The Unreasonable Effectiveness of Reaction Diffusion in Vertebrate Skin Color Patterning.

In 1952, Alan Turing published the reaction-diffusion (RD) mathematical framework, laying the foundations of morphogenesis as a self-organized process emerging from physicochemical first principles. Regrettably, this approach has been widely doubted in the field of developmental biology. First, we summarize Turing's line of thoughts to alleviate the misconception that RD is an artificial mathematical construct. Second, we discuss why phenomenological RD models are particularly effective for understanding skin color patterning at the meso/macroscopic scales, without the need to parameterize the profusion of variables at lower scales. More specifically, we discuss how RD models (a) recapitulate the diversity of actual skin patterns, (b) capture the underlying dynamics of cellular interactions, (c) interact with tissue size and shape, (d) can lead to ordered sequential patterning, (e) generate cellular automaton dynamics in lizards and snakes, (f) predict actual patterns beyond their statistical features, and (g) are robust to model variations. Third, we discuss the utility of linear stability analysis and perform numerical simulations to demonstrate how deterministic RD emerges from the underlying chaotic microscopic agents.

Nuclear retention coupled with sequential polyadenylation dictates post-transcriptional m6A modification in the nucleus.

N6-methyladenosine (m6A) modification is deemed to be co-transcriptionally installed on pre-mRNAs, thereby influencing various downstream RNA metabolism events. However, the causal relationship between m6A modification and RNA processing is often unclear, resulting in premature or even misleading generalizations on the function of m6A modification. Here, we develop 4sU-coupled m6A-level and isoform-characterization sequencing (4sU-m6A-LAIC-seq) and 4sU-GLORI to quantify the m6A levels for both newly synthesized and steady-state RNAs at transcript and single-base-resolution levels, respectively, which enable dissecting the relationship between m6A modification and alternative RNA polyadenylation. Unexpectedly, our results show that many m6A addition events occur post-transcriptionally, especially on transcripts with high m6A levels. Importantly, we find higher m6A levels on shorter 3' UTR isoforms, which likely result from sequential polyadenylation of longer 3' UTR isoforms with prolonged nuclear dwelling time. Therefore, m6A modification can also take place post-transcriptionally to intimately couple with other key RNA metabolism processes to establish and dynamically regulate epi-transcriptomics in mammalian cells.

CRISPR-Cas III-A Csm6 CARF Domain Is a Ring Nuclease Triggering Stepwise cA4 Cleavage with ApA>p Formation Terminating RNase Activity.

Type III-A CRISPR-Cas surveillance complexes containing multi-subunit Csm effector, guide, and target RNAs exhibit multiple activities, including formation of cyclic-oligoadenylates (cAn) from ATP and subsequent cAn-mediated cleavage of single-strand RNA (ssRNA) by the trans-acting Csm6 RNase. Our structure-function studies have focused on Thermococcus onnurineus Csm6 to deduce mechanistic insights into how cA4 binding to the Csm6 CARF domain triggers the RNase activity of the Csm6 HEPN domain and what factors contribute to regulation of RNA cleavage activity. We demonstrate that the Csm6 CARF domain is a ring nuclease, whereby bound cA4 is stepwise cleaved initially to ApApApA>p and subsequently to ApA>p in its CARF domain-binding pocket, with such cleavage bursts using a timer mechanism to regulate the RNase activity of the Csm6 HEPN domain. In addition, we establish T. onnurineus Csm6 as an adenosine-specific RNase and identify a histidine in the cA4 CARF-binding pocket involved in autoinhibitory regulation of RNase activity.

Electron transport through two interacting channels in Azurin-based solid-state junctions.

The fundamental question of "what is the transport path of electrons through proteins?" initially introduced while studying long-range electron transfer between localized redox centers in proteins in vivo is also highly relevant to the transport properties of solid-state, dry metal-protein-metal junctions. Here, we report conductance measurements of such junctions, Au-(Azurin monolayer ensemble)-Bismuth (Bi) ones, with well-defined nanopore geometry and ~103 proteins/pore. Our results can be understood as follows. (1) Transport is via two interacting conducting channels, characterized by different spatial and time scales. The slow and spatially localized channel is associated with the Cu center of Azurin and the fast delocalized one with the protein's polypeptide matrix. Transport via the slow channel is by a sequential (noncoherent) process and in the second one by direct, off-resonant tunneling. (2) The two channels are capacitively coupled. Thus, with a change in charge occupation of the weakly coupled (metal center) channel, the broad energy level manifold, responsible for off-resonance tunneling, shifts, relative to the electrodes' Fermi levels. In this process, the off-resonance (fast) channel dominates transport, and the slow (redox) channel, while contributing only negligibly directly, significantly affects transport by intramolecular gating.

