Multi-modal meta-analysis of cancer cell line omics profiles identifies ECHDC1 as a novel breast tumor suppressor

Integrated analysis of genomics and transcriptomics revealed the genetic basis for goaty flavor formation in goat milk

Goat milk exhibits a robust and distinctive "goaty" flavor. However, the underlying genetic basis of goaty flavor remains elusive and requires further elucidation at the genomic level. Through comparative genomics analysis, we identified divergent signatures of certain proteins in goat, sheep, and cow. MMUT has undergone a goat-specific mutation in the B12 binding domain. We observed the goat FASN exhibits nonsynonymous mutations in the acyltransferase domain. Structural variations in these key proteins may enhance the capacity for synthesizing goaty flavor compounds in goat. Integrated omics analysis revealed the catabolism of branched-chain amino acids contributed to the goat milk flavor. Furthermore, we uncovered a regulatory mechanism in which the transcription factor ZNF281 suppresses the expression of the ECHDC1 gene may play a pivotal role in the accumulation of flavor substances in goat milk. These findings provide insights into the genetic basis underlying the formation of goaty flavor in goat milk. STATEMENT OF SIGNIFICANCE: Branched-chain fatty acids (BCFAs) play a crucial role in generating the distinctive "goaty" flavor of goat milk. Whether there is an underlying genetic basis associated with goaty flavor is unknown. To begin deciphering mechanisms of goat milk flavor development, we collected transcriptomic data from mammary tissue of goat, sheep, cow, and buffalo at peak lactation for cross-species transcriptome analysis and downloaded nine publicly available genomes for comparative genomic analysis. Our data indicate that the catabolic pathway of branched-chain amino acids (BCAAs) is under positive selection in the goat genome, and most genes involved in this pathway exhibit significantly higher expression levels in goat mammary tissue compared to other species, which contributes to the development of flavor in goat milk. Furthermore, we have elucidated the regulatory mechanism by which the transcription factor ZNF281 suppresses ECHDC1 gene expression, thereby exerting an important influence on the accumulation of flavor compounds in goat milk. These findings provide insights into the genetic mechanisms underlying flavor formation in goat milk and suggest further research to manipulate the flavor of animal products.

Statistical integration of multi-omics and drug screening data from cell lines

Data integration methods are used to obtain a unified summary of multiple datasets. For multi-modal data, we propose a computational workflow to jointly analyze datasets from cell lines. The workflow comprises a novel probabilistic data integration method, named POPLS-DA, for multi-omics data. The workflow is motivated by a study on synucleinopathies where transcriptomics, proteomics, and drug screening data are measured in affected LUHMES cell lines and controls. The aim is to highlight potentially druggable pathways and genes involved in synucleinopathies. First, POPLS-DA is used to prioritize genes and proteins that best distinguish cases and controls. For these genes, an integrated interaction network is constructed where the drug screen data is incorporated to highlight druggable genes and pathways in the network. Finally, functional enrichment analyses are performed to identify clusters of synaptic and lysosome-related genes and proteins targeted by the protective drugs. POPLS-DA is compared to other single- and multi-omics approaches. We found that HSPA5, a member of the heat shock protein 70 family, was one of the most targeted genes by the validated drugs, in particular by AT1-blockers. HSPA5 and AT1-blockers have been previously linked to α-synuclein pathology and Parkinson's disease, showing the relevance of our findings. Our computational workflow identified new directions for therapeutic targets for synucleinopathies. POPLS-DA provided a larger interpretable gene set than other single- and multi-omic approaches. An implementation based on R and markdown is freely available online.

The impact of violating the independence assumption in meta-analysis on biomarker discovery

With rapid advancements in high-throughput sequencing technologies, massive amounts of "-omics" data are now available in almost every biomedical field. Due to variance in biological models and analytic methods, findings from clinical and biological studies are often not generalizable when tested in independent cohorts. Meta-analysis, a set of statistical tools to integrate independent studies addressing similar research questions, has been proposed to improve the accuracy and robustness of new biological insights. However, it is common practice among biomarker discovery studies using preclinical pharmacogenomic data to borrow molecular profiles of cancer cell lines from one study to another, creating dependence across studies. The impact of violating the independence assumption in meta-analyses is largely unknown. In this study, we review and compare different meta-analyses to estimate variations across studies along with biomarker discoveries using preclinical pharmacogenomics data. We further evaluate the performance of conventional meta-analysis where the dependence of the effects was ignored via simulation studies. Results show that, as the number of non-independent effects increased, relative mean squared error and lower coverage probability increased. Additionally, we also assess potential bias in the estimation of effects for established meta-analysis approaches when data are duplicated and the assumption of independence is violated. Using pharmacogenomics biomarker discovery, we find that treating dependent studies as independent can substantially increase the bias of meta-analyses. Importantly, we show that violating the independence assumption decreases the generalizability of the biomarker discovery process and increases false positive results, a key challenge in precision oncology.

