Elastic net regularized regression for time-series analysis of plasma metabolome stability under sub-optimal freezing condition

Data-driven prediction models for forecasting multistep ahead profiles of mammalian cell culture toward bioprocess digital twins

Recently, the advancement in process analytical technology and artificial intelligence (AI) has enabled the generation of enormous culture data sets from biomanufacturing processes that produce various recombinant therapeutic proteins (RTPs), such as monoclonal antibodies (mAbs). Thus, now it is very important to exploit them for the enhanced reliability, efficiency, and consistency of the RTP-producing culture processes and for the reduced incipient or abrupt faults. It is achievable by AI-based data-driven models (DDMs), which allow us to correlate biological and process conditions and cell culture states. In this work, we provide practical guidelines for choosing the best combination of model elements to design and implement successful DDMs for given hypothetical in-line data sets during mAb-producing Chinese hamster ovary cell culture, as such enabling us to forecast dynamic behaviors of culture performance such as viable cell density, mAb titer as well as glucose, lactate and ammonia concentrations. To do so, we created DDMs that balance computational load with model accuracy and reliability by identifying the best combination of multistep ahead forecasting strategies, input features, and AI algorithms, which is potentially applicable to implementation of interactive DDM within bioprocess digital twins. We believe this systematic study can help bioprocess engineers start developing predictive DDMs with their own data sets and learn how their cell cultures behave in near future, thereby rendering proactive decision possible.

Cytomegalovirus infection disrupts the influence of short-chain fatty acid producers on Treg/Th17 balance

BACKGROUND

Both the gut microbiota and chronic viral infections have profound effects on host immunity, but interactions between these influences have been only superficially explored. Cytomegalovirus (CMV), for example, infects approximately 80% of people globally and drives significant changes in immune cells. Similarly, certain gut-resident bacteria affect T-cell development in mice and nonhuman primates. It is unknown if changes imposed by CMV on the intestinal microbiome contribute to immunologic effects of the infection.

RESULTS

We show that rhesus cytomegalovirus (RhCMV) infection is associated with specific differences in gut microbiota composition, including decreased abundance of Firmicutes, and that the extent of microbial change was associated with immunologic changes including the proliferation, differentiation, and cytokine production of CD8+ T cells. Furthermore, RhCMV infection disrupted the relationship between short-chain fatty acid producers and Treg/Th17 balance observed in seronegative animals, showing that some immunologic effects of CMV are due to disruption of previously existing host-microbe relationships.

CONCLUSIONS

Gut microbes have an important influence on health and disease. Diet is known to shape the microbiota, but the influence of concomitant chronic viral infections is unclear. We found that CMV influences gut microbiota composition to an extent that is correlated with immunologic changes in the host. Additionally, pre-existing correlations between immunophenotypes and gut microbes can be subverted by CMV infection. Immunologic effects of CMV infection on the host may therefore be mediated by two different mechanisms involving gut microbiota. Video Abstract.

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 (AUC_{5-Fold CV} = 0·94) and lipid medication usage (AUC_{5-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).

From bedside to bench-practical considerations to avoid pre-analytical pitfalls and assess sample quality for high-resolution metabolomics and lipidomics analyses of body fluids

The stability of lipids and other metabolites in human body fluids ranges from very stable over several days to very unstable within minutes after sample collection. Since the high-resolution analytics of metabolomics and lipidomics approaches comprise all these compounds, the handling of body fluid samples, and thus the pre-analytical phase, is of utmost importance to obtain valid profiling data. This phase consists of two parts, sample collection in the hospital (“bedside”) and sample processing in the laboratory (“bench”). For sample quality, the apparently simple steps in the hospital are much more critical than the “bench” side handling, where (bio)analytical chemists focus on highly standardized processing for high-resolution analysis under well-controlled conditions. This review discusses the most critical pre-analytical steps for sample quality from patient preparation; collection of body fluids (blood, urine, cerebrospinal fluid) to sample handling, transport, and storage in freezers; and subsequent thawing using current literature, as well as own investigations and practical experiences in the hospital. Furthermore, it provides guidance for (bio)analytical chemists to detect and prevent potential pre-analytical pitfalls at the “bedside,” and how to assess the quality of already collected body fluid samples. A knowledge base is provided allowing one to decide whether or not the sample quality is acceptable for its intended use in distinct profiling approaches and to select the most suitable samples for high-resolution metabolomics and lipidomics investigations.

