Multi-Omics Factor Analysis-a framework for unsupervised integration of multi-omics data sets

An emerging multi-omic understanding of the genetics of opioid addiction

Opioid misuse, addiction, and associated overdose deaths remain global public health crises. Despite the tremendous need for pharmacological treatments, current options are limited in number, use, and effectiveness. Fundamental leaps forward in our understanding of the biology driving opioid addiction are needed to guide development of more effective medication-assisted therapies. This Review focuses on the omics-identified biological features associated with opioid addiction. Recent GWAS have begun to identify robust genetic associations, including variants in OPRM1, FURIN, and the gene cluster SCAI/PPP6C/RABEPK. An increasing number of omics studies of postmortem human brain tissue examining biological features (e.g., histone modification and gene expression) across different brain regions have identified broad gene dysregulation associated with overdose death among opioid misusers. Drawn together by meta-analysis and multi-omic systems biology, and informed by model organism studies, key biological pathways enriched for opioid addiction-associated genes are emerging, which include specific receptors (e.g., GABAB receptors, GPCR, and Trk) linked to signaling pathways (e.g., Trk, ERK/MAPK, orexin) that are associated with synaptic plasticity and neuronal signaling. Studies leveraging the agnostic discovery power of omics and placing it within the context of functional neurobiology will propel us toward much-needed, field-changing breakthroughs, including identification of actionable targets for drug development to treat this devastating brain disease.

Dual-Approach Co-expression Analysis Framework (D-CAF) Enables Identification of Novel Circadian Regulation From Multi-Omic Timeseries Data

The circadian clock is a central driver of many biological and behavioral processes, regulating the levels of many genes and proteins, termed clock controlled genes and proteins (CCGs/CCPs), to impart biological timing at the molecular level. While transcriptomic and proteomic data has been analyzed to find potential CCGs and CCPs, multi-omic modeling of circadian data, which has the potential to enhance the understanding of circadian control of biological timing, remains relatively rare due to several methodological hurdles. To address this gap, a Dual-approach Co-expression Analysis Framework (D-CAF) was created to perform perturbation-robust co-expression analysis on time-series measurements of both transcripts and proteins. Applying this D-CAF framework to previously gathered transcriptomic and proteomic data from mouse macrophages gathered over circadian time, we identified small, highly significant clusters of oscillating transcripts and proteins in the unweighted similarity matrices and larger, less significant clusters of of oscillating transcripts and proteins using the weighted similarity network. Functional enrichment analysis of these clusters identified novel immunological response pathways that appear to be under circadian control. Overall, our findings suggest that D-CAF is a tool that can be used by the circadian community to integrate multi-omic circadian data to improve our understanding of the mechanisms of circadian regulation of molecular processes.

Multi-omics profiling of DNA methylation and gene expression alterations in human cocaine use disorder

Structural and functional changes of the brain are assumed to contribute to excessive cocaine intake, craving, and relapse in cocaine use disorder (CUD). Epigenetic and transcriptional changes were hypothesized as a molecular basis for CUD-associated brain alterations. Here we performed a multi-omics study of CUD by integrating epigenome-wide methylomic (N = 42) and transcriptomic (N = 25) data from the same individuals using postmortem brain tissue of Brodmann Area 9 (BA9). Of the N = 1 057 differentially expressed genes (p < 0.05), one gene, ZFAND2A, was significantly upregulated in CUD at transcriptome-wide significance (q < 0.05). Differential alternative splicing (AS) analysis revealed N = 98 alternatively spliced transcripts enriched in axon and dendrite extension pathways. Strong convergent overlap in CUD-associated expression deregulation was found between our BA9 cohort and independent replication datasets. Epigenomic, transcriptomic, and AS changes in BA9 converged at two genes, ZBTB4 and INPP5E. In pathway analyses, synaptic signaling, neuron morphogenesis, and fatty acid metabolism emerged as the most prominently deregulated biological processes. Drug repositioning analysis revealed glucocorticoid receptor targeting drugs as most potent in reversing the CUD expression profile. Our study highlights the value of multi-omics approaches for an in-depth molecular characterization and provides insights into the relationship between CUD-associated epigenomic and transcriptomic signatures in the human prefrontal cortex.

Molecular and functional landscape of malignant serous effusions for precision oncology

Personalized treatment for patients with advanced solid tumors critically depends on the deep characterization of tumor cells from patient biopsies. Here, we comprehensively characterize a pan-cancer cohort of 150 malignant serous effusion (MSE) samples at the cellular, molecular, and functional level. We find that MSE-derived cancer cells retain the genomic and transcriptomic profiles of their corresponding primary tumors, validating their use as a patient-relevant model system for solid tumor biology. Integrative analyses reveal that baseline gene expression patterns relate to global ex vivo drug sensitivity, while high-throughput drug-induced transcriptional changes in MSE samples are indicative of drug mode of action and acquired treatment resistance. A case study exemplifies the added value of multi-modal MSE profiling for patients who lack genetically stratified treatment options. In summary, our study provides a functional multi-omics view on a pan-cancer solid tumor cohort and underlines the feasibility and utility of MSE-based precision oncology.

Mosaic integration and knowledge transfer of single-cell multimodal data with MIDAS

Integrating single-cell datasets produced by multiple omics technologies is essential for defining cellular heterogeneity. Mosaic integration, in which different datasets share only some of the measured modalities, poses major challenges, particularly regarding modality alignment and batch effect removal. Here, we present a deep probabilistic framework for the mosaic integration and knowledge transfer (MIDAS) of single-cell multimodal data. MIDAS simultaneously achieves dimensionality reduction, imputation and batch correction of mosaic data by using self-supervised modality alignment and information-theoretic latent disentanglement. We demonstrate its superiority to 19 other methods and reliability by evaluating its performance in trimodal and mosaic integration tasks. We also constructed a single-cell trimodal atlas of human peripheral blood mononuclear cells and tailored transfer learning and reciprocal reference mapping schemes to enable flexible and accurate knowledge transfer from the atlas to new data. Applications in mosaic integration, pseudotime analysis and cross-tissue knowledge transfer on bone marrow mosaic datasets demonstrate the versatility and superiority of MIDAS. MIDAS is available at https://github.com/labomics/midas .

A comprehensive review of machine learning techniques for multi-omics data integration: challenges and applications in precision oncology

Multi-omics data play a crucial role in precision medicine, mainly to understand the diverse biological interaction between different omics. Machine learning approaches have been extensively employed in this context over the years. This review aims to comprehensively summarize and categorize these advancements, focusing on the integration of multi-omics data, which includes genomics, transcriptomics, proteomics and metabolomics, alongside clinical data. We discuss various machine learning techniques and computational methodologies used for integrating distinct omics datasets and provide valuable insights into their application. The review emphasizes both the challenges and opportunities present in multi-omics data integration, precision medicine and patient stratification, offering practical recommendations for method selection in various scenarios. Recent advances in deep learning and network-based approaches are also explored, highlighting their potential to harmonize diverse biological information layers. Additionally, we present a roadmap for the integration of multi-omics data in precision oncology, outlining the advantages, challenges and implementation difficulties. Hence this review offers a thorough overview of current literature, providing researchers with insights into machine learning techniques for patient stratification, particularly in precision oncology. Contact:  anirban@klyuniv.ac.in.

