A model of dopamine and serotonin-kynurenine metabolism in cortisolemia: Implications for depression.

A major factor contributing to the etiology of depression is a neurochemical imbalance of the dopaminergic and serotonergic systems, which is caused by persistently high levels of circulating stress hormones. Here, a computational model is proposed to investigate the interplay between dopaminergic and serotonergic-kynurenine metabolism under cortisolemia and its consequences for the onset of depression. The model was formulated as a set of nonlinear ordinary differential equations represented with power-law functions. Parameter values were obtained from experimental data reported in the literature, biological databases, and other general information, and subsequently fine-tuned through optimization. Model simulations predict that changes in the kynurenine pathway, caused by elevated levels of cortisol, can increase the risk of neurotoxicity and lead to increased levels of 3,4-dihydroxyphenylaceltahyde (DOPAL) and 5-hydroxyindoleacetaldehyde (5-HIAL). These aldehydes contribute to alpha-synuclein aggregation and may cause mitochondrial fragmentation. Further model analysis demonstrated that the inhibition of both serotonin transport and kynurenine-3-monooxygenase decreased the levels of DOPAL and 5-HIAL and the neurotoxic risk often associated with depression. The mathematical model was also able to predict a novel role of the dopamine and serotonin metabolites DOPAL and 5-HIAL in the ethiology of depression, which is facilitated through increased cortisol levels. Finally, the model analysis suggests treatment with a combination of inhibitors of serotonin transport and kynurenine-3-monooxygenase as a potentially effective pharmacological strategy to revert the slow-down in monoamine neurotransmission that is often triggered by inflammation.

Clinical recovery of Macaca fascicularis infected with Plasmodium knowlesi.

BACKGROUND: Kra monkeys (Macaca fascicularis), a natural host of Plasmodium knowlesi, control parasitaemia caused by this parasite species and escape death without treatment. Knowledge of the disease progression and resilience in kra monkeys will aid the effective use of this species to study mechanisms of resilience to malaria. This longitudinal study aimed to define clinical, physiological and pathological changes in kra monkeys infected with P. knowlesi, which could explain their resilient phenotype. METHODS: Kra monkeys (n = 15, male, young adults) were infected intravenously with cryopreserved P. knowlesi sporozoites and the resulting parasitaemias were monitored daily. Complete blood counts, reticulocyte counts, blood chemistry and physiological telemetry data (n = 7) were acquired as described prior to infection to establish baseline values and then daily after inoculation for up to 50 days. Bone marrow aspirates, plasma samples, and 22 tissue samples were collected at specific time points to evaluate longitudinal clinical, physiological and pathological effects of P. knowlesi infections during acute and chronic infections. RESULTS: As expected, the kra monkeys controlled acute infections and remained with low-level, persistent parasitaemias without anti-malarial intervention. Unexpectedly, early in the infection, fevers developed, which ultimately returned to baseline, as well as mild to moderate thrombocytopenia, and moderate to severe anaemia. Mathematical modelling and the reticulocyte production index indicated that the anaemia was largely due to the removal of uninfected erythrocytes and not impaired production of erythrocytes. Mild tissue damage was observed, and tissue parasite load was associated with tissue damage even though parasite accumulation in the tissues was generally low. CONCLUSIONS: Kra monkeys experimentally infected with P. knowlesi sporozoites presented with multiple clinical signs of malaria that varied in severity among individuals. Overall, the animals shared common mechanisms of resilience characterized by controlling parasitaemia 3-5 days after patency, and controlling fever, coupled with physiological and bone marrow responses to compensate for anaemia. Together, these responses likely minimized tissue damage while supporting the establishment of chronic infections, which may be important for transmission in natural endemic settings. These results provide new foundational insights into malaria pathogenesis and resilience in kra monkeys, which may improve understanding of human infections.

Metabolic modeling helps interpret transcriptomic changes during malaria.

