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Disentangling signaling gradients generated by equivalent sources

Noa Rappaport, Naama Barkai
2011 Journal of biological physics (Print)  
Rappaport · N.  ... 
doi:10.1007/s10867-011-9240-x pmid:23450187 pmcid:PMC3326141 fatcat:z4472tf4brf7hpwdgeyhigtefq

The ups and downs of biological timers

Noa Rappaport, Shay Winter, Naama Barkai
2005 Theoretical Biology and Medical Modelling  
The need to execute a sequence of events in an orderly and timely manner is central to many biological processes, including cell cycle progression and cell differentiation. For self-perpetuating systems, such as the cell cycle oscillator, delay times between events are defined by the network of interacting proteins that propagates the system. However, protein levels inside cells are subject to genetic and environmental fluctuations, raising the question of how reliable timing is maintained. We
more » ... ompared the robustness of different mechanisms for encoding delay times to fluctuations in protein expression levels. Gradual accumulation and gradual decay of a regulatory protein have an equivalent capacity for defining delay times. Yet, we find that the former is highly sensitive to fluctuations in gene dosage, while the latter can buffer such perturbations. In particular, a positive feedback where the degrading protein auto-enhances its own degradation may render delay times practically insensitive to gene dosage. While our understanding of biological timing mechanisms is still rudimentary, it is clear that there is an ample use of degradation as well as self-enhanced degradation in processes such as cell cycle and circadian clocks. We propose that degradation processes, and specifically self-enhanced degradation, will be preferred in processes where maintaining the robustness of timing is important.
doi:10.1186/1742-4682-2-22 pmid:15967029 pmcid:PMC1208956 fatcat:awp2qzkvebf5tnlha724xdwwgu

Blood metabolome signature predicts gut microbiome α-diversity in health and disease [article]

Tomasz Wilmanski, Noa Rappaport, John C. Earls, Andrew T. Magis, Ohad Manor, Jennifer Lovejoy, Gilbert S. Omenn, Leroy Hood, Sean Gibbons, Nathan D. Price
2019 bioRxiv   pre-print
Defining a 'healthy' gut microbiome has been a challenge in the absence of detailed information on both host health and microbiome composition. Here, we analyzed a multi-omics dataset from hundreds of individuals (discovery n=399, validation n=540) enrolled in a consumer scientific wellness program to identify robust associations between host physiology and gut microbiome structure. We attempted to predict gut microbiome α-diversity from nearly 1000 analytes measured from blood, including
more » ... al laboratory tests, proteomics and metabolomics. While a broad panel of 77 standard clinical laboratory tests and a set of 263 proteins from blood could not accurately predict gut microbial α-diversity, we found that 45% of the variance in microbial community diversity was explained by a subset of 40 blood metabolites, many of microbial origin. This relationship between the host metabolome and gut microbiome α-diversity was very robust, persisting across disease conditions and antibiotics use. Several of these novel metabolic biomarkers of gut microbial diversity were previously associated with host health (e.g. cardiovascular disease risk, diabetes, and kidney function). A subset of 11 metabolites classified participants with potentially problematic low α-diversity (ROC AUC=0.88, Precision-Recall AUC=0.76). Relationships between host metabolites and α-diversity remained consistent across most of the Body Mass Index (BMI) spectrum, but were modified in extreme obesity (class II/III, but not class I), suggesting a significant metabolic shift. Out-of-sample prediction accuracy of α-diversity from the 40 identified blood metabolites in a validation cohort, whose microbiome samples were analyzed by a different vendor, confirmed the robust correspondence between gut microbiome structure and host physiology. Collectively, our results reveal a strong coupling between the human blood metabolome and gut microbial diversity, with implications for human health.
doi:10.1101/561209 fatcat:dfsvzqse6ber3gnxnkazbou5ai

Long-term Exposures to Air Pollutants Affect FeNO in Children: A Longitudinal Study [article]

