The parasite intraerythrocytic cycle and human circadian cycle are coupled during malaria infection.

During infections with the malaria parasites Plasmodium vivax, patients exhibit rhythmic fevers every 48 h. These fever cycles correspond with the time the parasites take to traverse the intraerythrocytic cycle (IEC). In other Plasmodium species that infect either humans or mice, the IEC is likely guided by a parasite-intrinsic clock [Rijo-Ferreiraet al., Science 368, 746-753 (2020); Smith et al., Science 368, 754-759 (2020)], suggesting that intrinsic clock mechanisms may be a fundamental feature of malaria parasites. Moreover, because Plasmodium cycle times are multiples of 24 h, the IECs may be coordinated with the host circadian clock(s). Such coordination could explain the synchronization of the parasite population in the host and enable alignment of IEC and circadian cycle phases. We utilized an ex vivo culture of whole blood from patients infected with P. vivax to examine the dynamics of the host circadian transcriptome and the parasite IEC transcriptome. Transcriptome dynamics revealed that the phases of the host circadian cycle and the parasite IEC are correlated across multiple patients, showing that the cycles are phase coupled. In mouse model systems, host-parasite cycle coupling appears to provide a selective advantage for the parasite. Thus, understanding how host and parasite cycles are coupled in humans could enable antimalarial therapies that disrupt this coupling.

Experimental guidance for discovering genetic networks through hypothesis reduction on time series.

Large programs of dynamic gene expression, like cell cyles and circadian rhythms, are controlled by a relatively small "core" network of transcription factors and post-translational modifiers, working in concerted mutual regulation. Recent work suggests that system-independent, quantitative features of the dynamics of gene expression can be used to identify core regulators. We introduce an approach of iterative network hypothesis reduction from time-series data in which increasingly complex features of the dynamic expression of individual, pairs, and entire collections of genes are used to infer functional network models that can produce the observed transcriptional program. The culmination of our work is a computational pipeline, Iterative Network Hypothesis Reduction from Temporal Dynamics (Inherent dynamics pipeline), that provides a priority listing of targets for genetic perturbation to experimentally infer network structure. We demonstrate the capability of this integrated computational pipeline on synthetic and yeast cell-cycle data.

Conservation of dynamic characteristics of transcriptional regulatory elements in periodic biological processes.

BACKGROUND: Cell and circadian cycles control a large fraction of cell and organismal physiology by regulating large periodic transcriptional programs that encompass anywhere from 15 to 80% of the genome despite performing distinct functions. In each case, these large periodic transcriptional programs are controlled by gene regulatory networks (GRNs), and it has been shown through genetics and chromosome mapping approaches in model systems that at the core of these GRNs are small sets of genes that drive the transcript dynamics of the GRNs. However, it is unlikely that we have identified all of these core genes, even in model organisms. Moreover, large periodic transcriptional programs controlling a variety of processes certainly exist in important non-model organisms where genetic approaches to identifying networks are expensive, time-consuming, or intractable. Ideally, the core network components could be identified using data-driven approaches on the transcriptome dynamics data already available. RESULTS: This study shows that a unified set of quantified dynamic features of high-throughput time series gene expression data are more prominent in the core transcriptional regulators of cell and circadian cycles than in their outputs, in multiple organism, even in the presence of external periodic stimuli. Additionally, we observe that the power to discriminate between core and non-core genes is largely insensitive to the particular choice of quantification of these features. CONCLUSIONS: There are practical applications of the approach presented in this study for network inference, since the result is a ranking of genes that is enriched for core regulatory elements driving a periodic phenotype. In this way, the method provides a prioritization of follow-up genetic experiments. Furthermore, these findings reveal something unexpected-that there are shared dynamic features of the transcript abundance of core components of unrelated GRNs that control disparate periodic phenotypes.

A yeast cell cycle pulse generator model shows consistency with multiple oscillatory and checkpoint mutant datasets.

Modeling biological systems holds great promise for speeding up the rate of discovery in systems biology by predicting experimental outcomes and suggesting targeted interventions. However, this process is dogged by an identifiability issue, in which network models and their parameters are not sufficiently constrained by coarse and noisy data to ensure unique solutions. In this work, we evaluated the capability of a simplified yeast cell-cycle network model to reproduce multiple observed transcriptomic behaviors under genomic mutations. We matched time-series data from both cycling and checkpoint arrested cells to model predictions using an asynchronous multi-level Boolean approach. We showed that this single network model, despite its simplicity, is capable of exhibiting dynamical behavior similar to the datasets in most cases, and we demonstrated the drop in severity of the identifiability issue that results from matching multiple datasets.

AutoGater: a weakly supervised neural network model to gate cells in flow cytometric analyses.

