus.

Gene-editing technologies promise to create a new class of therapeutics that can achieve permanent correction with a single intervention. Besides eliminating mutant alleles in familial disease, gene-editing can also be used to favorably manipulate upstream pathophysiologic events and alter disease-course in wider patient populations, but few such feasible therapeutic avenues have been reported. Here we use CRISPR-Cas9 to edit the last exon of amyloid precursor protein (App), relevant for Alzheimer's disease (AD). Our strategy effectively eliminates an endocytic (YENPTY) motif at APP C-terminus, while preserving the N-terminus and compensatory APP-homologues. This manipulation favorably alters events along the amyloid-pathway - inhibiting toxic APP-ß-cleavage fragments (including Aß) and upregulating neuroprotective APP-α-cleavage products. AAV-driven editing ameliorates neuropathologic, electrophysiologic, and behavioral deficits in an AD knockin mouse model. Effects persist for many months, and no abnormalities are seen in WT mice even after germline App-editing; underlining overall efficacy and safety. Pathologic alterations in the glial-transcriptome of App-KI mice, as seen by single nuclei RNA-sequencing (sNuc-Seq), are also normalized by App C-terminus editing. Our strategy takes advantage of innate transcriptional rules that render terminal exons insensitive to nonsense-decay, and the upstream manipulation is expected to be effective for all forms of AD. These studies offer a path for a one-time disease-modifying treatment for AD.

Model design choices impact biological insight: Unpacking the broad landscape of spatial-temporal model development decisions.

Computational models enable scientists to understand observed dynamics, uncover rules underlying behaviors, predict experimental outcomes, and generate new hypotheses. There are countless modeling approaches that can be used to characterize biological systems, further multiplied when accounting for the variety of model design choices. Many studies focus on the impact of model parameters on model output and performance; fewer studies investigate the impact of model design choices on biological insight. Here we demonstrate why model design choices should be deliberate and intentional in context of the specific research system and question. In this study, we analyze agnostic and broadly applicable modeling choices at three levels-system, cell, and environment-within the same agent-based modeling framework to interrogate their impact on temporal, spatial, and single-cell emergent dynamics. We identify key considerations when making these modeling choices, including the (i) differences between qualitative vs. quantitative results driven by choices in system representation, (ii) impact of cell-to-cell variability choices on cell-level and temporal trends, and (iii) relationship between emergent outcomes and choices of nutrient dynamics in the environment. This generalizable investigation can help guide the choices made when developing biological models that aim to characterize spatial-temporal dynamics.

Incorporating temporal information during feature engineering bolsters emulation of spatio-temporal emergence.

MOTIVATION: Emergent biological dynamics derive from the evolution of lower-level spatial and temporal processes. A long-standing challenge for scientists and engineers is identifying simple low-level rules that give rise to complex higher-level dynamics. High-resolution biological data acquisition enables this identification and has evolved at a rapid pace for both experimental and computational approaches. Simultaneously harnessing the resolution and managing the expense of emerging technologies-e.g. live cell imaging, scRNAseq, agent-based models-requires a deeper understanding of how spatial and temporal axes impact biological systems. Effective emulation is a promising solution to manage the expense of increasingly complex high-resolution computational models. In this research, we focus on the emulation of a tumor microenvironment agent-based model to examine the relationship between spatial and temporal environment features, and emergent tumor properties. RESULTS: Despite significant feature engineering, we find limited predictive capacity of tumor properties from initial system representations. However, incorporating temporal information derived from intermediate simulation states dramatically improves the predictive performance of machine learning models. We train a deep-learning emulator on intermediate simulation states and observe promising enhancements over emulators trained solely on initial conditions. Our results underscore the importance of incorporating temporal information in the evaluation of spatio-temporal emergent behavior. Nevertheless, the emulators exhibit inconsistent performance, suggesting that the underlying model characterizes unique cell populations dynamics that are not easily replaced. AVAILABILITY AND IMPLEMENTATION: All source codes for the agent-based model, emulation, and analyses are publicly available at the corresponding DOIs: 10.5281/zenodo.10622155, 10.5281/zenodo.10611675, 10.5281/zenodo.10621244, respectively.