Systemic dysregulation and molecular insights into poor influenza vaccine response in the aging population

Vaccination-induced protection against influenza is greatly diminished and increasingly heterogeneous with age. We investigated longitudinally (up to five time points) a cohort of 234 vaccinated >65-year-old vaccinees with adjuvanted vaccine FluAd across two independent seasons. System-level analyses of multiomics datasets measuring six modalities and serological data revealed that poor responders lacked time-dependent changes in response to vaccination as observed in responders, suggestive of systemic dysregulation in poor responders. Multiomics integration revealed key molecules and their likely role in vaccination response. High prevaccination plasma interleukin-15 (IL-15) concentrations negatively associated with antibody production, further supported by experimental validation in mice revealing an IL-15-driven natural killer cell axis explaining the suppressive role in vaccine-induced antibody production as observed in poor responders. We propose a subset of long-chain fatty acids as modulators of persistent inflammation in poor responders. Our findings provide a potential link between low-grade chronic inflammation and poor vaccination response and open avenues for possible pharmacological interventions to enhance vaccine responses.

An explainable graph neural network approach for effectively integrating multi-omics with prior knowledge to identify biomarkers from interacting biological domains

The rapid growth of multi-omics datasets, in addition to the wealth of existing biological prior knowledge, necessitates the development of effective methods for their integration. Such methods are essential for building predictive models and identifying disease-related molecular markers. We propose a framework for supervised integration of multi-omics data with biological priors represented as knowledge graphs. Our framework leverages graph neural networks (GNNs) to model the relationships among features from high-dimensional 'omics data and set transformers to integrate low-dimensional representations of 'omics features. Furthermore, our framework incorporates explainability methods to elucidate important biomarkers and extract interaction relationships between biological quantities of interest. We demonstrate the effectiveness of our approach by applying it to Alzheimer's disease (AD) multi-omics data from the ROSMAP cohort, showing that the integration of transcriptomics and proteomics data with AD biological domain network priors improves the prediction accuracy of AD status and highlights functional AD biomarkers.

Stool and blood metabolomics in the metabolic syndrome: a cross-sectional study

INTRODUCTION/OBJECTIVES

Changes in the stool metabolome have been poorly studied in the metabolic syndrome (MetS). Moreover, few studies have explored the relationship of stool metabolites with circulating metabolites. Here, we investigated the associations between stool and blood metabolites, the MetS and systemic inflammation.

METHODS

We analyzed data from 1,370 participants of the KORA FF4 study (Germany). Metabolites were measured by Metabolon, Inc. (untargeted) in stool, and using the AbsoluteIDQ® p180 kit (targeted) in blood. Multiple linear regression models, adjusted for dietary pattern, age, sex, physical activity, smoking status and alcohol intake, were used to estimate the associations of metabolites with the MetS, its components and high-sensitivity C-reactive protein (hsCRP) levels. Partial correlation and Multi-Omics Factor Analysis (MOFA) were used to investigate the relationship between stool and blood metabolites.

RESULTS

The MetS was significantly associated with 170 stool and 82 blood metabolites. The MetS components with the highest number of associations were triglyceride levels (stool) and HDL levels (blood). Additionally, 107 and 27 MetS-associated metabolites (in stool and blood, respectively) showed significant associations with hsCRP levels. We found low partial correlation coefficients between stool and blood metabolites. MOFA did not detect shared variation across the two datasets.

CONCLUSIONS

The MetS, particularly dyslipidemia, is associated with multiple stool and blood metabolites that are also associated with systemic inflammation. Further studies are necessary to validate our findings and to characterize metabolic alterations in the MetS. Although our analyses point to weak correlations between stool and blood metabolites, additional studies using integrative approaches are warranted.

Shared neutrophil and T cell dysfunction is accompanied by a distinct interferon signature during severe febrile illnesses in children

Severe febrile illnesses in children encompass life-threatening organ dysfunction caused by diverse pathogens and other severe inflammatory syndromes. A comparative approach to these illnesses may identify shared and distinct features of host immune dysfunction amenable to immunomodulation. Here, using immunophenotyping with mass cytometry and cell stimulation experiments, we illustrate trajectories of immune dysfunction in 74 children with multi-system inflammatory syndrome in children (MIS-C) associated with SARS-CoV-2, 30 with bacterial infection, 16 with viral infection, 8 with Kawasaki disease, and 42 controls. We explore these findings in a secondary cohort of 500 children with these illnesses and 134 controls. We show that neutrophil activation and apoptosis are prominent in multi-system inflammatory syndrome, and that this is partially shared with bacterial infection. We show that memory T cells from patients with multi-system inflammatory syndrome and bacterial infection are exhausted. In contrast, we show viral infection to be characterized by a distinct signature of decreased interferon signaling and lower interferon receptor gene expression. Improved understanding of immune dysfunction may improve approaches to immunomodulator therapy in severe febrile illnesses in children.

Mining nucleic acid "omics" to boost liquid biopsy in cancer

Treatments for cancer patients are becoming increasingly complex, and there is a growing desire from clinicians and patients for biomarkers that can account for this complexity to support informed decisions about clinical care. To achieve precision medicine, the new generation of biomarkers must reflect the spatial and temporal heterogeneity of cancer biology both between patients and within an individual patient. Mining the different layers of 'omics in a multi-modal way from a minimally invasive, easily repeatable, liquid biopsy has increasing potential in a range of clinical applications, and for improving our understanding of treatment response and resistance. Here, we detail the recent developments and methods allowing exploration of genomic, epigenomic, transcriptomic, and fragmentomic layers of 'omics from liquid biopsy, and their integration in a range of applications. We also consider the specific challenges that are posed by the clinical implementation of multi-omic liquid biopsies.

Batch-effect correction in single-cell RNA sequencing data using JIVE

Motivation

In single-cell RNA sequencing analysis, addressing batch effects-technical artifacts stemming from factors such as varying sequencing technologies, equipment, and capture times-is crucial. These factors can cause unwanted variation and obfuscate the underlying biological signal of interest. The joint and individual variation explained (JIVE) method can be used to extract shared biological patterns from multi-source sequencing data while adjusting for individual non-biological variations (i.e. batch effect). However, its current implementation is originally designed for bulk sequencing data, making it computationally infeasible for large-scale single-cell sequencing datasets.

Results

In this study, we enhance JIVE for large-scale single-cell data by boosting its computational efficiency. Additionally, we introduce a novel application of JIVE for batch-effect correction on multiple single-cell sequencing datasets. Our enhanced method aims to decompose single-cell sequencing datasets into a joint structure capturing the true biological variability and individual structures, which capture technical variability within each batch. This joint structure is then suitable for use in downstream analyses. We benchmarked the results against four popular tools, Seurat v5, Harmony, LIGER, and Combat-seq, which were developed for this purpose. JIVE performed best in terms of preserving cell-type effects and in scenarios in which the batch sizes are balanced.

Availability and implementation

The JIVE implementation used for this analysis can be found at https://github.com/oconnell-statistics-lab/scJIVE.

