A spiral scaffold underlies cytoadherent knobs in Plasmodium falciparum-infected erythrocytes

Much of the virulence of Plasmodium falciparum malaria is caused by cytoadherence of infected erythrocytes, which promotes parasite survival by preventing clearance in the spleen. Adherence is mediated by membrane protrusions known as knobs, whose formation depends on the parasite-derived, knob-associated histidine-rich protein (KAHRP). Knobs are required for cytoadherence under flow conditions, and they contain both KAHRP and the parasite-derived erythrocyte membrane protein PfEMP1. Using electron tomography, we have examined the three-dimensional structure of knobs in detergent-insoluble skeletons of P. falciparum 3D7 schizonts. We describe a highly organised knob skeleton composed of a spiral structure coated by an electron dense layer underlying the knob membrane. This knob skeleton is connected by multiple links to the erythrocyte cytoskeleton. We used immuno-electron microscopy to locate KAHRP in these structures. The arrangement of membrane proteins in the knobs, visualised by high resolution freeze fracture scanning electron microscopy, is distinct from that in the surrounding erythrocyte membrane, with a structure at the apex that likely represents the adhesion site. Thus, erythrocyte knobs in P. falciparum infection contain a highly organised skeleton structure underlying a specialised region of membrane. We propose that the spiral and dense coat organise the cytoadherence structures in the knob, and anchor them into the erythrocyte cytoskeleton. The high density of knobs and their extensive mechanical linkage suggest an explanation for the rigidification of the cytoskeleton in infected cells, and for the transmission to the cytoskeleton of shear forces experienced by adhering cells

The landscape of inherited and de novo copy number variants in a plasmodium falciparum genetic cross

<p>Abstract</p> <p>Background</p> <p>Copy number is a major source of genome variation with important evolutionary implications. Consequently, it is essential to determine copy number variant (CNV) behavior, distributions and frequencies across genomes to understand their origins in both evolutionary and generational time frames. We use comparative genomic hybridization (CGH) microarray and the resolution provided by a segregating population of cloned progeny lines of the malaria parasite, <it>Plasmodium falciparum</it>, to identify and analyze the inheritance of 170 genome-wide CNVs.</p> <p>Results</p> <p>We describe CNVs in progeny clones derived from both Mendelian (i.e. inherited) and non-Mendelian mechanisms. Forty-five CNVs were present in the parent lines and segregated in the progeny population. Furthermore, extensive variation that did not conform to strict Mendelian inheritance patterns was observed. 124 CNVs were called in one or more progeny but in neither parent: we observed CNVs in more than one progeny clone that were not identified in either parent, located more frequently in the telomeric-subtelomeric regions of chromosomes and singleton <it>de novo </it>CNVs distributed evenly throughout the genome. Linkage analysis of CNVs revealed dynamic copy number fluctuations and suggested mechanisms that could have generated them. Five of 12 previously identified expression quantitative trait loci (eQTL) hotspots coincide with CNVs, demonstrating the potential for broad influence of CNV on the transcriptional program and phenotypic variation.</p> <p>Conclusions</p> <p>CNVs are a significant source of segregating and <it>de novo </it>genome variation involving hundreds of genes. Examination of progeny genome segments provides a framework to assess the extent and possible origins of CNVs. This segregating genetic system reveals the breadth, distribution and dynamics of CNVs in a surprisingly plastic parasite genome, providing a new perspective on the sources of diversity in parasite populations.</p

Utility of a Proteomic Surrogate for Cardiovascular Outcomes to Predict and Monitor COVID-19 Induced Myocarditis