Flow cytometry is a useful and efficient method for the rapid characterization of a cell population based on the optical and fluorescence properties of individual cells. Ideally, the cell population would consist of only healthy viable cells as dead cells can confound the analysis. Thus, separating out healthy cells from dying and dead cells, and any potential debris, is an important first step in analysis of flow cytometry data. While gating of debris can be conducted using measured optical properties, identifying dead and dying cells often requires utilizing fluorescent stains (e.g. Sytox, a nucleic acid stain that stains cells with compromised cell membranes) to identify cells that should be excluded from downstream analyses. These stains prolong the experimental preparation process and use a flow cytometer's fluorescence channels that could otherwise be used to measure additional fluorescent markers within the cells (e.g. reporter proteins). Here we outline a stain-free method for identifying viable cells for downstream processing by gating cells that are dying or dead. AutoGater is a weakly supervised deep learning model that can separate healthy populations from unhealthy and dead populations using only light-scatter channels. In addition, AutoGater harmonizes different measurements of dead cells such as Sytox and CFUs.

Robustness and reproducibility of simple and complex synthetic logic circuit designs using a DBTL loop.

Computational tools addressing various components of design-build-test-learn (DBTL) loops for the construction of synthetic genetic networks exist but do not generally cover the entire DBTL loop. This manuscript introduces an end-to-end sequence of tools that together form a DBTL loop called Design Assemble Round Trip (DART). DART provides rational selection and refinement of genetic parts to construct and test a circuit. Computational support for experimental process, metadata management, standardized data collection and reproducible data analysis is provided via the previously published Round Trip (RT) test-learn loop. The primary focus of this work is on the Design Assemble (DA) part of the tool chain, which improves on previous techniques by screening up to thousands of network topologies for robust performance using a novel robustness score derived from dynamical behavior based on circuit topology only. In addition, novel experimental support software is introduced for the assembly of genetic circuits. A complete design-through-analysis sequence is presented using several OR and NOR circuit designs, with and without structural redundancy, that are implemented in budding yeast. The execution of DART tested the predictions of the design tools, specifically with regard to robust and reproducible performance under different experimental conditions. The data analysis depended on a novel application of machine learning techniques to segment bimodal flow cytometry distributions. Evidence is presented that, in some cases, a more complex build may impart more robustness and reproducibility across experimental conditions. Graphical Abstract.

Round Trip: An Automated Pipeline for Experimental Design, Execution, and Analysis.

Synthetic biology is a complex discipline that involves creating detailed, purpose-built designs from genetic parts. This process is often phrased as a Design-Build-Test-Learn loop, where iterative design improvements can be made, implemented, measured, and analyzed. Automation can potentially improve both the end-to-end duration of the process and the utility of data produced by the process. One of the most important considerations for the development of effective automation and quality data is a rigorous description of implicit knowledge encoded as a formal knowledge representation. The development of knowledge representation for the process poses a number of challenges, including developing effective human-machine interfaces, protecting against and repairing user error, providing flexibility for terminological mismatches, and supporting extensibility to new experimental types. We address these challenges with the DARPA SD2 Round Trip software architecture. The Round Trip is an open architecture that automates many of the key steps in the Test and Learn phases of a Design-Build-Test-Learn loop for high-throughput laboratory science. The primary contribution of the Round Trip is to assist with and otherwise automate metadata creation, curation, standardization, and linkage with experimental data. The Round Trip's focus on metadata supports fast, automated, and replicable analysis of experiments as well as experimental situational awareness and experimental interpretability. We highlight the major software components and data representations that enable the Round Trip to speed up the design and analysis of experiments by 2 orders of magnitude over prior ad hoc methods. These contributions support a number of experimental protocols and experimental types, demonstrating the Round Trip's breadth and extensibility. We describe both an illustrative use case using the Round Trip for an on-the-loop experimental campaign and overall contributions to reducing experimental analysis time and increasing data product volume in the SD2 program.

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline.

Developing gene regulatory network models is a major challenge in systems biology. Several computational tools and pipelines have been developed to tackle this challenge, including the newly developed Inherent Dynamics Pipeline. The Inherent Dynamics Pipeline consists of several previously published tools that work synergistically and are connected in a linear fashion, where the output of one tool is then used as input for the following tool. As with most computational techniques, each step of the Inherent Dynamics Pipeline requires the user to make choices about parameters that don't have a precise biological definition. These choices can substantially impact gene regulatory network models produced by the analysis. For this reason, the ability to visualize and explore the consequences of various parameter choices at each step can help increase confidence in the choices and the results.The Inherent Dynamics Visualizer is a comprehensive visualization package that streamlines the process of evaluating parameter choices through an interactive interface within a web browser. The user can separately examine the output of each step of the pipeline, make intuitive changes based on visual information, and benefit from the automatic production of necessary input files for the Inherent Dynamics Pipeline. The Inherent Dynamics Visualizer provides an unparalleled level of access to a highly intricate tool for the discovery of gene regulatory networks from time series transcriptomic data.

