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.

Comparison of Frequentist and Bayesian Statistics for Studying Unconscious Perception: Differences Between Null Awareness Dissociation and Relative Sensitivity Dissociation.

For unconscious perception research, Bayesian statistics are more appropriate for assessing null awareness of masked stimuli than traditional (frequentist) statistics. This assertion is based mostly upon the theoretical features of Bayesian statistics and modeling studies. To further assess the potential advantages, we compared frequentist and Bayesian statistical tests in a masked Stroop priming experiment in which the prime stimuli were presented at varying degrees of visibility. A novel contribution was to compare a null awareness dissociation approach (i.e., stimulus awareness = 0) to a relative sensitivity approach (indirect or priming effects > direct effects) for the same data. From a null awareness perspective, the frequentist t-tests for the Stroop effect (i.e., perception) for the briefest display conditions had non-significant outcomes. Similar Bayesian t-tests were inconclusive. In contrast, the relative sensitivity dissociation approach was more interpretable, with strong evidence against unconscious perception from a single Bayesian t test. For the longer display conditions, both statistical approaches suggested large conscious perception effects. We conclude that the utility of Bayesian statistics is highly dependent upon the type of dissociation approach, with a relative sensitivity approach being more straightforward to interpret than a null awareness approach.

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.

Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons.

Investigating the cell cycle often depends on synchronizing cell populations to measure various parameters in a time series as the cells traverse the cell cycle. However, even under similar conditions, replicate experiments display differences in the time required to recover from synchrony and to traverse the cell cycle, thus preventing direct comparisons at each time point. The problem of comparing dynamic measurements across experiments is exacerbated in mutant populations or in alternative growth conditions that affect the synchrony recovery time and/or the cell-cycle period. We have previously published a parametric mathematical model named Characterizing Loss of Cell Cycle Synchrony (CLOCCS) that monitors how synchronous populations of cells release from synchrony and progress through the cell cycle. The learned parameters from the model can then be used to convert experimental time points from synchronized time-series experiments into a normalized time scale (lifeline points). Rather than representing the elapsed time in minutes from the start of the experiment, the lifeline scale represents the progression from synchrony to cell-cycle entry and then through the phases of the cell cycle. Since lifeline points correspond to the phase of the average cell within the synchronized population, this normalized time scale allows for direct comparisons between experiments, including those with varying periods and recovery times. Furthermore, the model has been used to align cell-cycle experiments between different species (e.g., Saccharomyces cerevisiae and Schizosaccharomyces pombe), thus enabling direct comparison of cell-cycle measurements, which may reveal evolutionary similarities and differences.

Learning perturbation-inducible cell states from observability analysis of transcriptome dynamics.

A major challenge in biotechnology and biomanufacturing is the identification of a set of biomarkers for perturbations and metabolites of interest. Here, we develop a data-driven, transcriptome-wide approach to rank perturbation-inducible genes from time-series RNA sequencing data for the discovery of analyte-responsive promoters. This provides a set of biomarkers that act as a proxy for the transcriptional state referred to as cell state. We construct low-dimensional models of gene expression dynamics and rank genes by their ability to capture the perturbation-specific cell state using a novel observability analysis. Using this ranking, we extract 15 analyte-responsive promoters for the organophosphate malathion in the underutilized host organism Pseudomonas fluorescens SBW25. We develop synthetic genetic reporters from each analyte-responsive promoter and characterize their response to malathion. Furthermore, we enhance malathion reporting through the aggregation of the response of individual reporters with a synthetic consortium approach, and we exemplify the library's ability to be useful outside the lab by detecting malathion in the environment. The engineered host cell, a living malathion sensor, can be optimized for use in environmental diagnostics while the developed machine learning tool can be applied to discover perturbation-inducible gene expression systems in the compendium of host organisms.

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.

The Current State of Fat Grafting in the Hand: A Systematic Review for Hand Diseases.

Autologous fat grafting (AFG) has traditionally been used for facial rejuvenation and soft tissue augmentation, but in recent years, its use has expanded to treat diseases of the hand. Autologous fat grafting is ideal for use in the hand because it is minimally invasive, can restore volume, and has regenerative capabilities. This review summarizes the emerging evidence regarding the safety and efficacy of AFG to the hand in several conditions, including systemic sclerosis, Dupuytren disease, osteoarthritis, burns, and traumatic fingertip injuries. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses-compliant literature search on the use of AFG in hand pathologies was performed on October 8, 2020, in Ovid MEDLINE, Elsevier Embase, Clarivate Web of Science, and Wiley Cochrane Central Register of Controlled Trials. The retrieved hits were screened and reviewed by 2 independent reviewers and a third reviewer adjudicated when required. Reviewers identified 919 unique hits. Screening of the abstracts identified 22 manuscripts which described the use of AFG to treat an identified hand condition. Studies suggest AFG in the hands is a safe, noninvasive option for the management of systemic sclerosis, Dupuytren contracture, osteoarthritis, burns, and traumatic fingertip injuries. While AFG is a promising therapeutic option for autoimmune, inflammatory, and fibrotic disease manifestations in the hand, further studies are warranted to understand its efficacy and to establish more robust clinical guidelines. Studies to date show the regenerative, immunomodulatory, and volume-filling properties of AFG that facilitate wound healing and restoration of hand function with limited complications.