A spatially resolved single-cell lung atlas integrated with clinical and blood signatures distinguishes COVID-19 disease trajectories

COVID-19 is characterized by a broad range of symptoms and disease trajectories. Understanding the correlation between clinical biomarkers and lung pathology during acute COVID-19 is necessary to understand its diverse pathogenesis and inform more effective treatments. Here, we present an integrated analysis of longitudinal clinical parameters, peripheral blood markers, and lung pathology in 142 Brazilian patients hospitalized with COVID-19. We identified core clinical and peripheral blood signatures differentiating disease progression between patients who recovered from severe disease compared with those who succumbed to the disease. Signatures were heterogeneous among fatal cases yet clustered into two patient groups: "early death" (<15 days until death) and "late death" (>15 days). Progression to early death was characterized systemically and in lung histopathological samples by rapid endothelial and myeloid activation and the presence of thrombi associated with SARS-CoV-2+ macrophages. In contrast, progression to late death was associated with fibrosis, apoptosis, and SARS-CoV-2+ epithelial cells in postmortem lung tissue. In late death cases, cytotoxicity, interferon, and T helper 17 (TH17) signatures were only detectable in the peripheral blood after 2 weeks of hospitalization. Progression to recovery was associated with higher lymphocyte counts, TH2 responses, and anti-inflammatory-mediated responses. By integrating antemortem longitudinal blood signatures and spatial single-cell lung signatures from postmortem lung samples, we defined clinical parameters that could be used to help predict COVID-19 outcomes.

Putting computational models of immunity to the test - an invited challenge to predict B. pertussis vaccination outcomes

Systems vaccinology studies have been used to build computational models that predict individual vaccine responses and identify the factors contributing to differences in outcome. Comparing such models is challenging due to variability in study designs. To address this, we established a community resource to compare models predicting B. pertussis booster responses and generate experimental data for the explicit purpose of model evaluation. We here describe our second computational prediction challenge using this resource, where we benchmarked 49 algorithms from 53 scientists. We found that the most successful models stood out in their handling of nonlinearities, reducing large feature sets to representative subsets, and advanced data preprocessing. In contrast, we found that models adopted from literature that were developed to predict vaccine antibody responses in other settings performed poorly, reinforcing the need for purpose-built models. Overall, this demonstrates the value of purpose-generated datasets for rigorous and open model evaluations to identify features that improve the reliability and applicability of computational models in vaccine response prediction.

Transforming Clinical Research: The Power of High-Throughput Omics Integration

High-throughput omics technologies have dramatically changed biological research, providing unprecedented insights into the complexity of living systems. This review presents a comprehensive examination of the current landscape of high-throughput omics pipelines, covering key technologies, data integration techniques and their diverse applications. It looks at advances in next-generation sequencing, mass spectrometry and microarray platforms and highlights their contribution to data volume and precision. In addition, this review looks at the critical role of bioinformatics tools and statistical methods in managing the large datasets generated by these technologies. By integrating multi-omics data, researchers can gain a holistic understanding of biological systems, leading to the identification of new biomarkers and therapeutic targets, particularly in complex diseases such as cancer. The review also looks at the integration of omics data into electronic health records (EHRs) and the potential for cloud computing and big data analytics to improve data storage, analysis and sharing. Despite significant advances, there are still challenges such as data complexity, technical limitations and ethical issues. Future directions include the development of more sophisticated computational tools and the application of advanced machine learning techniques, which are critical for addressing the complexity and heterogeneity of omics datasets. This review aims to serve as a valuable resource for researchers and practitioners, highlighting the transformative potential of high-throughput omics technologies in advancing personalized medicine and improving clinical outcomes.

Advances in metabolomics profiling of pediatric kidney diseases: A review

Pediatric renal diseases encompass a diverse array of pathological conditions, often engendering enduring ramifications. Metabolomics, an emergent branch of omics sciences, endeavors to holistically delineate alterations in metabolite compositions through the amalgamation of sophisticated analytical chemistry techniques and robust statistical methodologies. Recent advancements in metabolomics research within the realm of pediatric nephrology have been substantial, offering promising avenues for the identification of robust biomarkers, the elaboration of novel therapeutic targets, and the intricate elucidation of molecular mechanisms. The present discourse aims to critically review the progress in metabolomics profiling pertinent to pediatric renal disorders over the previous 12 years.

Integrative analysis of multi-omics data reveals importance of collagen and the PI3K AKT signalling pathway in CAKUT

Congenital Anomalies of the Kidney and Urinary Tract (CAKUT) is the leading cause of childhood chronic kidney failure and a significant cause of chronic kidney disease in adults. Genetic and environmental factors are known to influence CAKUT development, but the currently known disease mechanism remains incomplete. Our goal is to identify affected pathways and networks in CAKUT, and thereby aid in getting a better understanding of its pathophysiology. With this goal, the miRNome, peptidome, and proteome of over 30 amniotic fluid samples of patients with non-severe CAKUT was compared to patients with severe CAKUT. These omics data sets were made findable, accessible, interoperable, and reusable (FAIR) to facilitate their integration with external data resources. Furthermore, we analysed and integrated the omics data sets using three different bioinformatics strategies: integrative analysis with mixOmics, joint dimensionality reduction and pathway analysis. The three bioinformatics analyses provided complementary features, but all pointed towards an important role for collagen in CAKUT development and the PI3K-AKT signalling pathway. Additionally, several key genes (CSF1, IGF2, ITGB1, and RAC1) and microRNAs were identified. We published the three analysis strategies as containerized workflows. These workflows can be applied to other FAIR data sets and help gaining knowledge on other rare diseases.

Natural variation in response to combined water and nitrogen deficiencies in Arabidopsis

Understanding plant responses to individual stresses does not mean that we understand real-world situations, where stresses usually combine and interact. These interactions arise at different levels, from stress exposure to the molecular networks of the stress response. Here, we built an in-depth multiomic description of plant responses to mild water (W) and nitrogen (N) limitations, either individually or combined, among 5 genetically different Arabidopsis (Arabidopsis thaliana) accessions. We highlight the different dynamics in stress response through integrative traits such as rosette growth and the physiological status of the plants. We also used transcriptomic and metabolomic profiling during a stage when the plant response was stabilized to determine the wide diversity in stress-induced changes among accessions, highlighting the limited reality of a "universal" stress response. The main effect of the W × N interaction was an attenuation of the N-deficiency syndrome when combined with mild drought, but to a variable extent depending on the accession. Other traits subject to W × N interactions are often accession specific. Multiomic analyses identified a subset of transcript-metabolite clusters that are critical to stress responses but essentially variable according to the genotype factor. Including intraspecific diversity in our descriptions of plant stress response places our findings in perspective.

An Integrated Multi-omics prediction model for stroke recurrence based on Lnet transformer layer and dynamic weighting mechanism

BACKGROUND AND OBJECTIVE

Stroke is a disease with high mortality and disability. Importantly, the fatality rate demonstrates a significant increase among patients afflicted by recurrent strokes compared to those experiencing their initial stroke episode. Currently, the existing research encounters three primary challenges. The first is the lack of a reliable, multi-omics image dataset related to stroke recurrence. The second is how to establish a high-performance feature extraction model and eliminate noise from continuous magnetic resonance imaging (MRI) data. The third is how to integration multi-omics data and dynamically weighted for different omics data.