Emerging computational tools and models for studying gut microbiota composition and function

The gut microbiota and its metabolites play critical roles in human health and disease. Advances in high-throughput sequencing, mass spectrometry, and other omics assay platforms have improved our ability to generate large volumes of data exploring the temporal variations in the compositions and functions of microbial communities. To elucidate mechanisms, methods and tools are needed that can rigorously model the dependencies within time-series data. Longitudinal data are often sparse and unevenly sampled, and nontrivial challenges remain in determining statistical significance, normalization across different data types, and model validation. In this review, we highlight recent developments in models and software tools for the analysis of time series microbiome and metabolome data, as well as integration of these data.

A multilevel analysis of financial institutions' systemic exposure from local and system-wide information

In the aftermath of the financial crisis of 2007–2009, the growing body of literature on financial networks has widely documented the predictive power of topological characteristics (e.g., degree centrality measures) to explain the systemic impact or systemic exposure of financial institutions. This study shows that considering alternative topological measures based on local sub-network environment improves our ability to identify systemic institutions. To provide empirical evidence, we apply a two-step procedure. First, we recover network communities (i.e., close-peer environment) on a spillover network of financial institutions. Second, we regress alternative measures of vulnerability (i.e. firm’s losses)on three levels of topological measures: the global level (i.e., firm topological characteristics computed over the whole system), local level (i.e., firm topological characteristics computed over the community to which it belongs), and aggregated level by averaging individual characteristics over the community. The sample includes 46 financial institutions (banks, broker-dealers, and insurance and real-estate companies) listed in the Standard & Poor’s 500 index. Our results confirm the informational content of topological metrics based on a close-peer environment. Such information is different from that embedded in traditional system-wide topological metrics and can help predict distress of financial institutions in times of crisis.

In Silico Model for Chemical-Induced Chromosomal Damages Elucidates Mode of Action and Irrelevant Positives

In silico tools to predict genotoxicity have become important for high-throughput screening of chemical substances. However, current in silico tools to evaluate chromosomal damage do not discriminate in vitro-specific positives that can be followed by in vivo tests. Herein, we establish an in silico model for chromosomal damages with the following approaches: (1) re-categorizing a previous data set into three groups (positives, negatives, and misleading positives) according to current reports that use weight-of-evidence approaches and expert judgments; (2) utilizing a generalized linear model (Elastic Net) that uses partial structures of chemicals (organic functional groups) as explanatory variables of the statistical model; and (3) interpreting mode of action in terms of chemical structures identified. The accuracy of our model was 85.6%, 80.3%, and 87.9% for positive, negative, and misleading positive predictions, respectively. Selected organic functional groups in the models for positive prediction were reported to induce genotoxicity via various modes of actions (e.g., DNA adduct formation), whereas those for misleading positives were not clearly related to genotoxicity (e.g., low pH, cytotoxicity induction). Therefore, the present model may contribute to high-throughput screening in material design or drug discovery to verify the relevance of estimated positives considering their mechanisms of action.

Lipidomic Profiling of the Epidermis in a Mouse Model of Dermatitis Reveals Sexual Dimorphism and Changes in Lipid Composition before the Onset of Clinical Disease

Atopic dermatitis (AD) is a multifactorial disease associated with alterations in lipid composition and organization in the epidermis. Multiple variants of AD exist with different outcomes in response to therapies. The evaluation of disease progression and response to treatment are observational assessments with poor inter-observer agreement highlighting the need for molecular markers. SHARPIN-deficient mice (Sharpin^cpdm) spontaneously develop chronic proliferative dermatitis with features similar to AD in humans. To study the changes in the epidermal lipid-content during disease progression, we tested 72 epidermis samples from three groups (5-, 7-, and 10-weeks old) of cpdm mice and their WT littermates. An agnostic mass-spectrometry strategy for biomarker discovery termed multiple-reaction monitoring (MRM)-profiling was used to detect and monitor 1,030 lipid ions present in the epidermis samples. In order to select the most relevant ions, we utilized a two-tiered filter/wrapper feature-selection strategy. Lipid categories were compressed, and an elastic-net classifier was used to rank and identify the most predictive lipid categories for sex, phenotype, and disease stages of cpdm mice. The model accurately classified the samples based on phospholipids, cholesteryl esters, acylcarnitines, and sphingolipids, demonstrating that disease progression cannot be defined by one single lipid or lipid category.