Reconstructing complex admixture history using a hierarchical model.

Various methods have been proposed to reconstruct admixture histories by analyzing the length of ancestral chromosomal tracts, such as estimating the admixture time and number of admixture events. However, available methods do not explicitly consider the complex admixture structure, which characterizes the joining and mixing patterns of different ancestral populations during the admixture process, and instead assume a simplified one-by-one sequential admixture model. In this study, we proposed a novel approach that considers the non-sequential admixture structure to reconstruct admixture histories. Specifically, we introduced a hierarchical admixture model that incorporated four ancestral populations and developed a new method, called HierarchyMix, which uses the length of ancestral tracts and the number of ancestry switches along genomes to reconstruct the four-way admixture history. By automatically selecting the optimal admixture model using the Bayesian information criterion principles, HierarchyMix effectively estimates the corresponding admixture parameters. Simulation studies confirmed the effectiveness and robustness of HierarchyMix. We also applied HierarchyMix to Uyghurs and Kazakhs, enabling us to reconstruct the admixture histories of Central Asians. Our results highlight the importance of considering complex admixture structures and demonstrate that HierarchyMix is a useful tool for analyzing complex admixture events.

Systematic Optimization of Automated Phosphopeptide Enrichment for High-Sensitivity Phosphoproteomics.

Improving coverage, robustness, and sensitivity is crucial for routine phosphoproteomics analysis by single-shot liquid chromatography-tandem mass spectrometry (LC-MS/MS) from minimal peptide inputs. Here, we systematically optimized key experimental parameters for automated on-bead phosphoproteomics sample preparation with a focus on low-input samples. Assessing the number of identified phosphopeptides, enrichment efficiency, site localization scores, and relative enrichment of multiply-phosphorylated peptides pinpointed critical variables influencing the resulting phosphoproteome. Optimizing glycolic acid concentration in the loading buffer, percentage of ammonium hydroxide in the elution buffer, peptide-to-beads ratio, binding time, sample, and loading buffer volumes allowed us to confidently identify >16,000 phosphopeptides in half-an-hour LC-MS/MS on an Orbitrap Exploris 480 using 30 µg of peptides as starting material. Furthermore, we evaluated how sequential enrichment can boost phosphoproteome coverage and showed that pooling fractions into a single LC-MS/MS analysis increased the depth. We also present an alternative phosphopeptide enrichment strategy based on stepwise addition of beads thereby boosting phosphoproteome coverage by 20%. Finally, we applied our optimized strategy to evaluate phosphoproteome depth with the Orbitrap Astral MS using a cell dilution series and were able to identify >32,000 phosphopeptides from 0.5 million HeLa cells in half-an-hour LC-MS/MS using narrow-window data-independent acquisition (nDIA).

Zinc treatment reverses and anti-Zn-regulated miRs suppress esophageal carcinomas in vivo.

Esophageal squamous cell carcinoma (ESCC) is a deadly disease with few prevention or treatment options. ESCC development in humans and rodents is associated with Zn deficiency (ZD), inflammation, and overexpression of oncogenic microRNAs: miR-31 and miR-21. In a ZD-promoted ESCC rat model with upregulation of these miRs, systemic antimiR-31 suppresses the miR-31-EGLN3/STK40-NF-κB-controlled inflammatory pathway and ESCC. In this model, systemic delivery of Zn-regulated antimiR-31, followed by antimiR-21, restored expression of tumor-suppressor proteins targeted by these specific miRs: STK40/EGLN3 (miR-31), PDCD4 (miR-21), suppressing inflammation, promoting apoptosis, and inhibiting ESCC development. Moreover, ESCC-bearing Zn-deficient (ZD) rats receiving Zn medication showed a 47% decrease in ESCC incidence vs. Zn-untreated controls. Zn treatment eliminated ESCCs by affecting a spectrum of biological processes that included downregulation of expression of the two miRs and miR-31-controlled inflammatory pathway, stimulation of miR-21-PDCD4 axis apoptosis, and reversal of the ESCC metabolome: with decrease in putrescine, increase in glucose, accompanied by downregulation of metabolite enzymes ODC and HK2. Thus, Zn treatment or miR-31/21 silencing are effective therapeutic strategies for ESCC in this rodent model and should be examined in the human counterpart exhibiting the same biological processes.