Disease represents a specific case of malfunctioning within a complex system. Whereas it is often feasible to observe and possibly treat the symptoms of a disease, it is much more challenging to identify and characterize its molecular root causes. Even in infectious diseases that are caused by a known parasite, it is often impossible to pinpoint exactly which molecular profiles of components or processes are directly or indirectly altered. However, a deep understanding of such profiles is a prerequisite for rational, efficacious treatments. Modern omics methodologies are permitting large-scale scans of some molecular profiles, but these scans often yield results that are not intuitive and difficult to interpret. For instance, the comparison of healthy and diseased transcriptome profiles may point to certain sets of involved genes, but a host of post-transcriptional processes and regulatory mechanisms renders predictions regarding metabolic or physiological consequences of the observed changes in gene expression unreliable. Here we present proof of concept that dynamic models of metabolic pathway systems may offer a tool for interpreting transcriptomic profiles measured during disease. We illustrate this strategy with the interpretation of expression data of genes coding for enzymes associated with purine metabolism. These data were obtained during infections of rhesus macaques (Macaca mulatta) with the malaria parasite Plasmodium cynomolgi or P. coatneyi. The model-based interpretation reveals clear patterns of flux redistribution within the purine pathway that are consistent between the two malaria pathogens and are even reflected in data from humans infected with P. falciparum. This article is part of a Special Issue entitled: Accelerating Precision Medicine through Genetic and Genomic Big Data Analysis edited by Yudong Cai & Tao Huang.

Analysis of erythrocyte dynamics in Rhesus macaque monkeys during infection with Plasmodium cynomolgi.

BACKGROUND: Malaria is a major mosquito transmitted, blood-borne parasitic disease that afflicts humans. The disease causes anaemia and other clinical complications, which can lead to death. Plasmodium vivax is known for its reticulocyte host cell specificity, but many gaps in disease details remain. Much less is known about the closely related species, Plasmodium cynomolgi, although it is naturally acquired and causes zoonotic malaria. Here, a computational model is developed based on longitudinal analyses of P. cynomolgi infections in nonhuman primates to investigate the erythrocyte dynamics that is pertinent to understanding both P. cynomolgi and P. vivax malaria in humans. METHODS: A cohort of five P. cynomolgi infected Rhesus macaques (Macaca mulatta) is studied, with individuals exhibiting a plethora of clinical outcomes, including varying levels of anaemia. A discrete recursive model with age structure is developed to replicate the dynamics of P. cynomolgi blood-stage infections. The model allows for parasitic reticulocyte preference and assumes an age preference among the mature RBCs. RBC senescence is modelled using a hazard function, according to which RBCs have a mean lifespan of 98 ± 21 days. RESULTS: Based on in vivo data from three cohorts of macaques, the computational model is used to characterize the reticulocyte lifespan in circulation as 24 ± 5 h (n = 15) and the rate of RBC production as 2727 ± 209 cells/h/µL (n = 15). Analysis of the host responses reveals a pre-patency increase in the number of reticulocytes. It also allows the quantification of RBC removal through the bystander effect. CONCLUSIONS: The evident pre-patency increase in reticulocytes is due to a shift towards the release of younger reticulocytes, which could result from a parasite-induced factor meant to increase reticulocyte availability and satisfy the parasite's tropism, which has an average value of 32:1 in this cohort. The number of RBCs lost due to the bystander effect relative to infection-induced RBC losses is 62% for P. cynomolgi infections, which is substantially lower than the value of 95% previously determined for another simian species, Plasmodium coatneyi.

A model of Plasmodium vivax concealment based on Plasmodium cynomolgi infections in Macaca mulatta.

BACKGROUND: Plasmodium vivax can cause severe malaria. The total parasite biomass during infections is correlated with the severity of disease but not necessarily quantified accurately by microscopy. This finding has raised the question whether there could be sub-populations of parasites that are not observed in peripheral blood smears but continue to contribute to the increase in parasite numbers that drive pathogenesis. Non-human primate infection models utilizing the closely related simian malaria parasite Plasmodium cynomolgi hold the potential for quantifying the magnitude of possibly unobserved infected red blood cell (iRBC) populations and determining how the presence of this hidden reservoir correlates with disease severity. METHODS: Time series data tracking the longitudinal development of parasitaemia in five Macaca mulatta infected with P. cynomolgi were used to design a computational model quantifying iRBCs that circulate in the blood versus those that are not detectable and are termed here as 'concealed'. This terminology is proposed to distinguish such observations from the deep vascular and widespread 'sequestration' of Plasmodium falciparum iRBCs, which is governed by distinctly different molecular mechanisms. RESULTS: The computational model presented here clearly demonstrates that the observed growth data of iRBC populations are not consistent with the known biology and blood-stage cycle of P. cynomolgi. However, the discrepancies can be resolved when a sub-population of concealed iRBCs is taken into account. The model suggests that the early growth of a hidden parasite sub-population has the potential to drive disease. As an alternative, the data could be explained by the sequential release of merozoites from the liver over a number of days, but this scenario seems less likely. CONCLUSIONS: Concealment of a non-circulating iRBC sub-population during P. cynomolgi infection of M. mulatta is an important aspect of this successful host-pathogen relationship. The data also support the likelihood that a sub-population of iRBCs of P. vivax has a comparable means to become withdrawn from the peripheral circulation. This inference has implications for understanding vivax biology and pathogenesis and stresses the importance of considering a concealed parasite reservoir with regard to vivax epidemiology and the quantification and treatment of P. vivax infections.