Bipartite network models to design combination therapies in acute myeloid leukaemia

Combination therapy is preferred over single-targeted monotherapies for cancer treatment due to its efficiency and safety. However, identifying effective drug combinations costs time and resources. We propose a method for identifying potential drug combinations by bipartite network modelling of patient-related drug response data, specifically the Beat AML dataset. The median of cell viability is used as a drug potency measurement to reconstruct a weighted bipartite network, model drug-biological sample interactions, and find the clusters of nodes inside two projected networks. Then, the clustering results are leveraged to discover effective multi-targeted drug combinations, which are also supported by more evidence using GDSC and ALMANAC databases. The potency and synergy levels of selective drug combinations are corroborated against monotherapy in three cell lines for acute myeloid leukaemia in vitro. In this study, we introduce a nominal data mining approach to improving acute myeloid leukaemia treatment through combinatorial therapy.

Identifying effective drug combinations to treat cancer is a challenging task, either experimentally or computationally. Here, the authors develop a bipartite network modelling approach to propose drug combination strategies in acute myeloid leukaemia using patient and cell line drug screening data.

Machine learning for multi-omics data integration in cancer

Multi-omics data analysis is an important aspect of cancer molecular biology studies and has led to ground-breaking discoveries. Many efforts have been made to develop machine learning methods that automatically integrate omics data. Here, we review machine learning tools categorized as either general-purpose or task-specific, covering both supervised and unsupervised learning for integrative analysis of multi-omics data. We benchmark the performance of five machine learning approaches using data from the Cancer Cell Line Encyclopedia, reporting accuracy on cancer type classification and mean absolute error on drug response prediction, and evaluating runtime efficiency. This review provides recommendations to researchers regarding suitable machine learning method selection for their specific applications. It should also promote the development of novel machine learning methodologies for data integration, which will be essential for drug discovery, clinical trial design, and personalized treatments.

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Featuring a balance of both biological and technical content

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Categorizing the reviewed tools into general purpose and task-specific

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Performing an independent benchmarking analysis using a publicly available dataset

ECHDC1 knockout mice accumulate ethyl-branched lipids and excrete abnormal intermediates of branched-chain fatty acid metabolism

The cytosolic enzyme ethylmalonyl-CoA decarboxylase (ECHDC1) decarboxylates ethyl- or methyl-malonyl-CoA, two side products of acetyl-CoA carboxylase. These CoA derivatives can be used to synthesize a subset of branched-chain fatty acids (FAs). We previously found that ECHDC1 limits the synthesis of these abnormal FAs in cell lines, but its effects in vivo are unknown. To further evaluate the effects of ECHDC1 deficiency, we generated knockout mice. These mice were viable, fertile, showed normal postnatal growth, and lacked obvious macroscopic and histologic changes. Surprisingly, tissues from wild-type mice already contained methyl-branched FAs due to methylmalonyl-CoA incorporation, but these FAs were only increased in the intraorbital glands of ECHDC1 knockout mice. In contrast, ECHDC1 knockout mice accumulated 16–20-carbon FAs carrying ethyl-branches in all tissues, which were undetectable in wild-type mice. Ethyl-branched FAs were incorporated into different lipids, including acylcarnitines, phosphatidylcholines, plasmanylcholines, and triglycerides. Interestingly, we found a variety of unusual glycine-conjugates in the urine of knockout mice, which included adducts of ethyl-branched compounds in different stages of oxidation. This suggests that the excretion of potentially toxic intermediates of branched-chain FA metabolism might prevent a more dramatic phenotype in these mice. Curiously, ECHDC1 knockout mice also accumulated 2,2-dimethylmalonyl-CoA. This indicates that the broad specificity of ECHDC1 might help eliminate a variety of potentially dangerous branched-chain dicarboxylyl-CoAs. We conclude that ECHDC1 prevents the formation of ethyl-branched FAs and that urinary excretion of glycine-conjugates allows mice to eliminate potentially deleterious intermediates of branched-chain FA metabolism.