Rationally Designed DNA-Based Scaffolds and Switching Probes for Protein Sensing.

The detection of a protein analyte and use of this type of information for disease diagnosis and physiological monitoring requires methods with high sensitivity and specificity that have to be also easy to use, rapid and, ideally, single step. In the last 10 years, a number of DNA-based sensing methods and sensors have been developed in order to achieve quantitative readout of protein biomarkers. Inspired by the speed, specificity, and versatility of naturally occurring chemosensors based on structure-switching biomolecules, significant efforts have been done to reproduce these mechanisms into the fabrication of artificial biosensors for protein detection. As an alternative, in scaffold DNA biosensors, different recognition elements (e.g., peptides, proteins, small molecules, and antibodies) can be conjugated to the DNA scaffold with high accuracy and precision in order to specifically interact with the target protein with high affinity and specificity. They have several advantages and potential, especially because the transduction signal can be drastically enhanced. Our aim here is to provide an overview of the best examples of structure switching-based and scaffold DNA sensors, as well as to introduce the reader to the rational design of innovative sensing mechanisms and strategies based on programmable functional DNA systems for protein detection.

PAM-Engineered Toehold Switches as Input-Responsive Activators of CRISPR-Cas12a for Sensing Applications.

The RNA-programmed CRISPR effector protein Cas12a has emerged as a powerful tool for gene editing and molecular diagnostics. However, additional bio-engineering strategies are required to achieve control over Cas12a activity. Here, we show that Toehold Switch DNA hairpins, presenting a rationally designed locked protospacer adjacent motif (PAM) in the loop, can be used to control Cas12a in response to molecular inputs. Reconfiguring the Toehold Switch DNA from a hairpin to a duplex conformation through a strand displacement reaction provides an effective means to modulate the accessibility of the PAM, thereby controlling the binding and cleavage activities of Cas12a. Through this approach, we showcase the potential to trigger downstream Cas12a activity by leveraging proximity-based strand displacement reactions in response to target binding. By utilizing the trans-cleavage activity of Cas12a as a signal transduction method, we demonstrate the versatility of our approach for sensing applications. Our system enables rapid, one-pot detection of IgG antibodies and small molecules with high sensitivity and specificity even within complex matrices. Besides the bioanalytical applications, the switchable PAM-engineered Toehold Switches serve as programmable tools capable of regulating Cas12a-based targeting and DNA processing in response to molecular inputs and hold promise for a wide array of biotechnological applications.

Energy metabolism modulates the regulatory impact of activators on gene expression.

Gene expression is a regulated process fueled by ATP consumption. Therefore, regulation must be coupled to constraints imposed by the level of energy metabolism. Here, we explore this relationship both theoretically and experimentally. A stylized mathematical model predicts that activators of gene expression have variable impact depending on metabolic rate. Activators become less essential when metabolic rate is reduced and more essential when metabolic rate is enhanced. We find that, in the Drosophila eye, expression dynamics of the yan gene are less affected by loss of EGFR-mediated activation when metabolism is reduced, and the opposite effect is seen when metabolism is enhanced. The effects are also seen at the level of pattern regularity in the adult eye, where loss of EGFR-mediated activation is mitigated by lower metabolism. We propose that gene activation is tuned by energy metabolism to allow for faithful expression dynamics in the face of variable metabolic conditions.

Energy metabolism modulates the regulatory impact of activators on gene expression.