Dynamics of the Heat Stress Response of Ceramides with Different Fatty-Acyl Chain Lengths in Baker's Yeast.

The article demonstrates that computational modeling has the capacity to convert metabolic snapshots, taken sequentially over time, into a description of cellular, dynamic strategies. The specific application is a detailed analysis of a set of actions with which Saccharomyces cerevisiae responds to heat stress. Using time dependent metabolic concentration data, we use a combination of mathematical modeling, reverse engineering, and optimization to infer dynamic changes in enzyme activities within the sphingolipid pathway. The details of the sphingolipid responses to heat stress are important, because they guide some of the longer-term alterations in gene expression, with which the cells adapt to the increased temperature. The analysis indicates that all enzyme activities in the system are affected and that the shapes of the time trends in activities depend on the fatty-acyl CoA chain lengths of the different ceramide species in the system.

Quantifying the removal of red blood cells in Macaca mulatta during a Plasmodium coatneyi infection.

BACKGROUND: Malaria is the most deadly parasitic disease in humans globally, and the long-time coexistence with malaria has left indelible marks in the human genome that are the causes of a variety of genetic disorders. Although anaemia is a common clinical complication of malaria, the root causes and mechanisms involved in the pathogenesis of malarial anaemia are unclear and difficult to study in humans. Non-human primate (NHP) model systems enable the mechanistic study and quantification of underlying causative factors of malarial anaemia, and particularly the onset of severe anaemia. METHODS: Data were obtained in the course of Plasmodium coatneyi infections of malaria-naïve and semi-immune rhesus macaques (Macaca mulatta), whose red blood cells (RBCs) were labelled in situ with biotin at the time the infections were initiated. The data were used for a survival analysis that permitted, for the first time, an accurate estimation of the lifespan of erythrocytes in macaques. The data furthermore formed the basis for the development and parameterization of a recursive dynamic model of erythrocyte turnover, which was used for the quantification of RBC production and removal in each macaque. RESULTS: The computational analysis demonstrated that the lifespan of erythrocytes in macaques is 98 ± 21 days. The model also unambiguously showed that death due to senescence and parasitaemia is not sufficient to account for the extent of infection-induced anaemia. Specifically, the model permits, for the first time, the quantification of the different causes of RBC death, namely, normal senescence, age-independent random loss, parasitization, and bystander effects in uninfected cells. Such a dissection of the overall RBC removal process is hardly possible with experimental means alone. In the infected malaria-naïve macaques, death of erythrocytes by normal physiological senescence processes accounts for 20 % and parasitization for only 4 %, whereas bystander effects are associated with an astonishing 76 % of total RBC losses. Model-based comparisons of alternative mechanisms involved in the bystander effect revealed that most of the losses are likely due to a process of removing uninfected RBCs of all age classes and only minimally due to an increased rate of senescence of the uninfected RBCs. CONCLUSIONS: A new malaria blood-stage model was developed for the analysis of data characterizing P. coatneyi infections of M. mulatta. The model used a discrete and recursive framework with age-structure that allowed the quantification of the most significant pathophysiological processes of RBC removal. The computational results revealed that the malarial anaemia caused by this parasite is mostly due to a loss of uninfected RBCs by an age-independent process. The biological identity and complete mechanism of this process is not fully understood and requires further investigation.

