Secondary integrated analysis of multi-tissue transcriptomic responses to a combined lifestyle intervention in older adults from the GOTO nonrandomized trial.

Molecular effects of lifestyle interventions are typically studied in a single tissue. Here, we perform a secondary analysis on the sex-specific effects of the Growing Old TOgether trial (GOTO, trial registration number GOT NL3301 ( https://onderzoekmetmensen.nl/nl/trial/27183 ), NL-OMON27183 , primary outcomes have been previously reported in ref. 1), a moderate 13-week combined lifestyle intervention on the transcriptomes of postprandial blood, subcutaneous adipose tissue (SAT) and muscle tissue in healthy older adults, the overlap in effect between tissues and their relation to whole-body parameters of metabolic health. The GOTO intervention has virtually no effect on the postprandial blood transcriptome, while the SAT and muscle transcriptomes respond significantly. In SAT, pathways involved in HDL remodeling, O2/CO2 exchange and signaling are overrepresented, while in muscle, collagen and extracellular matrix pathways are significantly overexpressed. Additionally, we find that the effects of the SAT transcriptome closest associates with gains in metabolic health. Lastly, in males, we identify a shared variation between the transcriptomes of the three tissues. We conclude that the GOTO intervention has a significant effect on metabolic and muscle fibre pathways in the SAT and muscle transcriptome, respectively. Aligning the response in the three tissues revealed a blood transcriptome component which may act as an integrated health marker for metabolic intervention effects across tissues.

Lifestyle factors and metabolomic aging biomarkers: Meta-analysis of cross-sectional and longitudinal associations in three prospective cohorts.

Biological age uses biophysiological information to capture a person's age-related risk of adverse outcomes. MetaboAge and MetaboHealth are metabolomics-based biomarkers of biological age trained on chronological age and mortality risk, respectively. Lifestyle factors contribute to the extent chronological and biological age differ. The association of lifestyle factors with MetaboAge and MetaboHealth, potential sex differences in these associations, and MetaboAge's and MetaboHealth's sensitivity to lifestyle changes have not been studied yet. Linear regression analyses and mixed-effect models were used to examine the cross-sectional and longitudinal associations of scaled lifestyle factors with scaled MetaboAge and MetaboHealth in 24,332 middle-aged participants from the Doetinchem Cohort Study, Rotterdam Study, and UK Biobank. Random-effect meta-analyses were performed across cohorts. Repeated metabolomics measurements had a ten-year interval in the Doetinchem Cohort Study and a five-year interval in the UK Biobank. In the first study incorporating longitudinal information on MetaboAge and MetaboHealth, we demonstrate associations between current smoking, sleeping ≥8 hours/day, higher BMI, and larger waist circumference were associated with higher MetaboHealth, the latter two also with higher MetaboAge. Furthermore, adhering to the dietary and physical activity guidelines were inversely associated with MetaboHealth. Lastly, we observed sex differences in the associations between alcohol use and MetaboHealth.

Causal inference using observational intensive care unit data: a scoping review and recommendations for future practice.

This scoping review focuses on the essential role of models for causal inference in shaping actionable artificial intelligence (AI) designed to aid clinicians in decision-making. The objective was to identify and evaluate the reporting quality of studies introducing models for causal inference in intensive care units (ICUs), and to provide recommendations to improve the future landscape of research practices in this domain. To achieve this, we searched various databases including Embase, MEDLINE ALL, Web of Science Core Collection, Google Scholar, medRxiv, bioRxiv, arXiv, and the ACM Digital Library. Studies involving models for causal inference addressing time-varying treatments in the adult ICU were reviewed. Data extraction encompassed the study settings and methodologies applied. Furthermore, we assessed reporting quality of target trial components (i.e., eligibility criteria, treatment strategies, follow-up period, outcome, and analysis plan) and main causal assumptions (i.e., conditional exchangeability, positivity, and consistency). Among the 2184 titles screened, 79 studies met the inclusion criteria. The methodologies used were G methods (61%) and reinforcement learning methods (39%). Studies considered both static (51%) and dynamic treatment regimes (49%). Only 30 (38%) of the studies reported all five target trial components, and only seven (9%) studies mentioned all three causal assumptions. To achieve actionable AI in the ICU, we advocate careful consideration of the causal question of interest, describing this research question as a target trial emulation, usage of appropriate causal inference methods, and acknowledgement (and examination of potential violations of) the causal assumptions.