Gene expression is a regulated process fueled by ATP consumption. Therefore, regulation must be coupled to constraints imposed by the level of energy metabolism. Here, we explore this relationship both theoretically and experimentally. A stylized mathematical model predicts that activators of gene expression have variable impact depending on metabolic rate. Activators become less essential when metabolic rate is reduced and more essential when metabolic rate is enhanced. We find that in the Drosophila eye, expression dynamics of the yan gene are less affected by loss of EGFR-mediated activation when metabolism is reduced, and the opposite effect is seen when metabolism is enhanced. The effects are also seen at the level of pattern regularity in the adult eye, where loss of EGFR-mediated activation is mitigated by lower metabolism. We propose that gene activation is tuned by energy metabolism to allow for faithful expression dynamics in the face of variable metabolic conditions.

Minimalist Design of Wireframe DNA Nanotubes: Tunable Geometry, Size, Chirality, and Dynamics.

DNA nanotubes (NTs) have attracted extensive interest as artificial cytoskeletons for biomedical, synthetic biology, and materials applications. Here, we report the modular design and assembly of a minimalist yet robust DNA wireframe nanotube with tunable cross-sectional geometry, cavity size, chirality, and length, while using only four DNA strands. We introduce an h-motif structure incorporating double-crossover (DX) tile-like DNA edges to achieve structural rigidity and provide efficient self-assembly of h-motif-based DNA nanotube (H-NT) units, thus producing programmable, micrometer-long nanotubes. We demonstrate control of the H-NT nanotube length via short DNA modulators. Finally, we use an enzyme, RNase H, to take these structures out of equilibrium and trigger nanotube assembly at a physiologically relevant temperature, underlining future cellular applications. The minimalist H-NTs can assemble at near-physiological salt conditions and will serve as an easily synthesized, DNA-economical modular template for biosensors, plasmonics, or other functional materials and as cost-efficient drug-delivery vehicles for biomedical applications.

Ratiometric sensing of Pnt and Yan transcription factor levels confers ultrasensitivity to photoreceptor fate transitions in Drosophila.

Cell state transitions are often triggered by large changes in the concentrations of transcription factors and therefore large differences in their stoichiometric ratios. Whether cells can elicit transitions using modest changes in the ratios of co-expressed factors is unclear. Here, we investigate how cells in the Drosophila eye resolve state transitions by quantifying the expression dynamics of the ETS transcription factors Pnt and Yan. Eye progenitor cells maintain a relatively constant ratio of Pnt/Yan protein, despite expressing both proteins with pulsatile dynamics. A rapid and sustained twofold increase in the Pnt/Yan ratio accompanies transitions to photoreceptor fates. Genetic perturbations that modestly disrupt the Pnt/Yan ratio produce fate transition defects consistent with the hypothesis that transitions are normally driven by a twofold shift in the ratio. A biophysical model based on cooperative Yan-DNA binding coupled with non-cooperative Pnt-DNA binding illustrates how twofold ratio changes could generate ultrasensitive changes in target gene transcription to drive fate transitions. Thus, coupling cell state transitions to the Pnt/Yan ratio sensitizes the system to modest fold-changes, conferring robustness and ultrasensitivity to the developmental program.

A smart paper-based electrochemical sensor for reliable detection of iron ions in serum.

The fast-growing healthcare demand for user-friendly and affordable analytical tools is driving the efforts to develop reliable platforms for the customization of therapy based on individual health conditions. In this overall scenario, we developed a paper-based electrochemical sensor for the quantification of iron ions in serum as a cost-effective sensing tool for the correct supplement administration. In detail, the working electrode of the screen-printed device has been modified with a nanocomposite constituted of carbon black and gold nanoparticles with a drop-casting procedure. Square wave voltammetry has been adopted as an electrochemical technique. This sensor was further modified with Nafion for iron quantification in serum after sample treatment with trifluoroacetic acid. Under optimized conditions, iron ions have been detected with a LOD down to 0.05 mg/L and a linearity up to 10 mg/L in standard solution. The obtained results have been compared with reference methods namely commercial colorimetric assay and atomic absorption spectroscopy, obtaining a good correlation within the experimental errors. These results demonstrated the suitability of the developed paper-based sensor for future applications in precision medicine of iron-deficiency diseases.

The in silico lab: Improving academic code using lessons from biology.