Inference of Gene Regulatory Network Uncovers the Linkage between Circadian Clock and Crassulacean Acid Metabolism in Kalanchoë fedtschenkoi.

The circadian clock drives time-specific gene expression, enabling biological processes to be temporally controlled. Plants that conduct crassulacean acid metabolism (CAM) photosynthesis represent an interesting case of circadian regulation of gene expression as stomatal movement is temporally inverted relative to stomatal movement in C3 plants. The mechanisms behind how the circadian clock enabled physiological differences at the molecular level is not well understood. Recently, the rescheduling of gene expression was reported as a mechanism to explain how CAM evolved from C3. Therefore, we investigated whether core circadian clock genes in CAM plants were re-phased during evolution, or whether networks of phase-specific genes were simply re-wired to different core clock genes. We identified candidate core clock genes based on gene expression features and then applied the Local Edge Machine (LEM) algorithm to infer regulatory relationships between this new set of core candidates and known core clock genes in Kalanchoë fedtschenkoi. We further inferred stomata-related gene targets for known and candidate core clock genes and constructed a gene regulatory network for core clock and stomata-related genes. Our results provide new insight into the mechanism of circadian control of CAM-related genes in K. fedtschenkoi, facilitating the engineering of CAM machinery into non-CAM plants for sustainable crop production in water-limited environments.

Impact of Sickle Cell Trait Hemoglobin on the Intraerythrocytic Transcriptional Program of Plasmodium falciparum.

Sickle-trait hemoglobin (HbAS) confers nearly complete protection from severe, life-threatening falciparum malaria in African children. Despite this clear protection, the molecular mechanisms by which HbAS confers these protective phenotypes remain incompletely understood. As a forward genetic screen for aberrant parasite transcriptional responses associated with parasite neutralization in HbAS red blood cells (RBCs), we performed comparative transcriptomic analyses of Plasmodium falciparum in normal (HbAA) and HbAS erythrocytes during both in vitro cultivation of reference parasite strains and naturally occurring P. falciparum infections in Malian children with HbAA or HbAS. During in vitro cultivation, parasites matured normally in HbAS RBCs, and the temporal expression was largely unperturbed of the highly ordered transcriptional program that underlies the parasite's maturation throughout the intraerythrocytic development cycle (IDC). However, differential expression analysis identified hundreds of transcripts aberrantly expressed in HbAS, largely occurring late in the IDC. Surprisingly, transcripts encoding members of the Maurer's clefts were overexpressed in HbAS despite impaired parasite protein export in these RBCs, while parasites in HbAS RBCs underexpressed transcripts associated with the endoplasmic reticulum and those encoding serine repeat antigen proteases that promote parasite egress. Analyses of P. falciparum transcriptomes from 32 children with uncomplicated malaria identified stage-specific differential expression: among infections composed of ring-stage parasites, only cyclophilin 19B was underexpressed in children with HbAS, while trophozoite-stage infections identified a range of differentially expressed transcripts, including downregulation in HbAS of several transcripts associated with severe malaria in collateral studies. Collectively, our comparative transcriptomic screen in vitro and in vivo indicates that P. falciparum adapts to HbAS by altering its protein chaperone and folding machinery, oxidative stress response, and protein export machinery. Because HbAS consistently protects from severe P. falciparum, modulation of these responses may offer avenues by which to neutralize P. falciparum parasites. IMPORTANCE Sickle-trait hemoglobin (HbAS) confers nearly complete protection from severe, life-threatening malaria, yet the molecular mechanisms that underlie HbAS protection from severe malaria remain incompletely understood. Here, we used transcriptome sequencing (RNA-seq) to measure the impact of HbAS on the blood-stage transcriptome of Plasmodium falciparum in in vitro time series experiments and in vivo samples from natural infections. Our in vitro time series data reveal that, during its blood stage, P. falciparum's gene expression in HbAS is impacted primarily through alterations in the abundance of gene products as opposed to variations in the timing of gene expression. Collectively, our in vitro and in vivo data indicate that P. falciparum adapts to HbAS by altering its protein chaperone and folding machinery, oxidative stress response, and protein export machinery. Due to the persistent association of HbAS and protection from severe disease, these processes that are modified in HbAS may offer strategies to neutralize P. falciparum.

Comparative Genomics Analysis Provides New Insight Into Molecular Basis of Stomatal Movement in Kalanchoë fedtschenkoi.