Carbon flux analysis by 13C nuclear magnetic resonance to determine the effect of CO2 on anaerobic succinate production by Corynebacterium glutamicum.

Wild-type Corynebacterium glutamicum produces a mixture of lactic, succinic, and acetic acids from glucose under oxygen deprivation. We investigated the effect of CO2 on the production of organic acids in a two-stage process: cells were grown aerobically in glucose, and subsequently, organic acid production by nongrowing cells was studied under anaerobic conditions. The presence of CO2 caused up to a 3-fold increase in the succinate yield (1 mol per mol of glucose) and about 2-fold increase in acetate, both at the expense of l-lactate production; moreover, dihydroxyacetone formation was abolished. The redistribution of carbon fluxes in response to CO2 was estimated by using (13)C-labeled glucose and (13)C nuclear magnetic resonance (NMR) analysis of the labeling patterns in end products. The flux analysis showed that 97% of succinate was produced via the reductive part of the tricarboxylic acid cycle, with the low activity of the oxidative branch being sufficient to provide the reducing equivalents needed for the redox balance. The flux via the pentose phosphate pathway was low (~5%) regardless of the presence or absence of CO2. Moreover, there was significant channeling of carbon to storage compounds (glycogen and trehalose) and concomitant catabolism of these reserves. The intracellular and extracellular pools of lactate and succinate were measured by in vivo NMR, and the stoichiometry (H(+):organic acid) of the respective exporters was calculated. This study shows that it is feasible to take advantage of natural cellular regulation mechanisms to obtain high yields of succinate with C. glutamicum without genetic manipulation.

Coordination of rapid sphingolipid responses to heat stress in yeast.

The regulatory roles of sphingolipids in diverse cell functions have been characterized extensively. However, the dynamics and interactions among the different sphingolipid species are difficult to assess, because de novo biosynthesis, metabolic inter-conversions, and the retrieval of sphingolipids from membranes form a complex, highly regulated pathway system. Here we analyze the heat stress response of this system in the yeast Saccharomyces cerevisiae and demonstrate how the cell dynamically adjusts its enzyme profile so that it is appropriate for operation under stress conditions before changes in gene expression become effective. The analysis uses metabolic time series data, a complex mathematical model, and a custom-tailored optimization strategy. The results demonstrate that all enzyme activities rapidly increase in an immediate response to the elevated temperature. After just a few minutes, different functional clusters of enzymes follow distinct activity patterns. Interestingly, starting after about six minutes, both de novo biosynthesis and all exit routes from central sphingolipid metabolism become blocked, and the remaining metabolic activity consists entirely of an internal redistribution among different sphingoid base and ceramide pools. After about 30 minutes, heat stress is still in effect and the enzyme activity profile is still significantly changed. Importantly, however, the metabolites have regained concentrations that are essentially the same as those under optimal conditions.

Control of Medical Digital Twins with Artificial Neural Networks.

The objective of personalized medicine is to tailor interventions to an individual patient's unique characteristics. A key technology for this purpose involves medical digital twins, computational models of human biology that can be personalized and dynamically updated to incorporate patient-specific data collected over time. Certain aspects of human biology, such as the immune system, are not easily captured with physics-based models, such as differential equations. Instead, they are often multi-scale, stochastic, and hybrid. This poses a challenge to existing model-based control and optimization approaches that cannot be readily applied to such models. Recent advances in automatic differentiation and neural-network control methods hold promise in addressing complex control problems. However, the application of these approaches to biomedical systems is still in its early stages. This work introduces dynamics-informed neural-network controllers as an alternative approach to control of medical digital twins. As a first use case for this method, the focus is on agent-based models, a versatile and increasingly common modeling platform in biomedicine. The effectiveness of the proposed neural-network control method is illustrated and benchmarked against other methods with two widely-used agent-based model types. The relevance of the method introduced here extends beyond medical digital twins to other complex dynamical systems.

Control of Medical Digital Twins with Artificial Neural Networks.