Association between newborn screening analyte profiles and infant mortality

Objective

To assess whether newborn screening analytes could be utilized beyond their traditional application to identify infants at high risk of mortality within the first 6 months of life.

Methods

We linked a province-wide newborn screening registry with health administrative databases to identify infant deaths within 6 months in a source population of live-born infants between 2010 and 2014. We used a nested case-control study design, in which all infant deaths between 7 days and 6 months of age were included as cases, and a random sample of infants from the source population were selected as controls and were matched to cases at a ratio of 10:1. We examined the association between mortality and screening analytes (acylcarnitines, amino acids, fetal-to-adult hemoglobin ratio, endocrine markers, and enzymes) using lasso regression to fit multivariable models.

Results

Among 350 infant deaths between 7 days and 6 months of age, and 3498 matched controls with complete data, our multivariable model demonstrated only modest ability to identify infant deaths (optimism-corrected c-statistic: 0.61, 95% confidence interval: 0.50–0.71).

Conclusions

We did not find newborn screening analytes to be strongly predictive of infant mortality between 7 days and 6 months of age in the general population of newborns. Future studies should investigate whether predictive modeling within more homogeneous cause-of-death categories could lead to improved predictive ability for infant mortality.

Advanced Imaging for Quantitative Evaluation of Aphanomyces Root Rot Resistance in Lentil

Aphanomyces root rot (ARR) is a soil-borne disease that results in severe yield losses in lentil. The development of resistant cultivars is one of the key strategies to control this pathogen. However, the evaluation of disease severity is limited to visual scores that can be subjective. This study utilized image-based phenotyping approaches to evaluate Aphanomyces euteiches resistance in lentil genotypes in greenhouse (351 genotypes from lentil single plant/LSP derived collection and 191 genotypes from recombinant inbred lines/RIL using digital Red-Green-Blue/RGB and hyperspectral imaging) and field (173 RIL genotypes using unmanned aerial system-based multispectral imaging) conditions. Moderate to strong correlations were observed between RGB, multispectral, and hyperspectral derived features extracted from lentil shoots/roots and visual scores. In general, root features extracted from RGB imaging were found to be strongly associated with disease severity. With only three root traits, elastic net regression model was able to predict disease severity across and within multiple datasets (R 2 = 0.45-0.73 and RMSE = 0.66-1.00). The selected features could represent visual disease scores. Moreover, we developed twelve normalized difference spectral indices (NDSIs) that were significantly correlated with disease scores: two NDSIs for lentil shoot section - computed from wavelengths of 1170, 1160, 1270, and 1280 nm (0.12 ≤ |r| ≤ 0.24, P < 0.05) and ten NDSIs for lentil root sections - computed from wavelengths in the range of 630-670, 700-840, and 1320-1530 nm (0.10 ≤ |r| ≤ 0.50, P < 0.05). Root-derived NDSIs were more accurate in predicting disease scores with an R 2 of 0.54 (RMSE = 0.86), especially when the model was trained and tested on LSP accessions, compared to R 2 of 0.25 (RMSE = 1.64) when LSP and RIL genotypes were used as train and test datasets, respectively. Importantly, NDSIs - computed from wavelengths of 700, 710, 730, and 790 nm - had strong positive correlations with disease scores (0.35 ≤r ≤ 0.50, P < 0.0001), which was confirmed in field phenotyping with similar correlations using vegetation index with red edge wavelength (normalized difference red edge, 0.36 ≤ |r| ≤ 0.57, P < 0.0001). The adopted image-based phenotyping approaches can help plant breeders to objectively quantify ARR resistance and reduce the subjectivity in selecting potential genotypes.