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.

Highly-automated, high-throughput replication of yeast-based logic circuit design assessments.

We describe an experimental campaign that replicated the performance assessment of logic gates engineered into cells of Saccharomyces cerevisiae by Gander et al. Our experimental campaign used a novel high-throughput experimentation framework developed under Defense Advanced Research Projects Agency's Synergistic Discovery and Design program: a remote robotic lab at Strateos executed a parameterized experimental protocol. Using this protocol and robotic execution, we generated two orders of magnitude more flow cytometry data than the original experiments. We discuss our results, which largely, but not completely, agree with the original report and make some remarks about lessons learned. Graphical Abstract.

Predictive values of time-dense SARS-CoV-2 wastewater analysis in university campus buildings.

Wastewater-based SARS-CoV-2 surveillance on college campuses has the ability to detect individual clinical COVID-19 cases at the building-level. High concordance of wastewater results and clinical cases has been observed when calculated over a time window of four days or longer and in settings with high incidence of infection. At Duke University, twice a week clinical surveillance of all resident undergraduates was carried out in the spring 2021 semester. We conducted simultaneous wastewater surveillance with daily frequency on selected residence halls to assess wastewater as an early warning tool during times of low transmission with the hope of scaling down clinical test frequency. We evaluated the temporal relationship of the two time-dense data sets, wastewater and clinical, and sought a strategy to achieve the highest wastewater predictive values using the shortest time window to enable timely intervention. There were 11 days with clinical cases in the residence halls (80-120 occupants) under wastewater surveillance with 5 instances of a single clinical case and 3 instances of two clinical cases which also corresponded to a positive wastewater SARS-CoV-2 signal. While the majority (71%) of our wastewater samples were negative for SARS-CoV-2, 29% resulted in at least one positive PCR signal, some of which did not correlate with an identified clinical case. Using a criteria of two consecutive days of positive wastewater signals, we obtained a positive predictive value (PPV) of 75% and a negative predictive value of 87% using a short 2 day time window for agreement. A conventional concordance over a much longer 4 day time window resulted in PPV of only 60%. Our data indicated that daily wastewater collection and using a criteria of two consecutive days of positive wastewater signals was the most predictive approach to timely early warning of COVID-19 cases at the building level.

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.

Association of COVID-19 Quarantine Duration and Postquarantine Transmission Risk in 4 University Cohorts.

Importance: Optimal quarantine length for COVID-19 infection is unclear, in part owing to limited empirical data. Objective: To assess postquarantine transmission risk for various quarantine lengths and potential associations between quarantine strictness and transmission risk. Design, Setting, and Participants: Retrospective cohort study in 4 US universities from September 2020 to February 2021, including 3641 university students and staff who were identified as close contacts to individuals who tested positive for SARS-CoV-2 infection. Individuals were tested throughout the 10 to 14-day quarantine, and follow-up testing continued at least weekly throughout the 2020-2021 academic year. Exposures: Strict quarantine, including designated housing with a private room, private bathroom, and meal delivery, vs nonstrict, which potentially included interactions with household members. Main Outcomes and Measures: Dates of last known exposure, last negative test result, and first positive test result during quarantine. Results: This study included 301 quarantined university students and staff who tested SARS-CoV-2-positive (of 3641 quarantined total). These 301 individuals had a median (IQR) age of 22.0 (20.0-25.0) years; 131 (43.5%) identified as female; and 20 (6.6%) were staff. Of the 287 self-reporting race and ethnicity according to university-defined classifications, 21 (7.3%) were African American or Black, 60 (20.9%) Asian, 17 (5.9%) Hispanic or Latinx, 174 (60.6%) White, and 15 (5.2%) other (including multiracial and/or multiethnic). Of the 301 participants, 40 (13.3%; 95% CI, 9.9%-17.6%) had negative test results and were asymptomatic on day 7 compared with 15 (4.9%; 95% CI, 3.0%-8.1%) and 4 (1.4%; 95% CI, 0.4%-3.5%) on days 10 and 14, respectively. Individuals in strict quarantine tested positive less frequently than those in nonstrict quarantine (10% vs 12%; P = .04). Conclusions and Relevance: To maintain the 5% transmission risk used as the basis for US Centers for Disease Control and Prevention's 7-day test-based quarantine guidance, our data suggest that quarantine with quantitative polymerase chain reaction testing 1 day before intended release should be 10 days for nonstrict quarantine and 8 days for strict quarantine, as ongoing exposure during quarantine may be associated with the higher rate of positive test results following nonstrict quarantine.

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.

Task-Specific Patient Preferences for Shared Decision-Making in Hand Surgery.

