Multiset sparse partial least squares path modeling for high dimensional omics data analysis

Opportunities offered by latent-based multiblock strategies to integrate biomarkers of chemical exposure and biomarkers of effect in environmental health studies

Modern environmental epidemiology benefits from a new generation of technologies that enable comprehensive profiling of biomarkers, including environmental chemical exposure and omic datasets. The integration and analysis of large and structured datasets to identify functional associations is constrained by computational challenges that cannot be overcome using conventional regression methods. Some extensions of Partial Least Squares (PLS) regression have been developed to efficently integrate multiple datasets, including Multiblock PLS (MB-PLS) and Sequential and Orthogonalized PLS; however, these approaches remain seldom applied in environmental epidemiology. To address that research gap, this study aimed to assess and compare the applicability of PLS-based multiblock models in an observational case study, where biomarkers of exposure to environmental chemicals and endogenous biomarkers of effect were simultaneously integrated to highlight biological links related to a health outcome. The methods were compared with and without sparsity coupling two metrics to support the variable selection: Variable Importance in Projection (VIP) and Selectivity Ratio (SR). The framework was applied to a case-study dataset mimicking the structure of 36 environmental exposure biomarkers (E-block), 61 inflammation biomarkers (M-block), and their relationships with the gestational age at delivery of 161 mother-infant pairs. The results showed an overall consistency in the selected variables across models, although some specific selection patterns were identified. The block-scaled concatenation-based approaches (e.g. MB-PLS) tended to select more variables from the E-block, while these methods were unable to identify certain variables in the M-block. Overall, the number of variables selected using the SR criterion was higher than using the VIP criterion, with lower predictive performances. The multiblock models coupled to VIP, appeared to be the methods of choice for identifying relevant variables with similar statistical performances. Overall, the use of multiblock PLS-based methods appears to be a good strategy to efficiently support the variable selection process in modern environmental epidemiology.

Multiomics, artificial intelligence, and precision medicine in perinatology

Technological advances in omics evaluation, bioinformatics, and artificial intelligence have made us rethink ways to improve patient outcomes. Collective quantification and characterization of biological data including genomics, epigenomics, metabolomics, and proteomics is now feasible at low cost with rapid turnover. Significant advances in the integration methods of these multiomics data sets by machine learning promise us a holistic view of disease pathogenesis and yield biomarkers for disease diagnosis and prognosis. Using machine learning tools and algorithms, it is possible to integrate multiomics data with clinical information to develop predictive models that identify risk before the condition is clinically apparent, thus facilitating early interventions to improve the health trajectories of the patients. In this review, we intend to update the readers on the recent developments related to the use of artificial intelligence in integrating multiomic and clinical data sets in the field of perinatology, focusing on neonatal intensive care and the opportunities for precision medicine. We intend to briefly discuss the potential negative societal and ethical consequences of using artificial intelligence in healthcare. We are poised for a new era in medicine where computational analysis of biological and clinical data sets will make precision medicine a reality. IMPACT: Biotechnological advances have made multiomic evaluations feasible and integration of multiomics data may provide a holistic view of disease pathophysiology. Artificial Intelligence and machine learning tools are being increasingly used in healthcare for diagnosis, prognostication, and outcome predictions. Leveraging artificial intelligence and machine learning tools for integration of multiomics and clinical data will pave the way for precision medicine in perinatology.

Integrated single-cell RNA-seq and DNA methylation reveal the effects of air pollution in patients with recurrent spontaneous abortion

Background

Maternal air pollutants exposure is associated with a number of adverse pregnancy outcomes, including recurrent spontaneous abortion (RSA). However, the underlying mechanisms are still unknown. The present study aimed to understand the mechanism of RSA and its relationship with air pollution exposure. We compared data of decidual tissue from individuals with induced abortions and those with RSA by bulk RNA sequencing (RNA-seq), reduced representation bisulfite sequencing (RRBS), and single-cell RNA sequencing (scRNA-seq). Differentially expressed genes (DEGs) were verified using RT-qPCR and pyrosequencing. A logistic regression model was used to investigate the association between air pollutants exposure and RSA.