METHODS

We systematically compiled MRI and conventional detection data from a cohort comprising 737 stroke patients and established PSTSZC, a multi-omics dataset for predicting stroke recurrence. We introduced the first-ever Integrated Multi-omics Prediction Model for Stroke Recurrence, MPSR, which is based on ResNet, Lnet-transformer, LSTM and dynamically weighted DNN. The MPSR model comprises two principal modules, the Feature Extraction Module, and the Integrated Multi-Omics Prediction Module. In the Feature Extraction module, we proposed a novel Lnet regularization layer, which effectively addresses noise issues in MRI data. In the Integrated Multi-omics Prediction Module, we propose a dynamic weighted mechanism based on evaluators, which mitigates the noise impact brought about by low-performance omics.

RESULTS

We compared seven single-omics models and six state-of-the-art multi-omics stroke recurrence models. The experimental results demonstrate that the MPSR model exhibited superior performance. The accuracy, AUROC, specificity, and sensitivity of the MPSR model can reach 0.96, 0.97, 1, and 0.94, respectively, which is higher than the results of contrast model.

CONCLUSION

MPSR is the first available high-performance multi-omics prediction model for stroke recurrence. We assert that the MPSR model holds the potential to function as a valuable tool in assisting clinicians in accurately diagnosing individuals with a predisposition to stroke recurrence.

Bayesian Simultaneous Factorization and Prediction Using Multi-Omic Data

Integrative factorization methods for multi-omic data estimate factors explaining biological variation. Factors can be treated as covariates to predict an outcome and the factorization can be used to impute missing values. However, no available methods provide a comprehensive framework for statistical inference and uncertainty quantification for these tasks. A novel framework, Bayesian Simultaneous Factorization (BSF), is proposed to decompose multi-omics variation into joint and individual structures simultaneously within a probabilistic framework. BSF uses conjugate normal priors and the posterior mode of this model can be estimated by solving a structured nuclear norm-penalized objective that also achieves rank selection and motivates the choice of hyperparameters. BSF is then extended to simultaneously predict a continuous or binary phenotype while estimating latent factors, termed Bayesian Simultaneous Factorization and Prediction (BSFP). BSF and BSFP accommodate concurrent imputation, i.e., imputation during the model-fitting process, and full posterior inference for missing data, including "blockwise" missingness. It is shown via simulation that BSFP is competitive in recovering latent variation structure, and demonstrate the importance of accounting for uncertainty in the estimated factorization within the predictive model. The imputation performance of BSF is examined via simulation under missing-at-random and missing-not-at-random assumptions. Finally, BSFP is used to predict lung function based on the bronchoalveolar lavage metabolome and proteome from a study of HIV-associated obstructive lung disease, revealing multi-omic patterns related to lung function decline and a cluster of patients with obstructive lung disease driven by shared metabolomic and proteomic abundance patterns.

MoNETA: MultiOmics Network Embedding for SubType Analysis

Cells are complex systems whose behavior emerges from a huge number of reactions taking place within and among different molecular districts. The availability of bulk and single-cell omics data fueled the creation of multi-omics systems biology models capturing the dynamics within and between omics layers. Powerful modeling strategies are needed to cope with the increased amount of data to be interrogated and the relative research questions. Here, we present MultiOmics Network Embedding for SubType Analysis (MoNETA) for fast and scalable identification of relevant multi-omics relationships between biological entities at the bulk and single-cells level. We apply MoNETA to show how glioma subtypes previously described naturally emerge with our approach. We also show how MoNETA can be used to identify cell types in five multi-omic single-cell datasets.

Smccnet 2.0: a comprehensive tool for multi-omics network inference with shiny visualization

Sparse multiple canonical correlation network analysis (SmCCNet) is a machine learning technique for integrating omics data along with a variable of interest (e.g., phenotype of complex disease), and reconstructing multi-omics networks that are specific to this variable. We present the second-generation SmCCNet (SmCCNet 2.0) that adeptly integrates single or multiple omics data types along with a quantitative or binary phenotype of interest. In addition, this new package offers a streamlined setup process that can be configured manually or automatically, ensuring a flexible and user-friendly experience. AVAILABILITY : This package is available in both CRAN: https://cran.r-project.org/web/packages/SmCCNet/index.html and Github: https://github.com/KechrisLab/SmCCNet under the MIT license. The network visualization tool is available at https://smccnet.shinyapps.io/smccnetnetwork/ .

Multimodal analysis unveils tumor microenvironment heterogeneity linked to immune activity and evasion

The cellular and molecular heterogeneity of tumors is a major obstacle to cancer immunotherapy. Here, we use a systems biology approach to derive a signature of the main sources of heterogeneity in the tumor microenvironment (TME) from lung cancer transcriptomics. We demonstrate that this signature, which we called iHet, is conserved in different cancers and associated with antitumor immunity. Through analysis of single-cell and spatial transcriptomics data, we trace back the cellular origin of the variability explaining the iHet signature. Finally, we demonstrate that iHet has predictive value for cancer immunotherapy, which can be further improved by disentangling three major determinants of anticancer immune responses: activity of immune cells, immune infiltration or exclusion, and cancer-cell foreignness. This work shows how transcriptomics data can be integrated to derive a holistic representation of the phenotypic heterogeneity of the TME and to predict its unfolding and fate during immunotherapy with immune checkpoint blockers.

Cell type-specific Multi-Omics Analysis of Cocaine Use Disorder in the Human Caudate Nucleus

Structural and functional alterations in the brain's reward circuitry are present in cocaine use disorder (CocUD), but their molecular underpinnings remain unclear. To investigate these mechanisms, we performed single-nuclei multiome profiling on postmortem caudate nucleus tissue from six individuals with CocUD and eight controls. We profiled 31,178 nuclei, identifying 13 cell types including D1- and D2-medium spiny neurons (MSNs) and glial cells. We observed 1,383 differentially regulated genes and 10,235 differentially accessible peaks, with alterations in MSNs and astrocytes related to neurotransmitter activity and synapse organization. Gene regulatory network analysis identified the transcription factor ZEB1 as exhibiting distinct CocUD-specific subclusters, activating downstream expression of ion- and calcium-channels in MSNs. Further, PDE10A emerged as a potential drug target, showing conserved effects in a rat model. This study highlights cell type-specific molecular alterations in CocUD and provides targets for further investigation, demonstrating the value of multi-omics approaches in addiction research.

Phenotyping Tumor Heterogeneity through Proteogenomics: Study Models and Challenges

Tumor heterogeneity refers to the diversity observed among tumor cells: both between different tumors (inter-tumor heterogeneity) and within a single tumor (intra-tumor heterogeneity). These cells can display distinct morphological and phenotypic characteristics, including variations in cellular morphology, metastatic potential and variability treatment responses among patients. Therefore, a comprehensive understanding of such heterogeneity is necessary for deciphering tumor-specific mechanisms that may be diagnostically and therapeutically valuable. Innovative and multidisciplinary approaches are needed to understand this complex feature. In this context, proteogenomics has been emerging as a significant resource for integrating omics fields such as genomics and proteomics. By combining data obtained from both Next-Generation Sequencing (NGS) technologies and mass spectrometry (MS) analyses, proteogenomics aims to provide a comprehensive view of tumor heterogeneity. This approach reveals molecular alterations and phenotypic features related to tumor subtypes, potentially identifying therapeutic biomarkers. Many achievements have been made; however, despite continuous advances in proteogenomics-based methodologies, several challenges remain: in particular the limitations in sensitivity and specificity and the lack of optimal study models. This review highlights the impact of proteogenomics on characterizing tumor phenotypes, focusing on the critical challenges and current limitations of its use in different clinical and preclinical models for tumor phenotypic characterization.