Risk-Taking Is Associated with Decreased Subjective Value Signals and Increased Prediction Error Signals in the Hot Columbia Card Task.

It remains a pressing concern to understand how neural computations relate to risky decisions. However, most observations of brain-behavior relationships in the risk-taking domain lack a rigorous computational basis or fail to emulate of the dynamic, sequential nature of real-life risky decision-making. Recent advances emphasize the role of neural prediction error (PE) signals. We modeled, according to prospect theory, the choices of n = 43 human participants (33 females, 10 males) performing an EEG version of the hot Columbia Card Task, featuring rounds of sequential decisions between stopping (safe option) and continuing with increasing odds of a high loss (risky option). Single-trial regression EEG analyses yielded a subjective value signal at centroparietal (300-700 ms) and frontocentral (>800 ms) electrodes and in the delta band, as well as PE signals tied to the feedback-related negativity, P3a, and P3b, and in the theta band. Higher risk preference (total number of risky choices) was linked to attenuated subjective value signals but increased PE signals. Higher P3-like activity associated with the most positive PE in each round predicted stopping in the present round but not risk-taking in the subsequent round. Our findings indicate that decreased representation of decision values and increased sensitivity to winning despite low odds (positive PE) facilitate risky choices at the subject level. Strong neural responses when gains are least expected (the most positive PE on each round) adaptively contribute to safer choices at the trial-by-trial level but do not affect risky choice at the round-by-round level.

Neural Correlates of Online Action Preparation.

When performing movements in rapid succession, the brain needs to coordinate ongoing execution with the preparation of an upcoming action. Here we identify the processes and brain areas involved in this ability of online preparation. Human participants (both male and female) performed pairs of single-finger presses or three-finger chords in rapid succession, while 7T fMRI was recorded. In the overlap condition, they could prepare the second movement during the first response and in the nonoverlap condition only after the first response was completed. Despite matched perceptual and movement requirements, fMRI revealed increased brain activity in the overlap condition in regions along the intraparietal sulcus and ventral visual stream. Multivariate analyses suggested that these areas are involved in stimulus identification and action selection. In contrast, the dorsal premotor cortex, known to be involved in planning upcoming movements, showed no discernible signs of heightened activity. This observation suggests that the bottleneck during simultaneous action execution and preparation arises at the level of stimulus identification and action selection, whereas movement planning in the premotor cortex can unfold concurrently with the execution of a current action without requiring additional neural activity.

Attention-based generative adversarial networks improve prognostic outcome prediction of cancer from multimodal data.

The prediction of prognostic outcome is critical for the development of efficient cancer therapeutics and potential personalized medicine. However, due to the heterogeneity and diversity of multimodal data of cancer, data integration and feature selection remain a challenge for prognostic outcome prediction. We proposed a deep learning method with generative adversarial network based on sequential channel-spatial attention modules (CSAM-GAN), a multimodal data integration and feature selection approach, for accomplishing prognostic stratification tasks in cancer. Sequential channel-spatial attention modules equipped with an encoder-decoder are applied for the input features of multimodal data to accurately refine selected features. A discriminator network was proposed to make the generator and discriminator learning in an adversarial way to accurately describe the complex heterogeneous information of multiple modal data. We conducted extensive experiments with various feature selection and classification methods and confirmed that the CSAM-GAN via the multilayer deep neural network (DNN) classifier outperformed these baseline methods on two different multimodal data sets with miRNA expression, mRNA expression and histopathological image data: lower-grade glioma and kidney renal clear cell carcinoma. The CSAM-GAN via the multilayer DNN classifier bridges the gap between heterogenous multimodal data and prognostic outcome prediction.

MLm5C: A high-precision human RNA 5-methylcytosine sites predictor based on a combination of hybrid machine learning models.