Canopy Effects on Snow Accumulation: Observations from Lidar, Canonical-View Photos, and Continuous Ground Measurements from Sensor Networks

A variety of canopy metrics were extracted from the snow-off airborne light detection and ranging (lidar) measurements over three study areas in the central and southern Sierra Nevada. Two of the sites, Providence and Wolverton, had wireless snow-depth sensors since 2008, with the third site, Pinecrest having sensors since 2014. At Wolverton and Pinecrest, images were captured and the sky-view factors were derived from hemispherical-view photos. We found the variation of snow accumulation across the landscape to be significantly related to canopy-cover conditions. Using a regularized regression model Elastic Net to model the normalized snow accumulation with canopy metrics as independent variables, we found that about 50 % of snow accumulation variability at each site can be explained by the canopy metrics from lidar.

Comprehensive analysis identifying aberrant DNA methylation in rectal mucosa from ulcerative colitis patients with neoplasia

Background

There are no biomarkers to facilitate the identification of patients with ulcerative colitis (UC) who are at high risk for developing colorectal cancer (CRC). In our current study, we used rectal tissues from UC patients to identify aberrant DNA methylations and evaluated whether they could be used to identify UC patients with coexisting colorectal neoplasia.

Results

Using a training set, we identified 484 differentially methylated regions (DMRs) with absolute delta beta-values > 0.1 in rectal mucosa by using the ChAMP algorithm. Next, pathway enrichment analysis was performed using 484 DMRs to select coordinately methylated DMRs, resulting in the selection of 187 aberrant DMRs in rectal tissues from UC-CRC. Then, the Elastic Net classification algorithm was performed to narrow down optimal aberrant DMRs, and we finally selected 11 DMRs as biomarkers for identification of UC-CRC patients. The 11 chosen DMRs could discriminate UC patients with or without CRC in a training set (area under the curve, 0.96) and the validation set (area under the curve, 0.81).

Conclusions

In conclusion, we identified 11 DMRs that could identify UC patients with CRC complications. Prospective studies should further confirm the validity of these biomarkers.

Methods

We performed genome-wide DNA methylation profiles in rectal mucosal tissues (n = 48) from 24 UC-CRC and 24 UC patients in a training set. Next, we performed comprehensive DNA methylation analysis using rectal mucosal tissues (n = 16) from 8 UC-CRC and 8 UC patients for validation.

Personalized Nutrition-Genes, Diet, and Related Interactive Parameters as Predictors of Cancer in Multiethnic Colorectal Cancer Families

To personalize nutrition, the purpose of this study was to examine five key genes in the folate metabolism pathway, and dietary parameters and related interactive parameters as predictors of colorectal cancer (CRC) by measuring the healthy eating index (HEI) in multiethnic families. The five genes included methylenetetrahydrofolate reductase (MTHFR) 677 and 1298, methionine synthase (MTR) 2756, methionine synthase reductase (MTRR 66), and dihydrofolate reductase (DHFR) 19bp, and they were used to compute a total gene mutation score. We included 53 families, 53 CRC patients and 53 paired family friend members of diverse population groups in Southern California. We measured multidimensional data using the ensemble bootstrap forest method to identify variables of importance within domains of genetic, demographic, and dietary parameters to achieve dimension reduction. We then constructed predictive generalized regression (GR) modeling with a supervised machine learning validation procedure with the target variable (cancer status) being specified to validate the results to allow enhanced prediction and reproducibility. The results showed that the CRC group had increased total gene mutation scores compared to the family members (p < 0.05). Using the Akaike’s information criterion and Leave-One-Out cross validation GR methods, the HEI was interactive with thiamine (vitamin B1), which is a new finding for the literature. The natural food sources for thiamine include whole grains, legumes, and some meats and fish which HEI scoring included as part of healthy portions (versus limiting portions on salt, saturated fat and empty calories). Additional predictors included age, as well as gender and the interaction of MTHFR 677 with overweight status (measured by body mass index) in predicting CRC, with the cancer group having more men and overweight cases. The HEI score was significant when split at the median score of 77 into greater or less scores, confirmed through the machine-learning recursive tree method and predictive modeling, although an HEI score of greater than 80 is the US national standard set value for a good diet. The HEI and healthy eating are modifiable factors for healthy living in relation to dietary parameters and cancer prevention, and they can be used for personalized nutrition in the precision-based healthcare era.