The objective of personalized medicine is to tailor interventions to an individual patient's unique characteristics. A key technology for this purpose involves medical digital twins, computational models of human biology that can be personalized and dynamically updated to incorporate patient-specific data collected over time. Certain aspects of human biology, such as the immune system, are not easily captured with physics-based models, such as differential equations. Instead, they are often multi-scale, stochastic, and hybrid. This poses a challenge to existing model-based control and optimization approaches that cannot be readily applied to such models. Recent advances in automatic differentiation and neural-network control methods hold promise in addressing complex control problems. However, the application of these approaches to biomedical systems is still in its early stages. This work introduces dynamics-informed neural-network controllers as an alternative approach to control of medical digital twins. As a first use case for this method, the focus is on agent-based models, a versatile and increasingly common modeling platform in biomedicine. The effectiveness of the proposed neural-network control method is illustrated and benchmarked against other methods with two widely-used agent-based model types. The relevance of the method introduced here extends beyond medical digital twins to other complex dynamical systems.

Surrogate modeling and control of medical digital twins.

The vision of personalized medicine is to identify interventions that maintain or restore a person's health based on their individual biology. Medical digital twins, computational models that integrate a wide range of health-related data about a person and can be dynamically updated, are a key technology that can help guide medical decisions. Such medical digital twin models can be high-dimensional, multi-scale, and stochastic. To be practical for healthcare applications, they often need to be simplified into low-dimensional surrogate models that can be used for optimal design of interventions. This paper introduces surrogate modeling algorithms for the purpose of optimal control applications. As a use case, we focus on agent-based models (ABMs), a common model type in biomedicine for which there are no readily available optimal control algorithms. By deriving surrogate models that are based on systems of ordinary differential equations, we show how optimal control methods can be employed to compute effective interventions, which can then be lifted back to a given ABM. The relevance of the methods introduced here extends beyond medical digital twins to other complex dynamical systems.

Forum on immune digital twins: a meeting report.

Medical digital twins are computational models of human biology relevant to a given medical condition, which are tailored to an individual patient, thereby predicting the course of disease and individualized treatments, an important goal of personalized medicine. The immune system, which has a central role in many diseases, is highly heterogeneous between individuals, and thus poses a major challenge for this technology. In February 2023, an international group of experts convened for two days to discuss these challenges related to immune digital twins. The group consisted of clinicians, immunologists, biologists, and mathematical modelers, representative of the interdisciplinary nature of medical digital twin development. A video recording of the entire event is available. This paper presents a synopsis of the discussions, brief descriptions of ongoing digital twin projects at different stages of progress. It also proposes a 5-year action plan for further developing this technology. The main recommendations are to identify and pursue a small number of promising use cases, to develop stimulation-specific assays of immune function in a clinical setting, and to develop a database of existing computational immune models, as well as advanced modeling technology and infrastructure.

Toward mechanistic medical digital twins: some use cases in immunology.

A fundamental challenge for personalized medicine is to capture enough of the complexity of an individual patient to determine an optimal way to keep them healthy or restore their health. This will require personalized computational models of sufficient resolution and with enough mechanistic information to provide actionable information to the clinician. Such personalized models are increasingly referred to as medical digital twins. Digital twin technology for health applications is still in its infancy, and extensive research and development is required. This article focuses on several projects in different stages of development that can lead to specific-and practical-medical digital twins or digital twin modeling platforms. It emerged from a two-day forum on problems related to medical digital twins, particularly those involving an immune system component. Open access video recordings of the forum discussions are available.

23Na multiple quantum filtered NMR characterisation of Na+ binding and dynamics in animal cells: a comparative study and effect of Na+/Li + competition.

Double quantum and triple quantum filtered (23)Na nuclear magnetic resonance techniques were used to characterise in detail the isotropic and anisotropic binding and dynamics of intra- and extracellular Na(+) in different cellular systems, in the absence and presence of Li(+). The kinetics of Li(+) influx by different cell types was evaluated. At steady state, astrocytes accumulated more Li(+) than red blood cells (RBCs), while a higher intracellular Li(+) concentration was found in chromaffin than in SH-SY5Y cells. Anisotropic and isotropic motions were detected for extracellular Na(+) in all cellular systems studied. Isotropic intracellular Na(+) motions were observed in all types of cells, while anisotropic Na(+) motions in the intracellular compartment were only detected in RBCs. (23)Na triple quantum signal efficiency for intracellular Na(+) was SH-SY5Y > chromaffin > RBCs, while the reverse order was observed for the extracellular ions. (23)Na double quantum signal efficiency for intracellular Na(+) was non-zero only in RBCs, and for extracellular Na(+) the order RBCs > chromaffin > SH-SY5Y cells was observed. Li(+) loading generally decreased intracellular Na(+) isotropic movements in the cells, except for astrocytes incubated with a low Li(+) concentration and increased anisotropic intracellular Na(+) movements in RBCs. Li(+) effects on the extracellular signals were more complex, reflecting Li(+)/Na(+) competition for isotropic and anisotropic binding sites at the extracellular surface of cell membranes and also at the surface of the gel used for cell immobilisation. These results are relevant and contribute to the interpretation of the in vivo pharmacokinetics and sites of Li(+) action.