Results

We identified 98 DEGs with aberrant methylation by overlapping the RRBS and RNA-seq data. Nineteen immune cell subsets were identified. Compared with normal controls, NK cells and macrophages accounted for different proportions in the decidua of patients with RSA. We observed that the methylation and expression of IGF2BP1 were different between patients with RSA and controls. Furthermore, we observed significant positive associations between maternal air pollutants exposure during the year prior to pregnancy and in early pregnancy and the risk of RSA. Mediation analyses suggested that 24.5% of the effects of air pollution on the risk of RSA were mediated through IGF2BP1 methylation.

Conclusion

These findings reveal a comprehensive cellular and molecular mechanism of RSA and suggest that air pollution might cause pregnancy loss by affecting the methylation level of the IGF2BP1 promoter.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-022-01327-2.

Fabrication approaches for high-throughput and biomimetic disease modeling

There is often a tradeoff between in vitro disease modeling platforms that capture pathophysiologic complexity and those that are amenable to high-throughput fabrication and analysis. However, this divide is closing through the application of a handful of fabrication approaches-parallel fabrication, automation, and flow-driven assembly-to design sophisticated cellular and biomaterial systems. The purpose of this review is to highlight methods for the fabrication of high-throughput biomaterial-based platforms and showcase examples that demonstrate their utility over a range of throughput and complexity. We conclude with a discussion of future considerations for the continued development of higher-throughput in vitro platforms that capture the appropriate level of biological complexity for the desired application. STATEMENT OF SIGNIFICANCE: There is a pressing need for new biomedical tools to study and understand disease. These platforms should mimic the complex properties of the body while also permitting investigation of many combinations of cells, extracellular cues, and/or therapeutics in high-throughput. This review summarizes emerging strategies to fabricate biomimetic disease models that bridge the gap between complex tissue-mimicking microenvironments and high-throughput screens for personalized medicine.

Multiblock variable influence on orthogonal projections (MB-VIOP) for enhanced interpretation of total, global, local and unique variations in OnPLS models

BACKGROUND

For multivariate data analysis involving only two input matrices (e.g., X and Y), the previously published methods for variable influence on projection (e.g., VIPOPLS or VIPO2PLS) are widely used for variable selection purposes, including (i) variable importance assessment, (ii) dimensionality reduction of big data and (iii) interpretation enhancement of PLS, OPLS and O2PLS models. For multiblock analysis, the OnPLS models find relationships among multiple data matrices (more than two blocks) by calculating latent variables; however, a method for improving the interpretation of these latent variables (model components) by assessing the importance of the input variables was not available up to now.

RESULTS

A method for variable selection in multiblock analysis, called multiblock variable influence on orthogonal projections (MB-VIOP) is explained in this paper. MB-VIOP is a model based variable selection method that uses the data matrices, the scores and the normalized loadings of an OnPLS model in order to sort the input variables of more than two data matrices according to their importance for both simplification and interpretation of the total multiblock model, and also of the unique, local and global model components separately. MB-VIOP has been tested using three datasets: a synthetic four-block dataset, a real three-block omics dataset related to plant sciences, and a real six-block dataset related to the food industry.

CONCLUSIONS

We provide evidence for the usefulness and reliability of MB-VIOP by means of three examples (one synthetic and two real-world cases). MB-VIOP assesses in a trustable and efficient way the importance of both isolated and ranges of variables in any type of data. MB-VIOP connects the input variables of different data matrices according to their relevance for the interpretation of each latent variable, yielding enhanced interpretability for each OnPLS model component. Besides, MB-VIOP can deal with strong overlapping of types of variation, as well as with many data blocks with very different dimensionality. The ability of MB-VIOP for generating dimensionality reduced models with high interpretability makes this method ideal for big data mining, multi-omics data integration and any study that requires exploration and interpretation of large streams of data.