RNA modification serves as a pivotal component in numerous biological processes. Among the prevalent modifications, 5-methylcytosine (m5C) significantly influences mRNA export, translation efficiency and cell differentiation and are also associated with human diseases, including Alzheimer's disease, autoimmune disease, cancer, and cardiovascular diseases. Identification of m5C is critically responsible for understanding the RNA modification mechanisms and the epigenetic regulation of associated diseases. However, the large-scale experimental identification of m5C present significant challenges due to labor intensity and time requirements. Several computational tools, using machine learning, have been developed to supplement experimental methods, but identifying these sites lack accuracy and efficiency. In this study, we introduce a new predictor, MLm5C, for precise prediction of m5C sites using sequence data. Briefly, we evaluated eleven RNA sequence-derived features with four basic machine learning algorithms to generate baseline models. From these 44 models, we ranked them based on their performance and subsequently stacked the Top 20 baseline models as the best model, named MLm5C. The MLm5C outperformed the-state-of-the-art predictors. Notably, the optimization of the sequence length surrounding the modification sites significantly improved the prediction performance. MLm5C is an invaluable tool in accelerating the detection of m5C sites within the human genome, thereby facilitating in the characterization of their roles in post-transcriptional regulation.

Neural mechanisms of sequential dependence in time perception: the impact of prior task and memory processing.

Our perception and decision-making are susceptible to prior context. Such sequential dependence has been extensively studied in the visual domain, but less is known about its impact on time perception. Moreover, there are ongoing debates about whether these sequential biases occur at the perceptual stage or during subsequent post-perceptual processing. Using functional magnetic resonance imaging, we investigated neural mechanisms underlying temporal sequential dependence and the role of action in time judgments across trials. Participants performed a timing task where they had to remember the duration of green coherent motion and were cued to either actively reproduce its duration or simply view it passively. We found that sequential biases in time perception were only evident when the preceding task involved active duration reproduction. Merely encoding a prior duration without reproduction failed to induce such biases. Neurally, we observed activation in networks associated with timing, such as striato-thalamo-cortical circuits, and performance monitoring networks, particularly when a "Response" trial was anticipated. Importantly, the hippocampus showed sensitivity to these sequential biases, and its activation negatively correlated with the individual's sequential bias following active reproduction trials. These findings highlight the significant role of memory networks in shaping time-related sequential biases at the post-perceptual stages.

Structural and Functional Impacts of ER Coactivator Sequential Recruitment.

Nuclear receptors recruit multiple coactivators sequentially to activate transcription. This "ordered" recruitment allows different coactivator activities to engage the nuclear receptor complex at different steps of transcription. Estrogen receptor (ER) recruits steroid receptor coactivator-3 (SRC-3) primary coactivator and secondary coactivators, p300/CBP and CARM1. CARM1 recruitment lags behind the binding of SRC-3 and p300 to ER. Combining cryo-electron microscopy (cryo-EM) structure analysis and biochemical approaches, we demonstrate that there is a close crosstalk between early- and late-recruited coactivators. The sequential recruitment of CARM1 not only adds a protein arginine methyltransferase activity to the ER-coactivator complex, it also alters the structural organization of the pre-existing ERE/ERα/SRC-3/p300 complex. It induces a p300 conformational change and significantly increases p300 HAT activity on histone H3K18 residues, which, in turn, promotes CARM1 methylation activity on H3R17 residues to enhance transcriptional activity. This study reveals a structural role for a coactivator sequential recruitment and biochemical process in ER-mediated transcription.

Seasonality, density dependence, and spatial population synchrony.

Studies of spatial population synchrony constitute a central approach for understanding the drivers of ecological dynamics. Recently, identifying the ecological impacts of climate change has emerged as a new important focus in population synchrony studies. However, while it is well known that climatic seasonality and sequential density dependence influences local population dynamics, the role of season-specific density dependence in shaping large-scale population synchrony has not received attention. Here, we present a widely applicable analytical protocol that allows us to account for both season and geographic context-specific density dependence to better elucidate the relative roles of deterministic and stochastic sources of population synchrony, including the renowned Moran effect. We exemplify our protocol by analyzing time series of seasonal (spring and fall) abundance estimates of cyclic rodent populations, revealing that season-specific density dependence is a major component of population synchrony. By accounting for deterministic sources of synchrony (in particular season-specific density dependence), we are able to identify stochastic components. These stochastic components include mild winter weather events, which are expected to increase in frequency under climate warming in boreal and Arctic ecosystems. Interestingly, these weather effects act both directly and delayed on the vole populations, thus enhancing the Moran effect. Our study demonstrates how different drivers of population synchrony, presently altered by climate warming, can be disentangled based on seasonally sampled population time-series data and adequate population models.