Dynamic prediction of mortality in COVID-19 patients in the intensive care unit: A retrospective multi-center cohort study.

Background: The COVID-19 pandemic continues to overwhelm intensive care units (ICUs) worldwide, and improved prediction of mortality among COVID-19 patients could assist decision making in the ICU setting. In this work, we report on the development and validation of a dynamic mortality model specifically for critically ill COVID-19 patients and discuss its potential utility in the ICU. Methods: We collected electronic medical record (EMR) data from 3222 ICU admissions with a COVID-19 infection from 25 different ICUs in the Netherlands. We extracted daily observations of each patient and fitted both a linear (logistic regression) and non-linear (random forest) model to predict mortality within 24 h from the moment of prediction. Isotonic regression was used to re-calibrate the predictions of the fitted models. We evaluated the models in a leave-one-ICU-out (LOIO) cross-validation procedure. Results: The logistic regression and random forest model yielded an area under the receiver operating characteristic curve of 0.87 [0.85; 0.88] and 0.86 [0.84; 0.88], respectively. The recalibrated model predictions showed a calibration intercept of -0.04 [-0.12; 0.04] and slope of 0.90 [0.85; 0.95] for logistic regression model and a calibration intercept of -0.19 [-0.27; -0.10] and slope of 0.89 [0.84; 0.94] for the random forest model. Discussion: We presented a model for dynamic mortality prediction, specifically for critically ill COVID-19 patients, which predicts near-term mortality rather than in-ICU mortality. The potential clinical utility of dynamic mortality models such as benchmarking, improving resource allocation and informing family members, as well as the development of models with more causal structure, should be topics for future research.

MiMIR: R-shiny application to infer risk factors and endpoints from Nightingale Health's 1H-NMR metabolomics data.

MOTIVATION: 1H-NMR metabolomics is rapidly becoming a standard resource in large epidemiological studies to acquire metabolic profiles in large numbers of samples in a relatively low-priced and standardized manner. Concomitantly, metabolomics-based models are increasingly developed that capture disease risk or clinical risk factors. These developments raise the need for user-friendly toolbox to inspect new 1H-NMR metabolomics data and project a wide array of previously established risk models. RESULTS: We present MiMIR (Metabolomics-based Models for Imputing Risk), a graphical user interface that provides an intuitive framework for ad hoc statistical analysis of Nightingale Health's 1H-NMR metabolomics data and allows for the projection and calibration of 24 pre-trained metabolomics-based models, without any pre-required programming knowledge. AVAILABILITY AND IMPLEMENTATION: The R-shiny package is available in CRAN or downloadable at https://github.com/DanieleBizzarri/MiMIR, together with an extensive user manual (also available as Supplementary Documents to the article). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

1H-NMR metabolomics-based surrogates to impute common clinical risk factors and endpoints.