"Good code" is often regarded as a nebulous, impractical ideal. Common best practices toward improving code quality can be inaccessible to those without a rigorous computer science or software engineering background, contributing to a gap between advancing scientific research and FAIR practices. We seek to equip researchers with the necessary background and context to tackle the challenge of improving code quality in computational biology research using analogies from biology to synthesize why certain best practices are critical for advancing computational research. Improving code quality requires active stewardship; we encourage researchers to deliberately adopt and share practices that ensure reusability, repeatability, and reproducibility.

Mapping CAR T-Cell Design Space Using Agent-Based Models.

Chimeric antigen receptor (CAR) T-cell therapy shows promise for treating liquid cancers and increasingly for solid tumors as well. While potential design strategies exist to address translational challenges, including the lack of unique tumor antigens and the presence of an immunosuppressive tumor microenvironment, testing all possible design choices in vitro and in vivo is prohibitively expensive, time consuming, and laborious. To address this gap, we extended the modeling framework ARCADE (Agent-based Representation of Cells And Dynamic Environments) to include CAR T-cell agents (CAR T-cell ARCADE, or CARCADE). We conducted in silico experiments to investigate how clinically relevant design choices and inherent tumor features-CAR T-cell dose, CD4+:CD8+ CAR T-cell ratio, CAR-antigen affinity, cancer and healthy cell antigen expression-individually and collectively impact treatment outcomes. Our analysis revealed that tuning CAR affinity modulates IL-2 production by balancing CAR T-cell proliferation and effector function. It also identified a novel multi-feature tuned treatment strategy for balancing selectivity and efficacy and provided insights into how spatial effects can impact relative treatment performance in different contexts. CARCADE facilitates deeper biological understanding of treatment design and could ultimately enable identification of promising treatment strategies to accelerate solid tumor CAR T-cell design-build-test cycles.

Enhancement of CRISPR/Cas12a trans-cleavage activity using hairpin DNA reporters.

The RNA programmed non-specific (trans) nuclease activity of CRISPR-Cas Type V and VI systems has opened a new era in the field of nucleic acid-based detection. Here, we report on the enhancement of trans-cleavage activity of Cas12a enzymes using hairpin DNA sequences as FRET-based reporters. We discover faster rate of trans-cleavage activity of Cas12a due to its improved affinity (Km) for hairpin DNA structures, and provide mechanistic insights of our findings through Molecular Dynamics simulations. Using hairpin DNA probes we significantly enhance FRET-based signal transduction compared to the widely used linear single stranded DNA reporters. Our signal transduction enables faster detection of clinically relevant double stranded DNA targets with improved sensitivity and specificity either in the presence or in the absence of an upstream pre-amplification step.

Fighting fire with fire: deploying complexity in computational modeling to effectively characterize complex biological systems.

Computational modeling empowers systems biologists to interrogate and understand increasingly complex biological phenomena, and the growing suite of computational approach presents both opportunities and challenges. Choosing the right computational approaches to address a given question requires managing a model's complexity, balancing goals and limitations including interpretability, data resolution, and computational cost. Excess model complexity can diminish the utility for building understanding, while excess simplicity can render the model insufficient for addressing the questions of interest. Using systems immunology as a case study, we review how different model design strategies uniquely manage complexity, ending with a consideration of composite models, which combine the benefits of individual paradigms but present additional challenges arising from added layers of complexity. We anticipate that considering general model design challenges and potential solutions through the lens of complexity will foster enhanced collaboration among computational and experimental researchers.

Effect of Preoperative Administration of Oral Melatonin on Pneumatic Tourniquet-Induced Ischemia-Reperfusion Injury in Orthopedic Surgery of Lower Extremities: A Randomized Clinical Trial.