Computational Modeling of Macrophage Iron Sequestration during Host Defense against Aspergillus.

Iron is essential to the virulence of Aspergillus species, and restricting iron availability is a critical mechanism of antimicrobial host defense. Macrophages recruited to the site of infection are at the crux of this process, employing multiple intersecting mechanisms to orchestrate iron sequestration from pathogens. To gain an integrated understanding of how this is achieved in aspergillosis, we generated a transcriptomic time series of the response of human monocyte-derived macrophages to Aspergillus and used this and the available literature to construct a mechanistic computational model of iron handling of macrophages during this infection. We found an overwhelming macrophage response beginning 2 to 4 h after exposure to the fungus, which included upregulated transcription of iron import proteins transferrin receptor-1, divalent metal transporter-1, and ZIP family transporters, and downregulated transcription of the iron exporter ferroportin. The computational model, based on a discrete dynamical systems framework, consisted of 21 3-state nodes, and was validated with additional experimental data that were not used in model generation. The model accurately captures the steady state and the trajectories of most of the quantitatively measured nodes. In the experimental data, we surprisingly found that transferrin receptor-1 upregulation preceded the induction of inflammatory cytokines, a feature that deviated from model predictions. Model simulations suggested that direct induction of transferrin receptor-1 (TfR1) after fungal recognition, independent of the iron regulatory protein-labile iron pool (IRP-LIP) system, explains this finding. We anticipate that this model will contribute to a quantitative understanding of iron regulation as a fundamental host defense mechanism during aspergillosis. IMPORTANCE Invasive pulmonary aspergillosis is a major cause of death among immunosuppressed individuals despite the best available therapy. Depriving the pathogen of iron is an essential component of host defense in this infection, but the mechanisms by which the host achieves this are complex. To understand how recruited macrophages mediate iron deprivation during the infection, we developed and validated a mechanistic computational model that integrates the available information in the field. The insights provided by this approach can help in designing iron modulation therapies as anti-fungal treatments.

Plasmodium knowlesi Cytoadhesion Involves SICA Variant Proteins.

Plasmodium knowlesi poses a health threat throughout Southeast Asian communities and currently causes most cases of malaria in Malaysia. This zoonotic parasite species has been studied in Macaca mulatta (rhesus monkeys) as a model for severe malarial infections, chronicity, and antigenic variation. The phenomenon of Plasmodium antigenic variation was first recognized during rhesus monkey infections. Plasmodium-encoded variant proteins were first discovered in this species and found to be expressed at the surface of infected erythrocytes, and then named the Schizont-Infected Cell Agglutination (SICA) antigens. SICA expression was shown to be spleen dependent, as SICA expression is lost after P. knowlesi is passaged in splenectomized rhesus. Here we present data from longitudinal P. knowlesi infections in rhesus with the most comprehensive analysis to date of clinical parameters and infected red blood cell sequestration in the vasculature of tissues from 22 organs. Based on the histopathological analysis of 22 tissue types from 11 rhesus monkeys, we show a comparative distribution of parasitized erythrocytes and the degree of margination of the infected erythrocytes with the endothelium. Interestingly, there was a significantly higher burden of parasites in the gastrointestinal tissues, and extensive margination of the parasites along the endothelium, which may help explain gastrointestinal symptoms frequently reported by patients with P. knowlesi malarial infections. Moreover, this margination was not observed in splenectomized rhesus that were infected with parasites not expressing the SICA proteins. This work provides data that directly supports the view that a subpopulation of P. knowlesi parasites cytoadheres and sequesters, likely via SICA variant antigens acting as ligands. This process is akin to the cytoadhesive function of the related variant antigen proteins, namely Erythrocyte Membrane Protein-1, expressed by Plasmodium falciparum.