BACKGROUND: Missing or incomplete phenotypic information can severely deteriorate the statistical power in epidemiological studies. High-throughput quantification of small-molecules in bio-samples, i.e. 'metabolomics', is steadily gaining popularity, as it is highly informative for various phenotypical characteristics. Here we aim to leverage metabolomics to impute missing data in clinical variables routinely assessed in large epidemiological and clinical studies. METHODS: To this end, we have employed ∼26,000 1H-NMR metabolomics samples from 28 Dutch cohorts collected within the BBMRI-NL consortium, to create 19 metabolomics-based predictors for clinical variables, including diabetes status (AUC5-Fold CV = 0·94) and lipid medication usage (AUC5-Fold CV = 0·90). FINDINGS: Subsequent application in independent cohorts confirmed that our metabolomics-based predictors can indeed be used to impute a wide array of missing clinical variables from a single metabolomics data resource. In addition, application highlighted the potential use of our predictors to explore the effects of totally unobserved confounders in omics association studies. Finally, we show that our predictors can be used to explore risk factor profiles contributing to mortality in older participants. INTERPRETATION: To conclude, we provide 1H-NMR metabolomics-based models to impute clinical variables routinely assessed in epidemiological studies and illustrate their merit in scenarios when phenotypic variables are partially incomplete or totally unobserved. FUNDING: BBMRI-NL, X-omics, VOILA, Medical Delta and the Dutch Research Council (NWO-VENI).

A systematic review on consumer acceptance of alternative proteins: Pulses, algae, insects, plant-based meat alternatives, and cultured meat.

Consumers' dietary patterns have a significant impact on planetary and personal health. To address health and environmental challenges one of the many possible solutions is to substitute meat consumption with alternative protein sources. This systematic review identifies 91 articles with a focus on the drivers of consumer acceptance of five alternative proteins: pulses, algae, insects, plant-based alternative proteins, and cultured meat. This review demonstrates that acceptance of the alternative proteins included here is relatively low (compared to that of meat); acceptance of insects is lowest, followed by acceptance of cultured meat. Pulses and plant-based alternative proteins have the highest acceptance level. In general, the following drivers of acceptance consistently show to be relevant for the acceptance of various alternative proteins: motives of taste and health, familiarity, attitudes, food neophobia, disgust, and social norms. However, there are also differences in relevance between individuals and between alternative proteins. For example, for insects and other novel alternative proteins the drivers of familiarity and affective processes of food neophobia and disgust seem more relevant. As part of gaining full insight in relevant drivers of acceptance, the review also shows an overview of the intervention studies that were included in the 91 articles of the review, providing implications on how consumer acceptance can be increased. The focal areas of the intervention studies included here do not fully correspond with the current knowledge of drivers. To date, intervention studies have mainly focussed on conscious deliberations, whereas familiarity and affective factors have also been shown to be key drivers. The comprehensive overview of the most relevant factors for consumer acceptance of various categories of alternative proteins thus shows large consistencies across bodies of research. Variations can be found in the nuances showing different priorities of drivers for different proteins and different segments, showing the relevance of being context and person specific for future research.

A modular versatile chip carrier for high-throughput screening of cell-biomaterial interactions.

The field of biomaterials research is witnessing a steady rise in high-throughput screening approaches, comprising arrays of materials of different physico-chemical composition in a chip format. Even though the cell arrays provide many benefits in terms of throughput, they also bring new challenges. One of them is the establishment of robust homogeneous cell seeding techniques and strong control over cell culture, especially for long time periods. To meet these demands, seeding cells with low variation per tester area is required, in addition to robust cell culture parameters. In this study, we describe the development of a modular chip carrier which represents an important step in standardizing cell seeding and cell culture conditions in array formats. Our carrier allows flexible and controlled cell seeding and subsequent cell culture using dynamic perfusion. To demonstrate the application of our device, we successfully cultured and evaluated C2C12 premyoblast cell viability under dynamic conditions for a period of 5 days using an automated pipeline for image acquisition and analysis. In addition, using computational fluid dynamics, lactate and BMP-2 as model molecules, we estimated that there is good exchange of nutrients and metabolites with the flowing medium, whereas no cross-talk between adjacent TestUnits should be expected. Moreover, the shear stresses to the cells can be tailored uniformly over the entire chip area. Based on these findings, we believe our chip carrier may be a versatile tool for high-throughput cell experiments in biomaterials sciences.

High content imaging in the screening of biomaterial-induced MSC behavior.

