Repressive Gene Regulation Synchronizes Development with Cellular Metabolism.

Metabolic conditions affect the developmental tempo of animals. Developmental gene regulatory networks (GRNs) must therefore synchronize their dynamics with a variable timescale. We find that layered repression of genes couples GRN output with variable metabolism. When repressors of transcription or mRNA and protein stability are lost, fewer errors in Drosophila development occur when metabolism is lowered. We demonstrate the universality of this phenomenon by eliminating the entire microRNA family of repressors and find that development to maturity can be largely rescued when metabolism is reduced. Using a mathematical model that replicates GRN dynamics, we find that lowering metabolism suppresses the emergence of developmental errors by curtailing the influence of auxiliary repressors on GRN output. We experimentally show that gene expression dynamics are less affected by loss of repressors when metabolism is reduced. Thus, layered repression provides robustness through error suppression and may provide an evolutionary route to a shorter reproductive cycle.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Electroanalytical Sensor Based on Gold-Nanoparticle-Decorated Paper for Sensitive Detection of Copper Ions in Sweat and Serum.

The growth of (bio)sensors in analytical chemistry is mainly attributable to the development of affordable, effective, portable, and user-friendly analytical tools. In the field of sensors, paper-based devices are gaining a relevant position for their outstanding features including foldability, ease of use, and instrument-free microfluidics. Herein, a multifarious use of filter paper to detect copper ions in bodily fluids is reported by exploiting this eco-friendly material to (i) synthesize AuNPs without the use of reductants and/or external stimuli, (ii) print the electrodes, (iii) load the reagents for the assay, (iv) filter the gross impurities, and (v) preconcentrate the target analyte. Copper ions were detected down to 3 ppb with a linearity up to 400 ppb in standard solutions. The applicability in biological matrices, namely, sweat and serum, was demonstrated by recovery studies and by analyzing these biofluids with the paper-based platform and the reference method (atomic absorption spectroscopy), demonstrating satisfactory accuracy of the novel eco-designed analytical tool.

Fly-QMA: Automated analysis of mosaic imaginal discs in Drosophila.

Mosaic analysis provides a means to probe developmental processes in situ by generating loss-of-function mutants within otherwise wildtype tissues. Combining these techniques with quantitative microscopy enables researchers to rigorously compare RNA or protein expression across the resultant clones. However, visual inspection of mosaic tissues remains common in the literature because quantification demands considerable labor and computational expertise. Practitioners must segment cell membranes or cell nuclei from a tissue and annotate the clones before their data are suitable for analysis. Here, we introduce Fly-QMA, a computational framework that automates each of these tasks for confocal microscopy images of Drosophila imaginal discs. The framework includes an unsupervised annotation algorithm that incorporates spatial context to inform the genetic identity of each cell. We use a combination of real and synthetic validation data to survey the performance of the annotation algorithm across a broad range of conditions. By contributing our framework to the open-source software ecosystem, we aim to contribute to the current move toward automated quantitative analysis among developmental biologists.

Windowed Granger causal inference strategy improves discovery of gene regulatory networks.

Accurate inference of regulatory networks from experimental data facilitates the rapid characterization and understanding of biological systems. High-throughput technologies can provide a wealth of time-series data to better interrogate the complex regulatory dynamics inherent to organisms, but many network inference strategies do not effectively use temporal information. We address this limitation by introducing Sliding Window Inference for Network Generation (SWING), a generalized framework that incorporates multivariate Granger causality to infer network structure from time-series data. SWING moves beyond existing Granger methods by generating windowed models that simultaneously evaluate multiple upstream regulators at several potential time delays. We demonstrate that SWING elucidates network structure with greater accuracy in both in silico and experimentally validated in vitro systems. We estimate the apparent time delays present in each system and demonstrate that SWING infers time-delayed, gene-gene interactions that are distinct from baseline methods. By providing a temporal framework to infer the underlying directed network topology, SWING generates testable hypotheses for gene-gene influences.

Hybrid analysis of gene dynamics predicts context-specific expression and offers regulatory insights.