BACKGROUND: Shared decision-making for surgery can increase patient engagement, satisfaction, and clinical outcomes. However, the level of involvement that patients desire at each step of the decision-making process is unknown. METHODS: The authors surveyed patients at an academic hand surgery clinic to examine the preferred role in decision-making using validated questionnaires (i.e., Control Preference Scale, Problem-Solving Decision-Making Scale, and General Self-Efficacy Scale). The Control Preference Scale assesses general treatment preferences, whereas the Problem-Solving Decision-Making Scale distinguishes between problem-solving tasks (e.g., making diagnoses, calculating risks/benefits) and decision-making tasks. Patients' self-beliefs and perceived ability to handle difficult situations were assessed with the General Self-Efficacy Scale. The authors used linear regression models and ordinal logistic regression to examine the relationship between self-efficacy and patients' preferred role in treatment decision-making. RESULTS: Patients overall preferred an equal share of decision-making responsibility with the surgeon (mean Control Preference Scale score, 3.3 ± 0.7). Specifically, for problem-solving tasks, however, 81 percent of patients wanted to "hand over" the responsibility and 19 percent preferred shared decision-making. In contrast, for decision-making tasks, 54 percent of patients preferred shared decision-making. Each point increase in General Self-Efficacy Scale score correlated with 12 percent greater odds of preferring to retain the responsibility (OR, 1.12; 95 percent CI, 1.05 to 1.21; p = 0.001). However, self-efficacy did not show a significant effect for problem-solving tasks. CONCLUSIONS: The authors found that patients prefer surgeons to provide expert knowledge for problem-solving tasks but desire equal share of responsibility in decision-making tasks. The authors' findings support the current shift away from the paternalistic model of surgical decision-making, and provide an effective strategy to tailor shared decision-making to align care delivery with patient preferences.

Trust and Distrust in Opioid Decision-Making: A Qualitative Assessment of Patient-Doctor Relationship.

PURPOSE: Surgeons often prescribe opioid analgesics for pain management after surgery. However, we understand little about how patients perceive opioid prescribing and make decisions to use opioids for postoperative pain management. In this study, we aimed to gain an understanding of patients' decision-making process on postoperative opioid use. METHODS: We conducted semi-structured interviews with 30 adult patients undergoing elective surgery at our institution. The interviews were content-coded for thematic analysis. We used trust in the medical setting as a conceptual framework to interpret and find the inherent theory in the data. RESULTS: We found that participants based their opioid decisions on their trust or distrust toward various elements of their postoperative pain management. Participants believed that the surgeons "know," thereby, reinforcing their trust in surgeons' postoperative opioid prescribing to be in the participants' best interest. Moreover, the positive reputation of the institution strengthened the participants' trust. However, participants conveyed nuanced trust because of their distrust toward the opioid medications themselves, which were viewed as "suspicious," and the pharmaceutical companies, that were "despised." Despite this distrust, participants had confidence in their inherent ability to protect themselves from opioid use disorders. CONCLUSIONS: Understanding how patients perceive and form decisions on postoperative opioid use based on their trust and distrust toward various factors involved in their care highlights the importance of the patient-doctor relationship and building trust to effectively address postoperative pain and reduce opioid-related harms. CLINICAL RELEVANCE: Through a strengthened therapeutic alliance between patients and surgeons, we can improve our strategies to overcome the ongoing opioid epidemic through patient-centered approaches.

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.

Evaluation of Factors Relevant to Pain Control Among Patients After Surgical Treatment.

Importance: Opioids are often prescribed after elective surgical treatment despite the potential for misuse. Although various pain control regimens exist, patient preferences for acute postoperative pain management are unknown. Objective: To describe patient-reported key attributes of postoperative pain management. Design, Setting, and Participants: This decision analytical model used responses from a survey based on conjoint analysis to investigate the value patients placed on different aspects of postoperative pain management. Participants were patients aged 18 years or older who underwent elective hand surgical procedures between July 1, 2018, and July 23, 2019, at a single academic center. The survey was completed on a web-based platform and took place between November 2019 and January 2020. Data were analyzed from May through July 2021. Exposures: Participants were presented with a series of discrete-choice tasks and asked to select between 2 postoperative medication options that changed from question to question and had varying characteristics. Main Outcomes and Measures: Attribute importance scores and part-worth utility values for the queried aspects of pain control were calculated. Results: Of 710 individuals invited, 321 (45.2%) completed the survey; there were 212 (66.0%) women and 108 (33.6%) men, and the most common age category was 60 to 69 years (102 participants [31.8%]). Most patients reported previous opioid use (282 individuals [87.9%]). Factors in the decision-making process with the highest attribute importance scores (SDs) were risk of addiction (26.3% [13.0%]) and amount of pain relief (25.6% [14.6%]). Adverse effects 13.9% (7.2%), functional independence 11.8% (7.3%), and level of trust in the prescriber 11.4% (5.8%) had intermediate attribute importance scores (SDs). Cost 7.9% (4.4%) and stigma 3.1% (1.3%) had the lowest attribute importance scores (SDs) in patient decisions. Conclusions and Relevance: These findings suggest that multimodal pain control regimens that are associated with optimized pain relief and minimized risk of addiction are preferable to treat acute postoperative pain. The results suggest that identifying procedures for which patients prioritize minimizing risk of addiction over pain relief and incorporating patient preferences into decision-making may be associated with decreased postoperative opioid prescribing.

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.

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.