Upon contact with a biomaterial, cells and surrounding tissues respond in a manner dictated by the physicochemical and mechanical properties of the material. Traditionally, cellular responses are monitored using invasive analytical methods that report the expression of genes or proteins. These analytical methods involve assessing commonly used markers for a predefined readout, masking the actual situation induced in the cells. Hence, a broader expression profile of the cellular response should be envisioned, which technically limits up scaling to higher throughput systems. However, it is increasingly recognized that morphometric readouts, obtained non-invasively, are related to gene expression patterns. Here, we introduced distinct surface roughness to three PLA surfaces, by exposure to oxygen plasma of different duration times. The response of mesenchymal stromal cells was compared to smooth untreated PLA surfaces without the addition of differentiation agents. Morphological and genome wide expression profiles revealed underlying cellular changes which was hidden for the commonly used gene markers for osteo-, chondro- and adipogenesis. Using 3 morphometric parameters, obtained by high content imaging, we were able to build a classifier and discriminate between oxygen plasma-induced modified sheets and non-modified PLA sheets where evaluating classical candidates missed this effect. This approach shows the feasibility to use noninvasive morphometric data in high-throughput systems to screen biomaterial surfaces indicating the underlying genetic biomaterial-induced changes.

Metabolite and reaction inference based on enzyme specificities.

MOTIVATION: Many enzymes are not absolutely specific, or even promiscuous: they can catalyze transformations of more compounds than the traditional ones as listed in, e.g. KEGG. This information is currently only available in databases, such as the BRENDA enzyme activity database. In this article, we propose to model enzyme aspecificity by predicting whether an input compound is likely to be transformed by a certain enzyme. Such a predictor has many applications, for example, to complete reconstructed metabolic networks, to aid in metabolic engineering or to help identify unknown peaks in mass spectra. RESULTS: We have developed a system for metabolite and reaction inference based on enzyme specificities (MaRIboES). It employs structural and stereochemistry similarity measures and molecular fingerprints to generalize enzymatic reactions based on data available in BRENDA. Leave-one-out cross-validation shows that 80% of known reactions are predicted well. Application to the yeast glycolytic and pentose phosphate pathways predicts a large number of known and new reactions, often leading to the formation of novel compounds, as well as a number of interesting bypasses and cross-links. AVAILABILITY: Matlab and C++ code is freely available at https://gforge.nbic.nl/projects/mariboes/

Stability from structure: metabolic networks are unlike other biological networks.

In recent work, attempts have been made to link the structure of biochemical networks to their complex dynamics. It was shown that structurally stable network motifs are enriched in such networks. In this work, we investigate to what extent these findings apply to metabolic networks. To this end, we extend a previously proposed method by changing the null model for determining motif enrichment, by using interaction types directly obtained from structural interaction matrices, by generating a distribution of partial derivatives of reaction rates and by simulating enzymatic regulation on metabolic networks. Our findings suggest that the conclusions drawn in previous work cannot be extended to metabolic networks, that is, structurally stable network motifs are not enriched in metabolic networks.

Combinatorial influence of environmental parameters on transcription factor activity.

MOTIVATION: Cells receive a wide variety of environmental signals, which are often processed combinatorially to generate specific genetic responses. Changes in transcript levels, as observed across different environmental conditions, can, to a large extent, be attributed to changes in the activity of transcription factors (TFs). However, in unraveling these transcription regulation networks, the actual environmental signals are often not incorporated into the model, simply because they have not been measured. The unquantified heterogeneity of the environmental parameters across microarray experiments frustrates regulatory network inference. RESULTS: We propose an inference algorithm that models the influence of environmental parameters on gene expression. The approach is based on a yeast microarray compendium of chemostat steady-state experiments. Chemostat cultivation enables the accurate control and measurement of many of the key cultivation parameters, such as nutrient concentrations, growth rate and temperature. The observed transcript levels are explained by inferring the activity of TFs in response to combinations of cultivation parameters. The interplay between activated enhancers and repressors that bind a gene promoter determine the possible up- or downregulation of the gene. The model is translated into a linear integer optimization problem. The resulting regulatory network identifies the combinatorial effects of environmental parameters on TF activity and gene expression. AVAILABILITY: The Matlab code is available from the authors upon request. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Creating gene set activity profiles with time-series expression data.