Bootstrap Evaluation of Association Matrices (BEAM) for Integrating Multiple Omics Profiles with Multiple Outcomes

Motivation

Large datasets containing multiple clinical and omics measurements for each subject motivate the development of new statistical methods to integrate these data to advance scientific discovery.

Model

We propose bootstrap evaluation of association matrices (BEAM), which integrates multiple omics profiles with multiple clinical endpoints. BEAM associates a set omic features with clinical endpoints via regression models and then uses bootstrap resampling to determine statistical significance of the set. Unlike existing methods, BEAM uniquely accommodates an arbitrary number of omic profiles and endpoints.

Results

In simulations, BEAM performed similarly to the theoretically best simple test and outperformed other integrated analysis methods. In an example pediatric leukemia application, BEAM identified several genes with biological relevance established by a CRISPR assay that had been missed by univariate screens and other integrated analysis methods. Thus, BEAM is a powerful, flexible, and robust tool to identify genes for further laboratory and/or clinical research evaluation.

Availability

Source code, documentation, and a vignette for BEAM are available on GitHub at: https://github.com/annaSeffernick/BEAMR. The R package is available from CRAN at: https://cran.r-project.org/package=BEAMR.

Contact

Stanley.Pounds@stjude.org.

Supplementary Information

Supplementary data are available at the journal's website.

Self-Guided Partial Graph Propagation for Incomplete Multiview Clustering

In this work, we study a more realistic challenging scenario in multiview clustering (MVC), referred to as incomplete MVC (IMVC) where some instances in certain views are missing. The key to IMVC is how to adequately exploit complementary and consistency information under the incompleteness of data. However, most existing methods address the incompleteness problem at the instance level and they require sufficient information to perform data recovery. In this work, we develop a new approach to facilitate IMVC based on the graph propagation perspective. Specifically, a partial graph is used to describe the similarity of samples for incomplete views, such that the issue of missing instances can be translated into the missing entries of the partial graph. In this way, a common graph can be adaptively learned to self-guide the propagation process by exploiting the consistency information, and the propagated graph of each view is in turn used to refine the common self-guided graph in an iterative manner. Thus, the associated missing entries can be inferred through graph propagation by exploiting the consistency information across all views. On the other hand, existing approaches focus on the consistency structure only, and the complementary information has not been sufficiently exploited due to the data incompleteness issue. By contrast, under the proposed graph propagation framework, an exclusive regularization term can be naturally adopted to exploit the complementary information in our method. Extensive experiments demonstrate the effectiveness of the proposed method in comparison with state-of-the-art methods. The source code of our method is available at the https://github.com/CLiu272/TNNLS-PGP.

Autophagy and machine learning: Unanswered questions

Autophagy is a critical conserved cellular process in maintaining cellular homeostasis by clearing and recycling damaged organelles and intracellular components in lysosomes and vacuoles. Autophagy plays a vital role in cell survival, bioenergetic homeostasis, organism development, and cell death regulation. Malfunctions in autophagy are associated with various human diseases and health disorders, such as cancers and neurodegenerative diseases. Significant effort has been devoted to autophagy-related research in the context of genes, proteins, diagnosis, etc. In recent years, there has been a surge of studies utilizing state of the art machine learning (ML) tools to analyze and understand the roles of autophagy in various biological processes. We taxonomize ML techniques that are applicable in an autophagy context, comprehensively review existing efforts being taken in this direction, and outline principles to consider in a biomedical context. In recognition of recent groundbreaking advances in the deep-learning community, we discuss new opportunities in interdisciplinary collaborations and seek to engage autophagy and computer science researchers to promote autophagy research with joint efforts.

COPS: A novel platform for multi-omic disease subtype discovery via robust multi-objective evaluation of clustering algorithms

Recent research on multi-view clustering algorithms for complex disease subtyping often overlooks aspects like clustering stability and critical assessment of prognostic relevance. Furthermore, current frameworks do not allow for a comparison between data-driven and pathway-driven clustering, highlighting a significant gap in the methodology. We present the COPS R-package, tailored for robust evaluation of single and multi-omics clustering results. COPS features advanced methods, including similarity networks, kernel-based approaches, dimensionality reduction, and pathway knowledge integration. Some of these methods are not accessible through R, and some correspond to new approaches proposed with COPS. Our framework was rigorously applied to multi-omics data across seven cancer types, including breast, prostate, and lung, utilizing mRNA, CNV, miRNA, and DNA methylation data. Unlike previous studies, our approach contrasts data- and knowledge-driven multi-view clustering methods and incorporates cross-fold validation for robustness. Clustering outcomes were assessed using the ARI score, survival analysis via Cox regression models including relevant covariates, and the stability of the results. While survival analysis and gold-standard agreement are standard metrics, they vary considerably across methods and datasets. Therefore, it is essential to assess multi-view clustering methods using multiple criteria, from cluster stability to prognostic relevance, and to provide ways of comparing these metrics simultaneously to select the optimal approach for disease subtype discovery in novel datasets. Emphasizing multi-objective evaluation, we applied the Pareto efficiency concept to gauge the equilibrium of evaluation metrics in each cancer case-study. Affinity Network Fusion, Integrative Non-negative Matrix Factorization, and Multiple Kernel K-Means with linear or Pathway Induced Kernels were the most stable and effective in discerning groups with significantly different survival outcomes in several case studies.

Multi-omics integration for both single-cell and spatially resolved data based on dual-path graph attention auto-encoder

Single-cell multi-omics integration enables joint analysis at the single-cell level of resolution to provide more accurate understanding of complex biological systems, while spatial multi-omics integration is benefit to the exploration of cell spatial heterogeneity to facilitate more comprehensive downstream analyses. Existing methods are mainly designed for single-cell multi-omics data with little consideration of spatial information and still have room for performance improvement. A reliable multi-omics integration method designed for both single-cell and spatially resolved data is necessary and significant. We propose a multi-omics integration method based on dual-path graph attention auto-encoder (SSGATE). It can construct the neighborhood graphs based on single-cell expression profiles or spatial coordinates, enabling it to process single-cell data and utilize spatial information from spatially resolved data. It can also perform self-supervised learning for integration through the graph attention auto-encoders from two paths. SSGATE is applied to integration of transcriptomics and proteomics, including single-cell and spatially resolved data of various tissues from different sequencing technologies. SSGATE shows better performance and stronger robustness than competitive methods and facilitates downstream analysis.

Multimodal functional deep learning for multiomics data

With rapidly evolving high-throughput technologies and consistently decreasing costs, collecting multimodal omics data in large-scale studies has become feasible. Although studying multiomics provides a new comprehensive approach in understanding the complex biological mechanisms of human diseases, the high dimensionality of omics data and the complexity of the interactions among various omics levels in contributing to disease phenotypes present tremendous analytical challenges. There is a great need of novel analytical methods to address these challenges and to facilitate multiomics analyses. In this paper, we propose a multimodal functional deep learning (MFDL) method for the analysis of high-dimensional multiomics data. The MFDL method models the complex relationships between multiomics variants and disease phenotypes through the hierarchical structure of deep neural networks and handles high-dimensional omics data using the functional data analysis technique. Furthermore, MFDL leverages the structure of the multimodal model to capture interactions between different types of omics data. Through simulation studies and real-data applications, we demonstrate the advantages of MFDL in terms of prediction accuracy and its robustness to the high dimensionality and noise within the data.