MaHPIC malaria systems biology data from Plasmodium cynomolgi sporozoite longitudinal infections in macaques.

Plasmodium cynomolgi causes zoonotic malarial infections in Southeast Asia and this parasite species is important as a model for Plasmodium vivax and Plasmodium ovale. Each of these species produces hypnozoites in the liver, which can cause relapsing infections in the blood. Here we present methods and data generated from iterative longitudinal systems biology infection experiments designed and performed by the Malaria Host-Pathogen Interaction Center (MaHPIC) to delve deeper into the biology, pathogenesis, and immune responses of P. cynomolgi in the Macaca mulatta host. Infections were initiated by sporozoite inoculation. Blood and bone marrow samples were collected at defined timepoints for biological and computational experiments and integrative analyses revolving around primary illness, relapse illness, and subsequent disease and immune response patterns. Parasitological, clinical, haematological, immune response, and -omic datasets (transcriptomics, proteomics, metabolomics, and lipidomics) including metadata and computational results have been deposited in public repositories. The scope and depth of these datasets are unprecedented in studies of malaria, and they are projected to be a F.A.I.R., reliable data resource for decades.

Dramatic transcriptomic differences in Macaca mulatta and Macaca fascicularis with Plasmodium knowlesi infections.

Plasmodium knowlesi, a model malaria parasite, is responsible for a significant portion of zoonotic malaria cases in Southeast Asia and must be controlled to avoid disease severity and fatalities. However, little is known about the host-parasite interactions and molecular mechanisms in play during the course of P. knowlesi malaria infections, which also may be relevant across Plasmodium species. Here we contrast P. knowlesi sporozoite-initiated infections in Macaca mulatta and Macaca fascicularis using whole blood RNA-sequencing and transcriptomic analysis. These macaque hosts are evolutionarily close, yet malaria-naïve M. mulatta will succumb to blood-stage infection without treatment, whereas malaria-naïve M. fascicularis controls parasitemia without treatment. This comparative analysis reveals transcriptomic differences as early as the liver phase of infection, in the form of signaling pathways that are activated in M. fascicularis, but not M. mulatta. Additionally, while most immune responses are initially similar during the acute stage of the blood infection, significant differences arise subsequently. The observed differences point to prolonged inflammation and anti-inflammatory effects of IL10 in M. mulatta, while M. fascicularis undergoes a transcriptional makeover towards cell proliferation, consistent with its recovery. Together, these findings suggest that timely detection of P. knowlesi in M. fascicularis, coupled with control of inflammation while initiating the replenishment of key cell populations, helps contain the infection. Overall, this study points to specific genes and pathways that could be investigated as a basis for new drug targets that support recovery from acute malaria.

Characterization of the individual glucose uptake systems of Lactococcus lactis: mannose-PTS, cellobiose-PTS and the novel GlcU permease.

According to previous reports, Lactococcus lactis imports glucose via two distinct phosphoenolpyruvate:phosphotransferase systems (mannose-PTS and cellobiose-PTS) and one or more unknown non-PTS permease(s). GlcU was identified as the sole non-PTS permease involved in the transport of glucose. Additionally, the biochemical properties of PTS(Man), PTS(Cel) and GlcU were characterized in double knockout mutants with glucose uptake restricted to a single system. Transport susceptibility to protonophores indicated that glucose uptake via GlcU is proton-motive force dependent. Competition assays revealed a high specificity of GlcU for glucose. Furthermore, the permease has low affinity for glucose and displays strong preference for the beta-anomer as shown by the profiles of consumption of the two glucose anomers studied by (13)C-NMR. Similar kinetic properties were found for PTS(Cel), while PTS(Man) is a high-affinity system recognizing equally well the two anomeric forms of glucose. Transcripts of the genes encoding the three transporters are present simultaneously in the parent strain NZ9000 as shown by reverse transcription-PCR. Investigation of the distribution of GlcU homologues among bacteria showed that these proteins are restricted to the low-GC Gram-positive Firmicutes. This work completes the identification of the glucose transport systems in L. lactis MG1363.