The use of predefined gene sets has become crucial in the interpretation of genomewide expression data. A limitation of the existing techniques that relate gene expression levels to gene sets is that they cannot readily be applied to time-course microarray data. The ability to attach statistical significance to the behaviour of biological processes over time would greatly contribute to understanding the complex regulatory mechanisms in the cell. We propose a statistical testing procedure based on the central limit theorem to assess the enrichment of a gene set. The technique is applied on time-course microarray data to generate gene-set specific 'activity profiles'.

Ectopic retroviral expression of LMO2, but not IL2Rgamma, blocks human T-cell development from CD34+ cells: implications for leukemogenesis in gene therapy.

The occurrence of leukemia in a gene therapy trial for SCID-X1 has highlighted insertional mutagenesis as an adverse effect. Although retroviral integration near the T-cell acute lymphoblastic leukemia (T-ALL) oncogene LIM-only protein 2 (LMO2) appears to be a common event, it is unclear why LMO2 was preferentially targeted. We show that of classical T-ALL oncogenes, LMO2 is most highly transcribed in CD34+ progenitor cells. Upon stimulation with growth factors typically used in gene therapy protocols transcription of LMO2, LYL1, TAL1 and TAN1 is most prominent. Therefore, these oncogenes may be susceptible to viral integration. The interleukin-2 receptor gamma chain (IL2Rgamma), which is mutated in SCID-X1, has been proposed as a cooperating oncogene to LMO2. However, we found that overexpressing IL2Rgamma had no effect on T-cell development. In contrast, retroviral overexpression of LMO2 in CD34+ cells caused severe abnormalities in T-cell development, but B-cell and myeloid development remained unaffected. Our data help explain why LMO2 was preferentially targeted over many of the other known T-ALL oncogenes. Furthermore, during T-cell development retrovirus-mediated expression of IL2Rgamma may not be directly oncogenic. Instead, restoration of normal IL7-receptor signaling may allow progression of T-cell development to stages where ectopic LMO2 expression causes aberrant thymocyte growth.

Least absolute regression network analysis of the murine osteoblast differentiation network.

MOTIVATION: We propose a reverse engineering scheme to discover genetic regulation from genome-wide transcription data that monitors the dynamic transcriptional response after a change in cellular environment. The interaction network is estimated by solving a linear model using simultaneous shrinking of the least absolute weights and the prediction error. RESULTS: The proposed scheme has been applied to the murine C2C12 cell-line stimulated to undergo osteoblast differentiation. Results show that our method discovers genetic interactions that display significant enrichment of co-citation in literature. More detailed study showed that the inferred network exhibits properties and hypotheses that are consistent with current biological knowledge.

Purity for clarity: the need for purification of tumor cells in DNA microarray studies.

It is now well established that gene expression profiling using DNA microarrays can provide novel information about various types of hematological malignancies, which may lead to identification of novel diagnostic markers. However, to successfully use microarrays for this purpose, the quality and reproducibility of the procedure need to be guaranteed. The quality of microarray analyses may be severely reduced, if variable frequencies of nontarget cells are present in the starting material. To systematically investigate the influence of different types of impurity, we determined gene expression profiles of leukemic samples containing different percentages of nonleukemic leukocytes. Furthermore, we used computer simulations to study the effect of different kinds of impurity as an alternative to conducting hundreds of microarray experiments on samples with various levels of purity. As expected, the percentage of erroneously identified genes rose with the increase of contaminating nontarget cells in the samples. The simulations demonstrated that a tumor load of less than 75% can lead to up to 25% erroneously identified genes. A tumor load of at least 90% leads to identification of at most 5% false-positive genes. We therefore propose that in order to draw well-founded conclusions, the percentage of target cells in microarray experiment samples should be at least 90%.