A practical introduction to holo-omics

Holo-omics refers to the joint study of non-targeted molecular data layers from host-microbiota systems or holobionts, which is increasingly employed to disentangle the complex interactions between the elements that compose them. We navigate through the generation, analysis, and integration of omics data, focusing on the commonalities and main differences to generate and analyze the various types of omics, with a special focus on optimizing data generation and integration. We advocate for careful generation and distillation of data, followed by independent exploration and analyses of the single omic layers to obtain a better understanding of the study system, before the integration of multiple omic layers in a final model is attempted. We highlight critical decision points to achieve this aim and flag the main challenges to address complex biological questions regarding the integrative study of host-microbiota relationships.

Enhanced Gut Microbiome Capacity for Amino Acid Metabolism is associated with Peanut Oral Immunotherapy Failure

Peanut Oral Immunotherapy (POIT) holds promise for remission of peanut allergy, though treatment is protracted and successful in only a subset of patients. Because the gut microbiome is linked to food allergy, we sought to identify fecal microbial predictors of POIT efficacy and to develop mechanistic insights into treatment response. Longitudinal functional analysis of the fecal microbiome of children (n=79) undergoing POIT in a first double-blind, placebo-controlled clinical trial, identified five microbial-derived bile acids enriched in fecal samples prior to POIT initiation that predicted treatment efficacy (AUC 0.71). Failure to induce disease remission was associated with a distinct fecal microbiome with enhanced capacity for bile acid deconjugation, amino acid metabolism, and increased peanut peptide degradation in vitro . Thus, microbiome mechanisms of POIT failure appear to include depletion of immunomodulatory secondary bile and amino acids and the antigenic peanut peptides necessary to promote peanut allergy desensitization and remission.

Understanding pulmonary hypertension: the need for an integrative metabolomics and transcriptomics approach

Pulmonary hypertension (PH), characterised by mean pulmonary arterial pressure (mPAP) >20 mm Hg at rest, is a complex pathophysiological disorder associated with multiple clinical conditions. The high prevalence of the disease along with increased mortality and morbidity makes it a global health burden. Despite major advances in understanding the disease pathophysiology, much of the underlying complex molecular mechanism remains to be elucidated. Lack of a robust diagnostic test and specific therapeutic targets also poses major challenges. This review provides a comprehensive update on the dysregulated pathways and promising candidate markers identified in PH patients using the transcriptomics and metabolomics approach. The review also highlights the need of using an integrative multi-omics approach for obtaining insight into the disease at a molecular level. The integrative multi-omics/pan-omics approach envisaged to help in bridging the gap from genotype to phenotype is outlined. Finally, the challenges commonly encountered while conducting omics-driven studies are also discussed.

Panpipes: a pipeline for multiomic single-cell and spatial transcriptomic data analysis

Single-cell multiomic analysis of the epigenome, transcriptome, and proteome allows for comprehensive characterization of the molecular circuitry that underpins cell identity and state. However, the holistic interpretation of such datasets presents a challenge given a paucity of approaches for systematic, joint evaluation of different modalities. Here, we present Panpipes, a set of computational workflows designed to automate multimodal single-cell and spatial transcriptomic analyses by incorporating widely-used Python-based tools to perform quality control, preprocessing, integration, clustering, and reference mapping at scale. Panpipes allows reliable and customizable analysis and evaluation of individual and integrated modalities, thereby empowering decision-making before downstream investigations.

Navigating Challenges and Opportunities in Multi-Omics Integration for Personalized Healthcare

The field of multi-omics has witnessed unprecedented growth, converging multiple scientific disciplines and technological advances. This surge is evidenced by a more than doubling in multi-omics scientific publications within just two years (2022-2023) since its first referenced mention in 2002, as indexed by the National Library of Medicine. This emerging field has demonstrated its capability to provide comprehensive insights into complex biological systems, representing a transformative force in health diagnostics and therapeutic strategies. However, several challenges are evident when merging varied omics data sets and methodologies, interpreting vast data dimensions, streamlining longitudinal sampling and analysis, and addressing the ethical implications of managing sensitive health information. This review evaluates these challenges while spotlighting pivotal milestones: the development of targeted sampling methods, the use of artificial intelligence in formulating health indices, the integration of sophisticated n-of-1 statistical models such as digital twins, and the incorporation of blockchain technology for heightened data security. For multi-omics to truly revolutionize healthcare, it demands rigorous validation, tangible real-world applications, and smooth integration into existing healthcare infrastructures. It is imperative to address ethical dilemmas, paving the way for the realization of a future steered by omics-informed personalized medicine.

Review: Cancer and neurodevelopmental disorders: multi-scale reasoning and computational guide

The connection and causality between cancer and neurodevelopmental disorders have been puzzling. How can the same cellular pathways, proteins, and mutations lead to pathologies with vastly different clinical presentations? And why do individuals with neurodevelopmental disorders, such as autism and schizophrenia, face higher chances of cancer emerging throughout their lifetime? Our broad review emphasizes the multi-scale aspect of this type of reasoning. As these examples demonstrate, rather than focusing on a specific organ system or disease, we aim at the new understanding that can be gained. Within this framework, our review calls attention to computational strategies which can be powerful in discovering connections, causalities, predicting clinical outcomes, and are vital for drug discovery. Thus, rather than centering on the clinical features, we draw on the rapidly increasing data on the molecular level, including mutations, isoforms, three-dimensional structures, and expression levels of the respective disease-associated genes. Their integrated analysis, together with chromatin states, can delineate how, despite being connected, neurodevelopmental disorders and cancer differ, and how the same mutations can lead to different clinical symptoms. Here, we seek to uncover the emerging connection between cancer, including pediatric tumors, and neurodevelopmental disorders, and the tantalizing questions that this connection raises.

Pathobiological signatures of dysbiotic lung injury in pediatric patients undergoing stem cell transplantation

Hematopoietic cell transplantation (HCT) uses cytotoxic chemotherapy and/or radiation followed by intravenous infusion of stem cells to cure malignancies, bone marrow failure and inborn errors of immunity, hemoglobin and metabolism. Lung injury is a known complication of the process, due in part to disruption in the pulmonary microenvironment by insults such as infection, alloreactive inflammation and cellular toxicity. How microorganisms, immunity and the respiratory epithelium interact to contribute to lung injury is uncertain, limiting the development of prevention and treatment strategies. Here we used 278 bronchoalveolar lavage (BAL) fluid samples to study the lung microenvironment in 229 pediatric patients who have undergone HCT treated at 32 children's hospitals between 2014 and 2022. By leveraging paired microbiome and human gene expression data, we identified high-risk BAL compositions associated with in-hospital mortality (P = 0.007). Disadvantageous profiles included bacterial overgrowth with neutrophilic inflammation, microbiome contraction with epithelial fibroproliferation and profound commensal depletion with viral and staphylococcal enrichment, lymphocytic activation and cellular injury, and were replicated in an independent cohort from the Netherlands (P = 0.022). In addition, a broad array of previously occult pathogens was identified, as well as a strong link between antibiotic exposure, commensal bacterial depletion and enrichment of viruses and fungi. Together these lung-immune system-microorganism interactions clarify the important drivers of fatal lung injury in pediatric patients who have undergone HCT. Further investigation is needed to determine how personalized interpretation of heterogeneous pulmonary microenvironments may be used to improve pediatric HCT outcomes.