Utility of epitope-specific IgE, IgG4, and IgG1 antibodies for the diagnosis of wheat allergy.

BACKGROUND: The bead-based epitope assay has been used to identify epitope-specific (es) antibodies and successfully used to diagnose clinical allergy to milk, egg, and peanut. OBJECTIVE: We sought to identify es-IgE, es-IgG4, and es-IgG1 of wheat proteins and determine the optimal peptides to differentiate wheat-allergic from wheat-tolerant using the bead-based epitope assay. METHODS: Children and adolescents who underwent an oral food challenge to confirm their wheat allergy status were enrolled. Seventy-nine peptides from α-/ß-gliadin, γ-gliadin, ω-5-gliadin, and high- and low-molecular-weight glutenin were commercially synthesized and coupled to LumAvidin beads (Luminex Corporation, Austin, Tex). Machine learning methods were used to identify diagnostic epitopes, and performance was evaluated using the DeLong test. RESULTS: The analysis included 122 children (83 wheat-allergic and 39 wheat-tolerant; 57.4% male). Machine learning coupled with simulations identified wheat es-IgE, but not es-IgG4 or es-IgG1, to be the most informative for diagnosing wheat allergy. Higher es-IgE binding intensity correlated with the severity of allergy phenotypes, with wheat anaphylaxis exhibiting the highest es-IgE binding intensity. In contrast, wheat-dependent exercise-induced anaphylaxis showed lower es-IgG1 binding intensity than did all the other groups. A set of 4 informative epitopes from ω-5-gliadin and γ-gliadin were the best predictors of wheat allergy, with an area under the curve of 0.908 (sensitivity, 83.4%; specificity, 88.4%), higher than the performance exhibited by wheat-specific IgE (area under the curve = 0.646; P < .001). The predictive ability of our model was confirmed in an external cohort of 71 patients (29 allergic, 42 nonallergic), with an area under the curve of 0.908 (sensitivity, 75.9%; specificity, 90.5%). CONCLUSIONS: The wheat bead-based epitope assay demonstrated greater diagnostic accuracy compared with existing specific IgE tests for wheat allergy.

High-Efficiency Pure-Red CsPbI3 Quantum Dot Light-Emitting Diodes Enabled by Strongly Electrostatic Potential Solvent and Sequential Ligand Post-treatment Process.

Efficient pure-red emission light-emitting diodes (LEDs) are essential for high-definition displays, yet achieving pure-red emission is hindered by challenges like phase segregation and spectral instability when using halide mixing. Additionally, strongly confined quantum dots (QDs) produced through traditional hot-injection methods face byproduct contamination due to poor solubility of metal halide salts in the solvent octadecene (ODE) at low temperatures. Herein, we introduced a novel method using a benzene-series strongly electrostatic potential solvent instead of ODE to prevent PbI2 intermediates and promote their dissolution into [PbI3]-. Increasing methyl groups on benzene yields precisely sized (4.4 ± 0.1 nm) CsPbI3 QDs with exceptional properties: a narrow 630 nm PL peak with photoluminescence quantum yield (PLQY) of 97%. Sequential ligand post-treatment optimizes optical and electrical performance of QDs. PeLEDs based on optimized QDs achieve pure-red EL (CIE: 0.700, 0.290) approaching Rec. 2020 standards, with an EQE of 25.2% and T50 of 120 min at initial luminance of 107 cd/m2.

Controlled Sequential Doping of Metal Nanocluster.

Atomically precise doping of metal nanoclusters provides excellent opportunities not only for subtly tailoring their properties but also for in-depth understanding of composition (structure)-property correlation of metal nanoclusters and has attracted increasing interest partly due to its significance for fundamental research and practical applications. Although single and multiple metal atom doping of metal nanoclusters (NCs) has been achieved, sequential single-to-multiple metal atom doping is still a big challenge and has not yet been reported. Herein, by introducing a second ligand, a novel multistep synthesis method was developed, controlled sequential single-to-multiple metal atom doping was successfully achieved for the first time, and three doped NCs Au25Cd1(p-MBT)17(PPh3)2, Au18Cd2(p-MBT)14(PPh3)2, and [Au19Cd3(p-MBT)18]- (p-MBTH: para-methylbenzenethiol) were obtained, including two novel NCs that were precisely characterized via mass spectrometry, single-crystal X-ray crystallography, and so forth. Furthermore, sequential doping-induced evolutions in the atomic and crystallographic structures and optical and catalytic properties of NCs were revealed.