Background: Ischemic reperfusion injury (IRI) causes cellular damage and dysfunction. The present study aimed to evaluate the effect of melatonin on pneumatic tourniquet-induced IRI in orthopedic surgery of the lower extremities. Methods: A randomized clinical trial was conducted at Chamran Hospital, Shiraz University of Medical Sciences (Shiraz, Iran), from September to November 2019. The target population was patients scheduled for elective orthopedic surgery of the lower extremities. A total of 67 patients were randomly divided into two groups, placebo (n=34) and melatonin (n=33). The groups received 10 mg melatonin or placebo the night before surgery and two hours before surgery. Primary outcome variables were the serum levels of superoxide dismutase (SOD) and malondialdehyde (MDA). Hemodynamic parameters, sedation score, and drug side effects were also evaluated. Data were analyzed using SPSS version 21.0 software. P<0.05 was considered statistically significant. Results: In the analysis phase, due to loss to follow-up (n=26), 41 patients divided into two groups of melatonin (n=20) and placebo (n=21) were evaluated. There was no significant difference in demographic data, duration of surgery (P=0.929), and tourniquet time (P=0.496) between the groups. The serum levels of SOD and MDA were not significantly different between the groups (P=0.866 and P=0.422, respectively), nor were the incidence of postoperative nausea (P=0.588) and patients' satisfaction (P=0.088). However, the postoperative sedation score and vomiting between the groups were significantly different (P<0.001). Conclusion: Administration of 10 mg melatonin provided effective sedation, but had no significant effect on the serum levels of SOD and MDA, nor on pneumatic tourniquet-induced IRI in orthopedic surgery of the lower limbs. Trial registration number: IRCT20141009019470N87.

The new era of quantitative cell imaging-challenges and opportunities.

Quantitative optical microscopy-an emerging, transformative approach to single-cell biology-has seen dramatic methodological advancements over the past few years. However, its impact has been hampered by challenges in the areas of data generation, management, and analysis. Here we outline these technical and cultural challenges and provide our perspective on the trajectory of this field, ushering in a new era of quantitative, data-driven microscopy. We also contrast it to the three decades of enormous advances in the field of genomics that have significantly enhanced the reproducibility and wider adoption of a plethora of genomic approaches.

GAMES: A Dynamic Model Development Workflow for Rigorous Characterization of Synthetic Genetic Systems.

Mathematical modeling is invaluable for advancing understanding and design of synthetic biological systems. However, the model development process is complicated and often unintuitive, requiring iteration on various computational tasks and comparisons with experimental data. Ad hoc model development can pose a barrier to reproduction and critical analysis of the development process itself, reducing the potential impact and inhibiting further model development and collaboration. To help practitioners manage these challenges, we introduce the Generation and Analysis of Models for Exploring Synthetic Systems (GAMES) workflow, which includes both automated and human-in-the-loop processes. We systematically consider the process of developing dynamic models, including model formulation, parameter estimation, parameter identifiability, experimental design, model reduction, model refinement, and model selection. We demonstrate the workflow with a case study on a chemically responsive transcription factor. The generalizable workflow presented in this tutorial can enable biologists to more readily build and analyze models for various applications.

Morphological features of single cells enable accurate automated classification of cancer from non-cancer cell lines.

Accurate cancer detection and diagnosis is of utmost importance for reliable drug-response prediction. Successful cancer characterization relies on both genetic analysis and histological scans from tumor biopsies. It is known that the cytoskeleton is significantly altered in cancer, as cellular structure dynamically remodels to promote proliferation, migration, and metastasis. We exploited these structural differences with supervised feature extraction methods to introduce an algorithm that could distinguish cancer from non-cancer cells presented in high-resolution, single cell images. In this paper, we successfully identified the features with the most discriminatory power to successfully predict cell type with as few as 100 cells per cell line. This trait overcomes a key barrier of machine learning methodologies: insufficient data. Furthermore, normalizing cell shape via microcontact printing on self-assembled monolayers enabled better discrimination of cell lines with difficult-to-distinguish phenotypes. Classification accuracy remained robust as we tested dissimilar cell lines across various tissue origins, which supports the generalizability of our algorithm.