DNA microarrays for comparison of gene expression profiles between diagnosis and relapse in precursor-B acute lymphoblastic leukemia: choice of technique and purification influence the identification of potential diagnostic markers.

Microarrays for gene expression profiling are rapidly becoming important research tools for the identification of novel markers, for example, for novel classification of leukemias and lymphomas. Here, we review the considerations and infrastructure for microarray experiments. These considerations are illustrated via a microarray-based comparison of gene expression profiles of paired diagnosis-relapse samples from patients with precursor-B acute lymphoblastic leukemia (ALL), who relapsed during therapy or after completion of treatment. Initial experiments showed that several seemingly differentially expressed genes were actually derived from contaminating non-leukemic cells, particularly myeloid cells and T-lymphocytes. Therefore, we purified the ALL cells of the diagnosis and relapse samples if their frequency was lower than 95%. Furthermore, we observed in earlier studies that extra RNA amplification leads to skewing of particular gene transcripts. Sufficient (non-amplified) RNA of purified and paired diagnosis-relapse samples was obtained from only seven cases. The gene expression profiles were evaluated with Affymetrix U95A chips containing 12 600 human genes. These diagnosis-relapse comparisons revealed only a small number of genes (n=6) that differed significantly in expression: mostly signaling molecules and transcription factors involved in cell proliferation and cell survival were highly upregulated at relapse, but we did not observe any increase in drug-resistance markers. This finding fits with the observation that tumors with a high proliferation index have a poor prognosis. The genes that changed between diagnosis and relapse are currently not in use as diagnostic or disease progression markers, but represent potential new markers for such applications. Leukemia (2003) 17, 1324-1332. doi:10.1038/sj.leu.2402974

Genetic network modeling.

The inference of genetic interactions from measured expression data is one of the most challenging tasks of modern functional genomics. When successful, the learned network of regulatory interactions yields a wealth of useful information. An inferred genetic network contains information about the pathway to which a gene belongs and which genes it interacts with. Furthermore, it explains the function of the gene in terms of how it influences other genes and indicates which genes are pathway initiators and therefore potential drug targets. Obviously, such wealth comes at a price and that of genetic network modeling is that it is an extremely complex task. Therefore, it is necessary to develop sophisticated computational tools that are able to extract relevant information from a limited set of microarray measurements and integrate this with different information sources, to come up with reliable hypotheses of a genetic regulatory network. Thus far, a multitude of modeling approaches have been proposed for discovering genetic networks. However, it is unclear what the advantages and disadvantages of each of the different approaches are and how their results can be compared. In this review, genetic network models are put in a historical perspective that explains why certain models were introduced. Various modeling assumptions and their consequences are also highlighted. In addition, an overview of the principal differences and similarities between the approaches is given by considering the qualitative properties of the chosen models and their learning strategies.

A comparison of genetic network models.

With the completion of the sequencing of the human genome, the need for tools capable of unraveling the interaction and functionality of genes becomes extremely urgent. In answer to this quest, the advent of microarray technology provides the opportunity to perform large scale gene expression analyses. Recently, genetic networks were proposed as a possible methodology for modeling genetic interactions. Since then, a wide variety of different models have been introduced. However, it is, in general, unclear what the strengths and weaknesses of each of these approaches are and where these models overlap and differ. This paper compares different genetic modeling approaches that attempt to extract the gene regulation matrix from expression data. A taxonomy of continuous genetic network models is proposed and the following important characteristics are suggested and employed to compare the models: (1) inferential power; (2) predictive power; (3) robustness; (4) consistency; (5) stability and (6) computational cost. Where possible, synthetic time series data are employed to investigate some of these properties.