Introduction: Poorer cardiovascular (CV) health is a risk factor for adverse outcomes from COVID-19, and a proportion of COVID-19 survivors exhibit CV dysfunction including myocardial inflammation after initial recovery. However, biomarker-driven predictors of CV outcomes in COVID-19 are lacking. We recently demonstrated that a proteomic surrogate of CV outcomes (MI, stroke, hospitalization for heart failure, or death within 4 years) is highly predictive of acute COVID-19 severity. The current study expands on this to determine if a proteomic CV risk test associates with myocarditis likelihood in COVID-19 survivors. Methods: The SomaScan® platform was used for plasma proteomic phenotyping in CISCO-19 trial participants (n=154, mean age 55y, 43% Female) at hospital discharge following COVID-19, and at follow-up within 6-months later when comprehensive CV assessment including cardiac MRI for myocarditis adjudication was performed. N=20 (13%) were classified as very likely to have myocarditis at follow-up. We used linear mixed models to test proteomic CV scores at both visits against myocarditis likelihood across COVID-19 recovery and compared the predictive performance of the CV risk score to discriminate myocarditis likelihood to performance of Troponin I (TnI) and NT-proBNP. Results: A 36% relative CV risk reduction was observed during COVID-19 recovery. CV risk scores were significantly higher among those very likely myocarditis positive at follow up (p &lt; 0.05 for effects) (Figure 1). Additionally, the performance of the CV risk test in classifying myocarditis likelihood at follow-up was superior to that of TnI or NT-proBNP (AUC=0.64 vs 0.52 and 0.51, respectively). Conclusions: A proteomic predictor of CV risk has potential to support cardiac screening and monitoring CV health across the course of COVID-19 recovery, with diagnostic specificity superior to that of specific single cardiac biomarkers. Future studies with this approach seem warranted

Prediction of Cardiometabolic Health Through Changes in Plasma Proteins With Intentional Weight Loss in the DiRECT and DIADEM-I Randomized Clinical Trials of Type 2 Diabetes Remission

Objective: To determine to what extent changes in plasma proteins, previously predictive of cardiometabolic outcomes, predict changes in two diabetes remission trials.Research Design and Methods: SomaSignal® predictive tests (each derived from ~5000 plasma proteins measurements using aptamer-based proteomics assay) were assessed in baseline and 1-year samples in trials (DiRECT n=118, DIADEM-I n=66) and control (DiRECT n=144, DIADEM-I n=76) participants. Results: Mean weight losses in DiRECT (UK) and DIADEM-I (Qatar) were 10.2 (SD 7.4) kg and 12.1 (SD 9.5) kg, respectively, versus 1.0 (3.7) kg and 4.0 (SD 5.4) kg in control groups. Cardiometabolic SomaSignal tests improved significantly (Bonferroni-adjusted p10kg predicted significant reductions in CV risk of -19.1% (CI -33.4 to -4.91) in DiRECT and -33.4% (CI -57.3, -9.6) in DIADEM-I. DIADEM-I also demonstrated rapid emergence of metabolic improvements at 3 months. Conclusion: Intentional weight loss in recent onset type 2 diabetes rapidly induces changes in protein-based risk models consistent with widespread cardiometabolic improvements, including cardiorespiratory fitness. Protein changes with larger (>10kg) weight loss also predicted lower cardiovascular risk, providing a positive outlook for relevant ongoing trials.</p

Abstract 13902: Using a Proteomics-Based Cardiovascular Risk Test to Identify Systemic Changes in a Clinical Trial of Nonalcoholic Fatty Liver Disease

Background: Nonalcoholic steatohepatitis (NASH) is associated with increased cardiovascular outcomes. Assessment of the impact of NASH therapy on cardiovascular risk is an important element of NASH drug development but is challenging particularly in early phase trials. Aptamer-based proteomic profiles (Somalogic®) in serum have been used to develop and validate a risk score as a surrogate for cardiovascular (CV) risk. Hypothesis: Improvement in NASH histology will result in improved proteomic cardiovascular risk scores. Methods: A post-hoc analysis of proteomic profiles of serum samples, using the Somalogic® platform, from the Pioglitazone vs. Vitamin E vs. Placebo for Treatment of Nonalcoholic Fatty Liver Disease (PIVENS) trial was conducted. PIVENS was a 96-week trial of nondiabetic participants with (NASH). We applied the proteomic CV risk scores to samples from baseline, on therapy and end of treatment visits (n=7) visits and received liver histology results at baseline and 96 weeks on N = 209 (84.6%) study participants. Generalized linear and mixed models were used to assess the association between CV risk score, treatment arm and change in liver biopsy results. Results: Baseline scores were similar across groups (mean 0.19, SD 0.14). There was no association between treatment arms and changes in scores during therapy and end of treatment. However, improvement in histological markers of activity (lobular inflammation and NAFLD activity score) and fibrosis were associated with improved cv risk scores (Figure) (p&lt; 0.05 for all). Conclusions: Improvement in hepatic inflammation, NAFLD activity score and fibrosis were associated with improved proteomic CV risk scores regardless of treatment provided. Additional prospective validation of these findings is warranted. Proteomic profiling can potentially be used to track changes in cardiovascular risk profile changes in response to therapy in the short term NASH treatment trials