SMART Designs: Bridging the Gap Between Clinical Trials and Practice in Infectious Diseases.

Traditional Randomized Controlled Trials often fall short in addressing the specific needs of clinical practice due to their one-size-fits-all treatment approaches. Sequential Multiple Assignment Randomized Trials (SMARTs) offer a dynamic and adaptive approach, allowing for multiple randomizations based on patient responses and evolving conditions. SMARTs enable personalized treatment pathways, such as in the trial for antiretroviral therapy (ART) in South Africa, which adjusts treatment based on patient outcomes. Despite these advantages, the use of SMARTs in infectious diseases remains limited. Greater adoption of SMARTs could promote more personalized treatment approaches, improve flexibility in response to public health needs, and enhance the effectiveness of interventions. However, challenges such as recruitment and increased expertise needed for more complex analyses must be addressed. Additionally, combining SMARTs with other adaptive designs could further improve the relevance and outcomes of clinical research.

PSSM-Sumo: deep learning based intelligent model for prediction of sumoylation sites using discriminative features.

Post-translational modifications (PTMs) are fundamental to essential biological processes, exerting significant influence over gene expression, protein localization, stability, and genome replication. Sumoylation, a PTM involving the covalent addition of a chemical group to a specific protein sequence, profoundly impacts the functional diversity of proteins. Notably, identifying sumoylation sites has garnered significant attention due to their crucial roles in proteomic functions and their implications in various diseases, including Parkinson's and Alzheimer's. Despite the proposal of several computational models for identifying sumoylation sites, their effectiveness could be improved by the limitations associated with conventional learning methodologies. In this study, we introduce pseudo-position-specific scoring matrix (PsePSSM), a robust computational model designed for accurately predicting sumoylation sites using an optimized deep learning algorithm and efficient feature extraction techniques. Moreover, to streamline computational processes and eliminate irrelevant and noisy features, sequential forward selection using a support vector machine (SFS-SVM) is implemented to identify optimal features. The multi-layer Deep Neural Network (DNN) is a robust classifier, facilitating precise sumoylation site prediction. We meticulously assess the performance of PSSM-Sumo through a tenfold cross-validation approach, employing various statistical metrics such as the Matthews Correlation Coefficient (MCC), accuracy, sensitivity, specificity, and the Area under the ROC Curve (AUC). Comparative analyses reveal that PSSM-Sumo achieves an exceptional average prediction accuracy of 98.71%, surpassing existing models. The robustness and accuracy of the proposed model position it as a promising tool for advancing drug discovery and the diagnosis of diverse diseases linked to sumoylation sites.

Isotopically non-stationary 13 C metabolic flux analysis of two closely related fast-growing cyanobacteria, Synechococcus elongatus PCC 11801 and 11802.

Synechococcus elongatus PCC 11801 and 11802 are closely related cyanobacterial strains that are fast-growing and tolerant to high light and temperature. These strains hold significant promise as chassis for photosynthetic production of chemicals from carbon dioxide. A detailed quantitative understanding of the central carbon pathways would be a reference for future metabolic engineering studies with these strains. We conducted isotopic non-stationary 13 C metabolic flux analysis to quantitively assess the metabolic potential of these two strains. This study highlights key similarities and differences in the central carbon flux distribution between these and other model/non-model strains. The two strains demonstrated a higher Calvin-Benson-Bassham (CBB) cycle flux coupled with negligible flux through the oxidative pentose phosphate pathway and the photorespiratory pathway and lower anaplerosis fluxes under photoautotrophic conditions. Interestingly, PCC 11802 shows the highest CBB cycle and pyruvate kinase flux values among those reported in cyanobacteria. The unique tricarboxylic acid (TCA) cycle diversion in PCC 11801 makes it ideal for the large-scale production of TCA cycle-derived chemicals. Additionally, dynamic labeling transients were measured for intermediates of amino acid, nucleotide, and nucleotide sugar metabolism. Overall, this study provides the first detailed metabolic flux maps of S. elongatus PCC 11801 and 11802, which may aid metabolic engineering efforts in these strains.