yue zhang, Sandrah Eckel, Kiros Berhane, Erika Garcia, Patrick Muchmore, Noa Ben-Ari Molshatzki, Edward Rappaport, William S.Linn, Rima Habre, Frank Gilliland
2021 medRxiv   pre-print
Fractional exhaled nitric oxide (FeNO) is a marker of airway inflammation shown to be responsive to short-term air pollution exposures; however, effects of long-term exposures are uncertain. Using longitudinal assessments of FeNO and air pollutant exposures, we aimed to determine whether FeNO is a marker for chronic effects of air pollution exposures after accounting for short-term exposures effects. FeNO was assessed up to six times 2004-2012 in 3607 schoolchildren from 12 communities in the
more » ... uthern California Children's Health Study. Within-community long-term ambient air pollution exposures (PM2.5, PM10, NO2, O3) were represented by differences between community-specific annual averages and the eight-year average spanning the study period. Linear mixed-effect models estimated within-participant associations of annual average air pollution with current FeNO, controlling for previous FeNO, prior seven-day average pollution, potential confounders, and community-level random intercepts. We considered effect modification by sex, ethnicity, asthma, and allergy at baseline. We found FeNO was positively associated with annual average air pollution, after accounting for short-term exposures. One standard deviation higher annual PM2.5 and NO2 exposures (PM2.5:2.0 μg/m3; NO2:2.7 ppb) were associated, respectively, with 4.6% (95%CI:2.3%-6.8%) and 6.5% (95%CI:4.1%-8.9%) higher FeNO. These associations were larger among females. We found little evidence supporting association with PM10 or O3. Annual average PM2.5 and NO2 levels were associated with FeNO in schoolchildren, adding new evidence that long-term exposure affects FeNO beyond the well-documented short-term effects. Longitudinal FeNO measurements may be useful as an early marker of chronic respiratory effects of long-term PM2.5 and NO2 exposures in children.
doi:10.1101/2021.03.01.21252712 fatcat:y7ymt5sqrzb4pjda5d5yffo22a

Genome-microbiome interplay provides insight into the determinants of the human blood metabolome [article]

Christian Diener, Chengzhen L Dai, Tomasz Wilmanski, Priyanka Baloni, Brett Smith, Noa Rappaport, Leroy Hood, Andrew T Magis, Sean M Gibbons
2022 bioRxiv   pre-print
Variation in the blood metabolome is intimately related to human health. Prior work has shown that host genetics and gut microbiome composition, combined, explain sizable, but orthogonal, components of the overall variance in blood metabolomic profiles. However, few details are known about the interplay between genetics and the microbiome in explaining variation on a metabolite-by-metabolite level. Here, we performed analyses of variance for each of the 945 blood metabolites that were robustly
more » ... etected across a cohort of 2,049 individuals, while controlling for a number of relevant covariates, like sex, age, and genetic ancestry. Over 60% of the detected blood metabolites were significantly associated with either host genetics or the gut microbiome, with more than half of these associations driven solely by the microbiome and around 30% under hybrid genetic-microbiome control. The variances explained by genetics and the microbiome for each metabolite were indeed largely additive, although subtle, but significant, non-additivity was detected. We found that interaction effects, where a metabolite-microbe association was specific to a particular genetic background, were quite common, albeit with modest effect sizes. The outputs of our integrated genetic-microbiome regression models provide novel biological insights into the processes governing the composition of the blood metabolome. For example, we found that unconjugated secondary bile acids were solely associated with the microbiome, while their conjugated forms were under strong host genetic control. Overall, our results reveal which components of the blood metabolome are under strong genetic control, which are more dependent on gut microbiome composition, and which are dependent upon both. This knowledge will help to guide targeted interventions designed to alter the composition of the blood metabolome.
doi:10.1101/2022.02.04.479172 fatcat:y4nv76o4pjgudlxnq7ofpphjuq

MalaCards: A Comprehensive Automatically-Mined Database of Human Diseases

Noa Rappaport, Michal Twik, Noam Nativ, Gil Stelzer, Iris Bahir, Tsippi Iny Stein, Marilyn Safran, Doron Lancet
2014 Current Protocols in Bioinformatics  
Information about the computation of the different types of scores is found in (Rappaport et al., 2013) and at http://www.malacards.org/pages/info/#scores.  ...  An earlier description of many aspects of MalaCards, including the various sections within a specific card, is available in Rappaport et al. (2013) and at http://www.malacards.org/pages/info.  ... 
doi:10.1002/0471250953.bi0124s47 pmid:25199789 fatcat:vowmssjxt5c5pnc56rviyj4ot4

Towards early risk biomarkers: serum metabolic signature in childhood predicts cardio-metabolic risk in adulthood [article]