A proteomic surrogate for cardiovascular outcomes that is sensitive to multiple mechanisms of change in risk

A reliable, individualized, and dynamic surrogate of cardiovascular risk, synoptic for key biologic mechanisms, could shorten the path for drug development, enhance drug cost-effectiveness and improve patient outcomes. We used highly multiplexed proteomics to address these objectives, measuring about 5000 proteins in each of 32,130 archived plasma samples from 22,849 participants in nine clinical studies. We used machine learning to derive a 27-protein model predicting 4-year likelihood of myocardial infarction, stroke, heart failure, or death. The 27 proteins encompassed 10 biologic systems, and 12 were associated with relevant causal genetic traits. We independently validated results in 11,609 participants. Compared to a clinical model, the ratio of observed events in quintile 5 to quintile 1 was 6.7 for proteins versus 2.9 for the clinical model, AUCs (95% CI) were 0.73 (0.72 to 0.74) versus 0.64 (0.62 to 0.65), c -statistics were 0.71 (0.69 to 0.72) versus 0.62 (0.60 to 0.63), and the net reclassification index was +0.43. Adding the clinical model to the proteins only improved discrimination metrics by 0.01 to 0.02. Event rates in four predefined protein risk categories were 5.6, 11.2, 20.0, and 43.4% within 4 years; median time to event was 1.71 years. Protein predictions were directionally concordant with changed outcomes. Adverse risks were predicted for aging, approaching an event, anthracycline chemotherapy, diabetes, smoking, rheumatoid arthritis, cancer history, cardiovascular disease, high systolic blood pressure, and lipids. Reduced risks were predicted for weight loss and exenatide. The 27-protein model has potential as a “universal” surrogate end point for cardiovascular risk

Plasma protein patterns as comprehensive indicators of health

Proteins are effector molecules that mediate the functions of genes1,2 and modulate comorbidities3,4,5,6,7,8,9,10, behaviors and drug treatments11. They represent an enormous potential resource for personalized, systemic and data-driven diagnosis, prevention, monitoring and treatment. However, the concept of using plasma proteins for individualized health assessment across many health conditions simultaneously has not been tested. Here, we show that plasma protein expression patterns strongly encode for multiple different health states, future disease risks and lifestyle behaviors. We developed and validated protein-phenotype models for 11 different health indicators: liver fat, kidney filtration, percentage body fat, visceral fat mass, lean body mass, cardiopulmonary fitness, physical activity, alcohol consumption, cigarette smoking, diabetes risk and primary cardiovascular event risk. The analyses were prospectively planned, documented and executed at scale on archived samples and clinical data, with a total of ~85 million protein measurements in 16,894 participants. Our proof-of-concept study demonstrates that protein expression patterns reliably encode for many different health issues, and that large-scale protein scanning12,13,14,15,16 coupled with machine learning is viable for the development and future simultaneous delivery of multiple measures of health. We anticipate that, with further validation and the addition of more protein-phenotype models, this approach could enable a single-source, individualized so-called liquid health check

Circulating proteins to predict COVID-19 severity.

Predicting COVID-19 severity is difficult, and the biological pathways involved are not fully understood. To approach this problem, we measured 4701 circulating human protein abundances in two independent cohorts totaling 986 individuals. We then trained prediction models including protein abundances and clinical risk factors to predict COVID-19 severity in 417 subjects and tested these models in a separate cohort of 569 individuals. For severe COVID-19, a baseline model including age and sex provided an area under the receiver operator curve (AUC) of 65% in the test cohort. Selecting 92 proteins from the 4701 unique protein abundances improved the AUC to 88% in the training cohort, which remained relatively stable in the testing cohort at 86%, suggesting good generalizability. Proteins selected from different COVID-19 severity were enriched for cytokine and cytokine receptors, but more than half of the enriched pathways were not immune-related. Taken together, these findings suggest that circulating proteins measured at early stages of disease progression are reasonably accurate predictors of COVID-19 severity. Further research is needed to understand how to incorporate protein measurement into clinical care