Probabilistic pathway-based multimodal factor analysis

MOTIVATION

Multimodal profiling strategies promise to produce more informative insights into biomedical cohorts via the integration of the information each modality contributes. To perform this integration, however, the development of novel analytical strategies is needed. Multimodal profiling strategies often come at the expense of lower sample numbers, which can challenge methods to uncover shared signals across a cohort. Thus, factor analysis approaches are commonly used for the analysis of high-dimensional data in molecular biology, however, they typically do not yield representations that are directly interpretable, whereas many research questions often center around the analysis of pathways associated with specific observations.

RESULTS

We develop PathFA, a novel approach for multimodal factor analysis over the space of pathways. PathFA produces integrative and interpretable views across multimodal profiling technologies, which allow for the derivation of concrete hypotheses. PathFA combines a pathway-learning approach with integrative multimodal capability under a Bayesian procedure that is efficient, hyper-parameter free, and able to automatically infer observation noise from the data. We demonstrate strong performance on small sample sizes within our simulation framework and on matched proteomics and transcriptomics profiles from real tumor samples taken from the Swiss Tumor Profiler consortium. On a subcohort of melanoma patients, PathFA recovers pathway activity that has been independently associated with poor outcome. We further demonstrate the ability of this approach to identify pathways associated with the presence of specific cell-types as well as tumor heterogeneity. Our results show that we capture known biology, making it well suited for analyzing multimodal sample cohorts.

AVAILABILITY AND IMPLEMENTATION

The tool is implemented in python and available at https://github.com/ratschlab/path-fa.

Charting a course for global progress in PIDs by 2030 - proceedings from the IPOPI global multi-stakeholders' summit (September 2023)

The International Patient Organisation for Primary Immunodeficiencies (IPOPI) held its second Global Multi-Stakeholders' Summit, an annual stimulating and forward-thinking meeting uniting experts to anticipate pivotal upcoming challenges and opportunities in the field of primary immunodeficiency (PID). The 2023 summit focused on three key identified discussion points: (i) How can immunoglobulin (Ig) therapy meet future personalized patient needs? (ii) Pandemic preparedness: what's next for public health and potential challenges for the PID community? (iii) Diagnosing PIDs in 2030: what needs to happen to diagnose better and to diagnose more? Clinician-Scientists, patient representatives and other stakeholders explored avenues to improve Ig therapy through mechanistic insights and tailored Ig preparations/products according to patient-specific needs and local exposure to infectious agents, amongst others. Urgency for pandemic preparedness was discussed, as was the threat of shortage of antibiotics and increasing antimicrobial resistance, emphasizing the need for representation of PID patients and other vulnerable populations throughout crisis and care management. Discussion also covered the complexities of PID diagnosis, addressing issues such as global diagnostic disparities, the integration of patient-reported outcome measures, and the potential of artificial intelligence to increase PID diagnosis rates and to enhance diagnostic precision. These proceedings outline the outcomes and recommendations arising from the 2023 IPOPI Global Multi-Stakeholders' Summit, offering valuable insights to inform future strategies in PID management and care. Integral to this initiative is its role in fostering collaborative efforts among stakeholders to prepare for the multiple challenges facing the global PID community.

Predicting type 2 diabetes via machine learning integration of multiple omics from human pancreatic islets

Type 2 diabetes (T2D) is the fastest growing non-infectious disease worldwide. Impaired insulin secretion from pancreatic beta-cells is a hallmark of T2D, but the mechanisms behind this defect are insufficiently characterized. Integrating multiple layers of biomedical information, such as different Omics, may allow more accurate understanding of complex diseases such as T2D. Our aim was to explore and use Machine Learning to integrate multiple sources of biological/molecular information (multiOmics), in our case RNA-sequening, DNA methylation, SNP and phenotypic data from islet donors with T2D and non-diabetic controls. We exploited Machine Learning to perform multiOmics integration of DNA methylation, expression, SNPs, and phenotypes from pancreatic islets of 110 individuals, with ~ 30% being T2D cases. DNA methylation was analyzed using Infinium MethylationEPIC array, expression was analyzed using RNA-sequencing, and SNPs were analyzed using HumanOmniExpress arrays. Supervised linear multiOmics integration via DIABLO based on Partial Least Squares (PLS) achieved an accuracy of 91 ± 15% of T2D prediction with an area under the curve of 0.96 ± 0.08 on the test dataset after cross-validation. Biomarkers identified by this multiOmics integration, including SACS and TXNIP DNA methylation, OPRD1 and RHOT1 expression and a SNP annotated to ANO1, provide novel insights into the interplay between different biological mechanisms contributing to T2D. This Machine Learning approach of multiOmics cross-sectional data from human pancreatic islets achieved a promising accuracy of T2D prediction, which may potentially find broad applications in clinical diagnostics. In addition, it delivered novel candidate biomarkers for T2D and links between them across the different Omics.

Gut microbiota-metabolite interactions meditate the effect of dietary patterns on precocious puberty

Precocious puberty, a pediatric endocrine disorder classified as central precocious puberty (CPP) or peripheral precocious puberty (PPP), is influenced by diet, gut microbiota, and metabolites, but the specific mechanisms remain unclear. Our study found that increased alpha-diversity and abundance of short-chain fatty acid-producing bacteria led to elevated levels of luteinizing hormone and follicle-stimulating hormone, contributing to precocious puberty. The integration of specific microbiota and metabolites has potential diagnostic value for precocious puberty. The Prevotella genus-controlled interaction factor, influenced by complex carbohydrate consumption, mediated a reduction in estradiol levels. Interactions between obesity-related bacteria and metabolites mediated the beneficial effect of seafood in reducing luteinizing hormone levels, reducing the risk of obesity-induced precocious puberty, and preventing progression from PPP to CPP. This study provides valuable insights into the complex interplay between diet, gut microbiota and metabolites in the onset, development and clinical classification of precocious puberty and warrants further investigation.

Multiomic ALS signatures highlight subclusters and sex differences suggesting the MAPK pathway as therapeutic target

Amyotrophic lateral sclerosis (ALS) is a debilitating motor neuron disease and lacks effective disease-modifying treatments. This study utilizes a comprehensive multiomic approach to investigate the early and sex-specific molecular mechanisms underlying ALS. By analyzing the prefrontal cortex of 51 patients with sporadic ALS and 50 control subjects, alongside four transgenic mouse models (C9orf72-, SOD1-, TDP-43-, and FUS-ALS), we have uncovered significant molecular alterations associated with the disease. Here, we show that males exhibit more pronounced changes in molecular pathways compared to females. Our integrated analysis of transcriptomes, (phospho)proteomes, and miRNAomes also identified distinct ALS subclusters in humans, characterized by variations in immune response, extracellular matrix composition, mitochondrial function, and RNA processing. The molecular signatures of human subclusters were reflected in specific mouse models. Our study highlighted the mitogen-activated protein kinase (MAPK) pathway as an early disease mechanism. We further demonstrate that trametinib, a MAPK inhibitor, has potential therapeutic benefits in vitro and in vivo, particularly in females, suggesting a direction for developing targeted ALS treatments.