Xiaowei Ojanen, Runtan Cheng, Timo Tormakangas, Na Wu, Noa Rappaport, Tomasz Wilmanski, Wei Yan, Nathan D Price, Sulin Cheng, Petri Wiklund
2019 medRxiv   pre-print
Cardiovascular diseases have their origin in childhood. Early biomarkers identifying individuals with increased risk for disease are needed to support early detection and to optimize prevention strategies. By applying machine learning approach on high throughput NMR-based metabolomics data, we identified metabolic predictors of cardiovascular risk in circulation in a cohort of 396 females, followed from childhood (mean age 11.2 years) to early adulthood (mean age 18.1 years). The identified
more » ... dhood metabolic signature included three circulating biomarkers robustly associating with increased cardiovascular risk in early adulthood (AUC = 0.641 to 0.802, all p<0.01). These associations were confirmed in two validation cohorts including middle-aged women, with similar effect estimates. We subsequently applied random intercept cross-lagged panel model analysis, which suggested causal relationship between metabolites and cardio-metabolic risk score from childhood to early adulthood. These results provide evidence for the utility of circulating metabolomics panel to identify children and adolescents at risk for cardiovascular disease, to whom preventive measures and follow-up could be indicated.
doi:10.1101/2019.12.11.19014308 fatcat:g4itqnp3wzhbpf3idwxdopyjki

Multiomic investigations of Body Mass Index reveal heterogeneous trajectories in response to a lifestyle intervention [article]

Kengo Watanabe, Tomasz Wilmanski, Christian Diener, Anat Zimmer, Briana Lincoln, Jennifer J. Hadlock, Jennifer C. Lovejoy, Andrew T. Magis, Leroy Hood, Nathan D. Price, Noa Rappaport
2022 medRxiv   pre-print
AbstractMultiomic profiling is useful in characterizing heterogeneity of both health and disease states. Obesity exerts profound metabolic perturbation in individuals and is a risk factor for multiple chronic diseases. Here, we report a global atlas of cross-sectional and longitudinal changes associated with Body Mass Index (BMI) across 1,100+ blood analytes, as well as their correspondence to host genome and fecal microbiome composition, from a cohort of 1,277 individuals enrolled in a
more » ... program. Machine learning-based models predicting BMI from blood multiomics captured heterogeneous states of both metabolic and gut microbiome health better than classically measured BMI, suggesting that multiomic data can provide deeper insight into host physiology. Moreover, longitudinal analyses identified variable trajectories of BMI in response to a lifestyle intervention, depending on the analyzed omics platform; metabolomics-based BMI decreased to a greater extent than actual BMI, while proteomics-based BMI exhibited greater resistance. Our analysis further elucidated blood analyte–analyte associations which were significantly modified by obesity and partially reversed in the metabolically obese population through the program. Altogether, our findings provide an atlas of the gradual blood perturbations accompanying obesity and serve as a valuable resource for robustly characterizing metabolic health and identifying actionable targets for obesity.
doi:10.1101/2022.01.20.22269601 fatcat:5wx2gw2hcjceln6pcrxvd5gc7q

Heterogeneity in statin responses explained by variation in the human gut microbiome [article]

Tomasz M Wilmanski, Sergey A Kornilov, Christian Diener, Matthew Conomos, Jennifer C Lovejoy, Paola Sebastiani, Eric S Orwoll, Leroy Hood, Nathan D Price, Noa Rappaport, Andrew T Magis, Sean M Gibbons
2021 medRxiv   pre-print
Statins remain one of the most prescribed medications worldwide. While effective in decreasing atherosclerotic cardiovascular disease risk, statin use is associated with several side effects for a subset of patients, including disrupted metabolic control and increased risk of type II diabetes. We investigated the potential role of the gut microbiome in modifying patient response to statin therapy. In a cohort of >1840 individuals, we find that the hydrolyzed substrate for
more » ... rate-CoA (HMG-CoA) reductase, HMG, may serve as a reliable marker for statin on-target effects. Through exploring gut microbiome associations between blood-derived measures of statin effectiveness and metabolic health parameters among statin users and non-users, we find that heterogeneity in statin response is associated with variation in the gut microbiome. A Bacteroides rich, α-diversity depleted, microbiome composition corresponds to the strongest statin on-target response, but also greatest disruption to glucose homeostasis, indicating lower treatment doses and/or complementary therapies may be beneficial in those individuals. Our findings suggest a potential path towards personalizing statin treatment through gut microbiome monitoring.
doi:10.1101/2021.12.02.21267193 fatcat:fhx5qkwyingplbfozhfxbudxfa

Rational confederation of genes and diseases: NGS interpretation via GeneCards, MalaCards and VarElect