MOTIVATION: To understand the regulatory pathways underlying diseases, studies often investigate the differential gene expression between genetically or chemically differing cell populations. Differential expression analysis identifies global changes in transcription and enables the inference of functional roles of applied perturbations. This approach has transformed the discovery of genetic drivers of disease and possible therapies. However, differential expression analysis does not provide quantitative predictions of gene expression in untested conditions. We present a hybrid approach, termed Differential Expression in Python (DiffExPy), that uniquely combines discrete, differential expression analysis with in silico differential equation simulations to yield accurate, quantitative predictions of gene expression from time-series data. RESULTS: To demonstrate the distinct insight provided by DiffExpy, we applied it to published, in vitro, time-series RNA-seq data from several genetic PI3K/PTEN variants of MCF10a cells stimulated with epidermal growth factor. DiffExPy proposed ensembles of several minimal differential equation systems for each differentially expressed gene. These systems provide quantitative models of expression for several previously uncharacterized genes and uncover new regulation by the PI3K/PTEN pathways. We validated model predictions on expression data from conditions that were not used for model training. Our discrete, differential expression analysis also identified SUZ12 and FOXA1 as possible regulators of specific groups of genes that exhibit late changes in expression. Our work reveals how DiffExPy generates quantitatively predictive models with testable, biological hypotheses from time-series expression data. AVAILABILITY AND IMPLEMENTATION: DiffExPy is available on GitHub (https://github.com/bagherilab/diffexpy). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Network inference performance complexity: a consequence of topological, experimental and algorithmic determinants.

MOTIVATION: Network inference algorithms aim to uncover key regulatory interactions governing cellular decision-making, disease progression and therapeutic interventions. Having an accurate blueprint of this regulation is essential for understanding and controlling cell behavior. However, the utility and impact of these approaches are limited because the ways in which various factors shape inference outcomes remain largely unknown. RESULTS: We identify and systematically evaluate determinants of performance-including network properties, experimental design choices and data processing-by developing new metrics that quantify confidence across algorithms in comparable terms. We conducted a multifactorial analysis that demonstrates how stimulus target, regulatory kinetics, induction and resolution dynamics, and noise differentially impact widely used algorithms in significant and previously unrecognized ways. The results show how even if high-quality data are paired with high-performing algorithms, inferred models are sometimes susceptible to giving misleading conclusions. Lastly, we validate these findings and the utility of the confidence metrics using realistic in silico gene regulatory networks. This new characterization approach provides a way to more rigorously interpret how algorithms infer regulation from biological datasets. AVAILABILITY AND IMPLEMENTATION: Code is available at http://github.com/bagherilab/networkinference/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DUETT quantitatively identifies known and novel events in nascent RNA structural dynamics from chemical probing data.

MOTIVATION: RNA molecules can undergo complex structural dynamics, especially during transcription, which influence their biological functions. Recently developed high-throughput chemical probing experiments that study RNA cotranscriptional folding generate nucleotide-resolution 'reactivities' for each length of a growing nascent RNA that reflect structural dynamics. However, the manual annotation and qualitative interpretation of reactivity across these large datasets can be nuanced, laborious, and difficult for new practitioners. We developed a quantitative and systematic approach to automatically detect RNA folding events from these datasets to reduce human bias/error, standardize event discovery and generate hypotheses about RNA folding trajectories for further analysis and experimental validation. RESULTS: Detection of Unknown Events with Tunable Thresholds (DUETT) identifies RNA structural transitions in cotranscriptional RNA chemical probing datasets. DUETT employs a feedback control-inspired method and a linear regression approach and relies on interpretable and independently tunable parameter thresholds to match qualitative user expectations with quantitatively identified folding events. We validate the approach by identifying known RNA structural transitions within the cotranscriptional folding pathways of the Escherichia coli signal recognition particle RNA and the Bacillus cereus crcB fluoride riboswitch. We identify previously overlooked features of these datasets such as heightened reactivity patterns in the signal recognition particle RNA about 12 nt lengths before base-pair rearrangement. We then apply a sensitivity analysis to identify tradeoffs when choosing parameter thresholds. Finally, we show that DUETT is tunable across a wide range of contexts, enabling flexible application to study broad classes of RNA folding mechanisms. AVAILABILITY AND IMPLEMENTATION: https://github.com/BagheriLab/DUETT. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Multidimensional Assessment of the Host Response in Mechanically Ventilated Patients with Suspected Pneumonia.