Statistical and computational methods for integrating microbiome, host genomics, and metabolomics data

Large-scale microbiome studies are progressively utilizing multiomics designs, which include the collection of microbiome samples together with host genomics and metabolomics data. Despite the increasing number of data sources, there remains a bottleneck in understanding the relationships between different data modalities due to the limited number of statistical and computational methods for analyzing such data. Furthermore, little is known about the portability of general methods to the metagenomic setting and few specialized techniques have been developed. In this review, we summarize and implement some of the commonly used methods. We apply these methods to real data sets where shotgun metagenomic sequencing and metabolomics data are available for microbiome multiomics data integration analysis. We compare results across methods, highlight strengths and limitations of each, and discuss areas where statistical and computational innovation is needed.

Multi-omics data integration and drug screening of AML cancer using Generative Adversarial Network

In the era of precision medicine, accurate disease phenotype prediction for heterogeneous diseases, such as cancer, is emerging due to advanced technologies that link genotypes and phenotypes. However, it is difficult to integrate different types of biological data because they are so varied. In this study, we focused on predicting the traits of a blood cancer called Acute Myeloid Leukemia (AML) by combining different kinds of biological data. We used a recently developed method called Omics Generative Adversarial Network (GAN) to better classify cancer outcomes. The primary advantages of a GAN include its ability to create synthetic data that is nearly indistinguishable from real data, its high flexibility, and its wide range of applications, including multi-omics data analysis. In addition, the GAN was effective at combining two types of biological data. We created synthetic datasets for gene activity and DNA methylation. Our method was more accurate in predicting disease traits than using the original data alone. The experimental results provided evidence that the creation of synthetic data through interacting multi-omics data analysis using GANs improves the overall prediction quality. Furthermore, we identified the top-ranked significant genes through statistical methods and pinpointed potential candidate drug agents through in-silico studies. The proposed drugs, also supported by other independent studies, might play a crucial role in the treatment of AML cancer. The code is available on GitHub; https://github.com/SabrinAfroz/omicsGAN_codes?fbclid=IwAR1-/stuffmlE0hyWgSu2wlXo6dYlKUei3faLdlvpxTOOUPVlmYCloXf4Uk9ejK4I.

Multiomic analyses uncover immunological signatures in acute and chronic coronary syndromes

Acute and chronic coronary syndromes (ACS and CCS) are leading causes of mortality. Inflammation is considered a key pathogenic driver of these diseases, but the underlying immune states and their clinical implications remain poorly understood. Multiomic factor analysis (MOFA) allows unsupervised data exploration across multiple data types, identifying major axes of variation and associating these with underlying molecular processes. We hypothesized that applying MOFA to multiomic data obtained from blood might uncover hidden sources of variance and provide pathophysiological insights linked to clinical needs. Here we compile a longitudinal multiomic dataset of the systemic immune landscape in both ACS and CCS (n = 62 patients in total, n = 15 women and n = 47 men) and validate this in an external cohort (n = 55 patients in total, n = 11 women and n = 44 men). MOFA reveals multicellular immune signatures characterized by distinct monocyte, natural killer and T cell substates and immune-communication pathways that explain a large proportion of inter-patient variance. We also identify specific factors that reflect disease state or associate with treatment outcome in ACS as measured using left ventricular ejection fraction. Hence, this study provides proof-of-concept evidence for the ability of MOFA to uncover multicellular immune programs in cardiovascular disease, opening new directions for mechanistic, biomarker and therapeutic studies.

Transkingdom Network Analysis (TkNA): a systems framework for inferring causal factors underlying host-microbiota and other multi-omic interactions

We present Transkingdom Network Analysis (TkNA), a unique causal-inference analytical framework that offers a holistic view of biological systems by integrating data from multiple cohorts and diverse omics types. TkNA helps to decipher key players and mechanisms governing host-microbiota (or any multi-omic data) interactions in specific conditions or diseases. TkNA reconstructs a network that represents a statistical model capturing the complex relationships between different omics in the biological system. It identifies robust and reproducible patterns of fold change direction and correlation sign across several cohorts to select differential features and their per-group correlations. The framework then uses causality-sensitive metrics, statistical thresholds and topological criteria to determine the final edges forming the transkingdom network. With the subsequent network's topological features, TkNA identifies nodes controlling a given subnetwork or governing communication between kingdoms and/or subnetworks. The computational time for the millions of correlations necessary for network reconstruction in TkNA typically takes only a few minutes, varying with the study design. Unlike most other multi-omics approaches that find only associations, TkNA focuses on establishing causality while accounting for the complex structure of multi-omic data. It achieves this without requiring huge sample sizes. Moreover, the TkNA protocol is user friendly, requiring minimal installation and basic familiarity with Unix. Researchers can access the TkNA software at https://github.com/CAnBioNet/TkNA/ .

Correlation-based network integration of lung RNA sequencing and DNA methylation data in chronic obstructive pulmonary disease

Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous, chronic inflammatory process of the lungs and, like other complex diseases, is caused by both genetic and environmental factors. Detailed understanding of the molecular mechanisms of complex diseases requires the study of the interplay among different biomolecular layers, and thus the integration of different omics data types. In this study, we investigated COPD-associated molecular mechanisms through a correlation-based network integration of lung tissue RNA-seq and DNA methylation data of COPD cases (n = 446) and controls (n = 346) derived from the Lung Tissue Research Consortium. First, we performed a SWIM-network based analysis to build separate correlation networks for RNA-seq and DNA methylation data for our case-control study population. Then, we developed a method to integrate the results into a coupled network of differentially expressed and differentially methylated genes to investigate their relationships across both molecular layers. The functional enrichment analysis of the nodes of the coupled network revealed a strikingly significant enrichment in Immune System components, both innate and adaptive, as well as immune-system component communication (interleukin and cytokine-cytokine signaling). Our analysis allowed us to reveal novel putative COPD-associated genes and to analyze their relationships, both at the transcriptomics and epigenomics levels, thus contributing to an improved understanding of COPD pathogenesis.

Gut microbiome remodeling and metabolomic profile improves in response to protein pacing with intermittent fasting versus continuous caloric restriction

The gut microbiome (GM) modulates body weight/composition and gastrointestinal functioning; therefore, approaches targeting resident gut microbes have attracted considerable interest. Intermittent fasting (IF) and protein pacing (P) regimens are effective in facilitating weight loss (WL) and enhancing body composition. However, the interrelationships between IF- and P-induced WL and the GM are unknown. The current randomized controlled study describes distinct fecal microbial and plasma metabolomic signatures between combined IF-P (n = 21) versus a heart-healthy, calorie-restricted (CR, n = 20) diet matched for overall energy intake in free-living human participants (women = 27; men = 14) with overweight/obesity for 8 weeks. Gut symptomatology improves and abundance of Christensenellaceae microbes and circulating cytokines and amino acid metabolites favoring fat oxidation increase with IF-P (p < 0.05), whereas metabolites associated with a longevity-related metabolic pathway increase with CR (p < 0.05). Differences indicate GM and metabolomic factors play a role in WL maintenance and body composition. This novel work provides insight into the GM and metabolomic profile of participants following an IF-P or CR diet and highlights important differences in microbial assembly associated with WL and body composition responsiveness. These data may inform future GM-focused precision nutrition recommendations using larger sample sizes of longer duration. Trial registration, March 6, 2020 (ClinicalTrials.gov as NCT04327141), based on a previous randomized intervention trial.