Noa Rappaport, Simon Fishilevich, Ron Nudel, Michal Twik, Frida Belinky, Inbar Plaschkes, Tsippi Iny Stein, Dana Cohen, Danit Oz-Levi, Marilyn Safran, Doron Lancet
2017 BioMedical Engineering OnLine  
Stelzer et al. (2016) The GeneCards suite: from gene data mining to disease genome sequence analysis, current protocols in bioinformatics [7] MalaCards Affiliated database Human disease database Rappaport  ... 
doi:10.1186/s12938-017-0359-2 pmid:28830434 pmcid:PMC5568599 fatcat:nr5iblpqh5bn5glqnrgelrdehm

CELL-TYPE SPECIFIC MECHANISTIC AND DIRECTIONAL TRANSCRIPTIONAL REGULATORY NETWORKS IN ALZHEIMER'S DISEASE

Cory C. Funk, Paul Shannon, David Gibbs, Noa Rappaport, Mariet Allen, Minerva M. Carrasquillo, Nilufer Ertekin-Taner, Todd E. Golde, Ilya Shmulevich, Leroy Hood, Nathan D. Price
2019 Alzheimer's & Dementia  
Funk 1 , Paul Shannon 1 , David Gibbs 1 , Noa Rappaport 1 , Mariet Allen 2 , Minerva M. Carrasquillo 2 , Nilufer Ertekin-Taner 2 , Todd E. Golde 3 , Ilya Shmulevich 1 , Leroy Hood 4 , Nathan D.  ... 
doi:10.1016/j.jalz.2019.06.3764 fatcat:ygqxqj2fozdhrikm2e3dwch2cm

MalaCards: an integrated compendium for diseases and their annotation

Noa Rappaport, Noam Nativ, Gil Stelzer, Michal Twik, Yaron Guan-Golan, Tsippi Iny Stein, Iris Bahir, Frida Belinky, C. Paul Morrey, Marilyn Safran, Doron Lancet
2013 Database: The Journal of Biological Databases and Curation  
Citation details: Rappaport,N., Nativ,N., Stelzer,G., et al. MalaCards: an integrated compendium for diseases and their annotation.  ... 
doi:10.1093/database/bat018 pmid:23584832 pmcid:PMC3625956 fatcat:ju462m3cezgqtkai2jjrdvatuq

The geometry of clinical labs and wellness states from deeply phenotyped humans

Anat Zimmer, Yael Korem, Noa Rappaport, Tomasz Wilmanski, Priyanka Baloni, Kathleen Jade, Max Robinson, Andrew T. Magis, Jennifer Lovejoy, Sean M. Gibbons, Leroy Hood, Nathan D. Price
2021 Nature Communications  
AbstractLongitudinal multi-omics measurements are highly valuable in studying heterogeneity in health and disease phenotypes. For thousands of people, we have collected longitudinal multi-omics data. To analyze, interpret and visualize this extremely high-dimensional data, we use the Pareto Task Inference (ParTI) method. We find that the clinical labs data fall within a tetrahedron. We then use all other data types to characterize the four archetypes. We find that the tetrahedron comprises
more » ... wellness states, defining a wellness triangular plane, and one aberrant health state that captures aspects of commonality in movement away from wellness. We reveal the tradeoffs that shape the data and their hierarchy, and use longitudinal data to observe individual trajectories. We then demonstrate how the movement on the tetrahedron can be used for detecting unexpected trajectories, which might indicate transitions from health to disease and reveal abnormal conditions, even when all individual blood measurements are in the norm.
doi:10.1038/s41467-021-23849-8 pmid:34117230 fatcat:7a2sjmhb5bdh7eq2bvtgd4tn2a

Untargeted longitudinal analysis of a wellness cohort identifies markers of metastatic cancer years prior to diagnosis

Andrew T. Magis, Noa Rappaport, Matthew P. Conomos, Gilbert S. Omenn, Jennifer C. Lovejoy, Leroy Hood, Nathan D. Price
2020 Scientific Reports  
We analyzed 1196 proteins in longitudinal plasma samples from participants in a commercial wellness program, including samples collected pre-diagnosis from ten cancer patients and 69 controls. For three individuals ultimately diagnosed with metastatic breast, lung, or pancreatic cancer, CEACAM5 was a persistent longitudinal outlier as early as 26.5 months pre-diagnosis. CALCA, a biomarker for medullary thyroid cancer, was hypersecreted in metastatic pancreatic cancer at least 16.5 months
more » ... gnosis. ERBB2 levels spiked in metastatic breast cancer between 10.0 and 4.0 months pre-diagnosis. Our results support the value of deep phenotyping seemingly healthy individuals in prospectively inferring disease transitions.
doi:10.1038/s41598-020-73451-z pmid:33004987 fatcat:orznwfn3cjfz7a32v5y3tw6hki
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