Rationale: The identification of informative elements of the host response to infection may improve the diagnosis and management of bacterial pneumonia. Objectives: To determine whether the absence of alveolar neutrophilia can exclude bacterial pneumonia in critically ill patients with suspected infection and to test whether signatures of bacterial pneumonia can be identified in the alveolar macrophage transcriptome. Methods: We determined the test characteristics of alveolar neutrophilia for the diagnosis of bacterial pneumonia in three cohorts of mechanically ventilated patients. In one cohort, we also isolated macrophages from alveolar lavage fluid and used the transcriptome to identify signatures of bacterial pneumonia. Finally, we developed a humanized mouse model of Pseudomonas aeruginosa pneumonia to determine if pathogen-specific signatures can be identified in human alveolar macrophages. Measurements and Main Results: An alveolar neutrophil percentage less than 50% had a negative predictive value of greater than 90% for bacterial pneumonia in both the retrospective (n = 851) and validation cohorts (n = 76 and n = 79). A transcriptional signature of bacterial pneumonia was present in both resident and recruited macrophages. Gene signatures from both cell types identified patients with bacterial pneumonia with test characteristics similar to alveolar neutrophilia. Conclusions: The absence of alveolar neutrophilia has a high negative predictive value for bacterial pneumonia in critically ill patients with suspected infection. Macrophages can be isolated from alveolar lavage fluid obtained during routine care and used for RNA-Seq analysis. This novel approach may facilitate a longitudinal and multidimensional assessment of the host response to bacterial pneumonia.

Combining gold nanoparticle-based headspace single-drop microextraction and a paper-based colorimetric assay for selenium determination.

A novel method combining headspace single-drop microextraction with a paper-based colorimetric assay was developed. Headspace single-drop microextraction using a microdrop containing unmodified gold nanoparticles (AuNPs) as both the extractant and the colorimetric probe was used for the sensitive and selective determination of Se(IV). The method relies on the color change of the microdrop solution caused by the adsorption of in situ-generated hydrogen selenide on the surface of AuNPs. Following extraction, the microdrop was spotted onto cellulose paper, and scanometric-assisted digital image analysis was used for selenium quantification. The analytical variables affecting the method sensitivity, including the drop volume, the concentrations of KBH4, HCl, and AuNP solutions, and the extraction time, were studied. Under the optimal conditions, a linear correlation between the colorimetric signal and Se(IV) concentration in the range from 15-100 µg L-1 with a limit of quantification of 12 µg L-1 was achieved. The repeatability of the method was studied by the calculation of intraday and interday precision for the standard solutions at concentrations of 20 and 70 µg L-1. The batch-to-batch reproducibility of the AuNPs synthesized under the same conditions was also assessed. The relative standard deviations were less than 7%. The method provided satisfactory results for the determination of selenium in real samples.

Machine Learning on Signal-to-Noise Ratios Improves Peptide Array Design in SAMDI Mass Spectrometry.

Emerging peptide array technologies are able to profile molecular activities within cell lysates. However, the structural diversity of peptides leads to inherent differences in peptide signal-to-noise ratios (S/N). These complex effects can lead to potentially unrepresentative signal intensities and can bias subsequent analyses. Within mass spectrometry-based peptide technologies, the relation between a peptide's amino acid sequence and S/N remains largely nonquantitative. To address this challenge, we present a method to quantify and analyze mass spectrometry S/N of two peptide arrays, and we use this analysis to portray quality of data and to design future arrays for SAMDI mass spectrometry. Our study demonstrates that S/N varies significantly across peptides within peptide arrays, and variation in S/N is attributable to differences of single amino acids. We apply supervised machine learning to predict peptide S/N based on amino acid sequence, and identify specific physical properties of the amino acids that govern variation of this metric. We find low peptide-S/N concordance between arrays, demonstrating that different arrays require individual characterization and that global peptide-S/N relationships are difficult to identify. However, with proper peptide sampling, this study illustrates how machine learning can accurately predict the S/N of a peptide in an array, allowing for the efficient design of arrays through selection of high S/N peptides.

Polyfunctional responses by human T cells result from sequential release of cytokines.

The release of cytokines by T cells defines a significant part of their functional activity in vivo, and their ability to produce multiple cytokines has been associated with beneficial immune responses. To date, time-integrated end-point measurements have obscured whether these polyfunctional states arise from the simultaneous or successive release of cytokines. Here, we used serial, time-dependent, single-cell analysis of primary human T cells to resolve the temporal dynamics of cytokine secretion from individual cells after activation ex vivo. We show that multifunctional, Th1-skewed cytokine responses (IFN-γ, IL-2, TNFα) are initiated asynchronously, but the ensuing dynamic trajectories of these responses evolve programmatically in a sequential manner. That is, cells predominantly release one of these cytokines at a time rather than maintain active secretion of multiple cytokines simultaneously. Furthermore, these dynamic trajectories are strongly associated with the various states of cell differentiation suggesting that transient programmatic activities of many individual T cells contribute to sustained, population-level responses. The trajectories of responses by single cells may also provide unique, time-dependent signatures for immune monitoring that are less compromised by the timing and duration of integrated measures.