CO2 uptake and water loss in plants are regulated by microscopic pores on the surface of leaves, called stomata. This enablement of gas exchange by the opening and closing of stomata is one of the most essential processes in plant photosynthesis and transpiration, affecting water-use efficiency (WUE) and thus drought susceptibility. In plant species with crassulacean acid metabolism (CAM) photosynthesis, diel stomatal movement pattern is inverted relative to C3 and C4 photosynthesis species, resulting in much higher WUE and drought tolerance. However, little is known about the molecular basis of stomatal movement in CAM species. The goal of this study is to identify candidate genes that could play a role in stomatal movement in an obligate CAM species, Kalanchoë fedtschenkoi. By way of a text-mining approach, proteins were identified in various plant species, spanning C3, C4, and CAM photosynthetic types, which are orthologous to proteins known to be involved in stomatal movement. A comparative analysis of diel time-course gene expression data was performed between K. fedtschenkoi and two C3 species (i.e., Arabidopsis thaliana and Solanum lycopersicum) to identify differential gene expression between the dusk and dawn phases of the 24-h cycle. A rescheduled catalase gene known to be involved in stomatal movement was identified, suggesting a role for H2O2 in CAM-like stomatal movement. Overall, these results provide new insights into the molecular regulation of stomatal movement in CAM plants, facilitating genetic improvement of drought resistance in agricultural crops through manipulation of stomata-related genes.

Conservation and Diversification of Circadian Rhythmicity Between a Model Crassulacean Acid Metabolism Plant Kalanchoë fedtschenkoi and a Model C3 Photosynthesis Plant Arabidopsis thaliana.

Crassulacean acid metabolism (CAM) improves photosynthetic efficiency under limited water availability relative to C3 photosynthesis. It is widely accepted that CAM plants have evolved from C3 plants and it is hypothesized that CAM is under the control of the internal circadian clock. However, the role that the circadian clock plays in the evolution of CAM is not well understood. To identify the molecular basis of circadian control over CAM evolution, rhythmic gene sets were identified in a CAM model plant species (Kalanchoë fedtschenkoi) and a C3 model plant species (Arabidopsis thaliana) through analysis of diel time-course gene expression data using multiple periodicity detection algorithms. Based on protein sequences, ortholog groups were constructed containing genes from each of these two species. The ortholog groups were categorized into five gene sets based on conservation and diversification of rhythmic gene expression. Interestingly, minimal functional overlap was observed when comparing the rhythmic gene sets of each species. Specifcally, metabolic processes were enriched in the gene set under circadian control in K. fedtschenkoi and numerous genes were found to have retained or gained rhythmic expression in K. fedtsechenkoi. Additonally, several rhythmic orthologs, including CAM-related orthologs, displayed phase shifts between species. Results of this analysis point to several mechanisms by which the circadian clock plays a role in the evolution of CAM. These genes provide a set of testable hypotheses for future experiments.

Perspectives on the basic and applied aspects of crassulacean acid metabolism (CAM) research.

Due to public concerns about the decreasing supply of blue water and increasing heat and drought stress on plant growth caused by urbanization, increasing human population and climate change, interest in crassulacean acid metabolism (CAM), a specialized type of photosynthesis enhancing water-use efficiency (WUE) and drought tolerance, has increased markedly. Significant progress has been achieved in both basic and applied research in CAM plants since the beginning of this century. Here we provide a brief overview of the current status of CAM research, and discuss future needs and opportunities in a wide range of areas including systems biology, synthetic biology, and utilization of CAM crops for human benefit, with a focus on the following aspects: 1) application of genome-editing technology and high-throughput phenotyping to functional genomics research in model CAM species and genetic improvement of CAM crops, 2) challenges for multi-scale metabolic modeling of CAM systems, 3) opportunities and new strategies for CAM pathway engineering to enhance WUE and drought tolerance in C3 (and C4) photosynthesis crops, 4) potential of CAM species as resources for food, feed, natural products, pharmaceuticals and biofuels, and 5) development of CAM crops for ecological and aesthetic benefits.

The Kalanchoë genome provides insights into convergent evolution and building blocks of crassulacean acid metabolism.

Crassulacean acid metabolism (CAM) is a water-use efficient adaptation of photosynthesis that has evolved independently many times in diverse lineages of flowering plants. We hypothesize that convergent evolution of protein sequence and temporal gene expression underpins the independent emergences of CAM from C3 photosynthesis. To test this hypothesis, we generate a de novo genome assembly and genome-wide transcript expression data for Kalanchoë fedtschenkoi, an obligate CAM species within the core eudicots with a relatively small genome (~260 Mb). Our comparative analyses identify signatures of convergence in protein sequence and re-scheduling of diel transcript expression of genes involved in nocturnal CO2 fixation, stomatal movement, heat tolerance, circadian clock, and carbohydrate metabolism in K. fedtschenkoi and other CAM species in comparison with non-CAM species. These findings provide new insights into molecular convergence and building blocks of CAM and will facilitate CAM-into-C3 photosynthesis engineering to enhance water-use efficiency in crops.