Predicting gastrointestinal drug effects using contextualized metabolic models

Towards a hybrid model-driven platform based on flux balance analysis and a machine learning pipeline for biosystem design

Metabolic modeling and machine learning (ML) are crucial components of the evolving next-generation tools in systems and synthetic biology, aiming to unravel the intricate relationship between genotype, phenotype, and the environment. Nonetheless, the comprehensive exploration of integrating these two frameworks, and fully harnessing the potential of fluxomic data, remains an unexplored territory. In this study, we present, rigorously evaluate, and compare ML-based techniques for data integration. The hybrid model revealed that the overexpression of six target genes and the knockout of seven target genes contribute to enhanced ethanol production. Specifically, we investigated the influence of succinate dehydrogenase (SDH) on ethanol biosynthesis in Saccharomyces cerevisiae through shake flask experiments. The findings indicate a noticeable increase in ethanol yield, ranging from 6 % to 10 %, in SDH subunit gene knockout strains compared to the wild-type strain. Moreover, in pursuit of a high-yielding strain for ethanol production, dual-gene deletion experiments were conducted targeting glycerol-3-phosphate dehydrogenase (GPD) and SDH. The results unequivocally demonstrate significant enhancements in ethanol production for the engineered strains Δsdh4Δgpd1, Δsdh5Δgpd1, Δsdh6Δgpd1, Δsdh4Δgpd2, Δsdh5Δgpd2, and Δsdh6Δgpd2, with improvements of 21.6 %, 27.9 %, and 22.7 %, respectively. Overall, the results highlighted that integrating mechanistic flux features substantially improves the prediction of gene knockout strains not accounted for in metabolic reconstructions. In addition, the finding in this study delivers valuable tools for comprehending and manipulating intricate phenotypes, thereby enhancing prediction accuracy and facilitating deeper insights into mechanistic aspects within the field of synthetic biology.

Direct-acting antiviral agent use and gastrointestinal safety in patients with chronic hepatitis C: a pharmacovigilance study based on FDA Adverse Event Reporting System

BACKGROUND

Gastrointestinal adverse drug reactions (GADRs) of direct-acting antiviral agents (DAAs) in patients with chronic hepatitis C are underestimated.

AIM

This study aimed to comprehensively evaluate the gastrointestinal safety of DAAs in patients with chronic hepatitis C.

METHOD

The US FDA Adverse Event Reporting System database was searched for GADR cases reported from 01 to 2012 to 30 September 2021. Twelve DAA types used for hepatitis C virus were included. The top 30 GADRs were assessed based on the use of DAAs, number of cases, and clinical features. A case-non-case disproportionality approach was used to confirm pharmacovigilance signals, whereby reporting odds ratios (ROR) with 95% CI were calculated.

RESULTS

Nausea (70.01/1000), diarrhoea (39.10/1000), and vomiting (31.68/1000) accounted for the highest number of cases. The pooled median time-to-onset of the top 30 GADRs was 13 days (Q1-Q3: 2-38) and the proportion of drug discontinuation was 19.17%. The highest number of DAA-related cases involved ledipasvir/sofosbuvir (21.86%), sofosbuvir/velpatasvir (21.77%), and sofosbuvir (13.41%). When DAAs were considered as a class drug, after adjusting for age, sex, concomitant diseases and drugs that potentially induced GADRs, significant RORs for specific GADRs were noted, including abdominal discomfort (1.62, 95% CI 1.32-1.99), constipation (1.54, 95% CI 1.26-1.89), dyspepsia (1.25, 95% CI 1.01-1.55), abdominal distension (1.36, 95% CI 1.05-1.75), faeces discoloured (1.77, 95% CI 1.15-2.73), and gastric ulcer (2.37, 95% CI 1.28-4.41).

CONCLUSION

Clinicians should have a deeper understanding of GADRs to improve the gastrointestinal tolerance of patients with chronic hepatitis C.

Whole-genome sequencing and genome-scale metabolic modeling of Chromohalobacter canadensis 85B to explore its salt tolerance and biotechnological use

Salt tolerant organisms are increasingly being used for the industrial production of high-value biomolecules due to their better adaptability compared to mesophiles. Chromohalobacter canadensis is one of the early halophiles to show promising biotechnology potential, which has not been explored to date. Advanced high throughput technologies such as whole-genome sequencing allow in-depth insight into the potential of organisms while at the frontiers of systems biology. At the same time, genome-scale metabolic models (GEMs) enable phenotype predictions through a mechanistic representation of metabolism. Here, we sequence and analyze the genome of C. canadensis 85B, and we use it to reconstruct a GEM. We then analyze the GEM using flux balance analysis and validate it against literature data on C. canadensis. We show that C. canadensis 85B is a metabolically versatile organism with many features for stress and osmotic adaptation. Pathways to produce ectoine and polyhydroxybutyrates were also predicted. The GEM reveals the ability to grow on several carbon sources in a minimal medium and reproduce osmoadaptation phenotypes. Overall, this study reveals insights from the genome of C. canadensis 85B, providing genomic data and a draft GEM that will serve as the first steps towards a better understanding of its metabolism, for novel applications in industrial biotechnology.

Connecting omics signatures and revealing biological mechanisms with iLINCS

There are only a few platforms that integrate multiple omics data types, bioinformatics tools, and interfaces for integrative analyses and visualization that do not require programming skills. Here we present iLINCS ( http://ilincs.org ), an integrative web-based platform for analysis of omics data and signatures of cellular perturbations. The platform facilitates mining and re-analysis of the large collection of omics datasets (>34,000), pre-computed signatures (>200,000), and their connections, as well as the analysis of user-submitted omics signatures of diseases and cellular perturbations. iLINCS analysis workflows integrate vast omics data resources and a range of analytics and interactive visualization tools into a comprehensive platform for analysis of omics signatures. iLINCS user-friendly interfaces enable execution of sophisticated analyses of omics signatures, mechanism of action analysis, and signature-driven drug repositioning. We illustrate the utility of iLINCS with three use cases involving analysis of cancer proteogenomic signatures, COVID 19 transcriptomic signatures and mTOR signaling.

Integrating genome-scale metabolic modelling and transfer learning for human gene regulatory network reconstruction

MOTIVATION

Gene regulation is responsible for controlling numerous physiological functions and dynamically responding to environmental fluctuations. Reconstructing the human network of gene regulatory interactions is thus paramount to understanding the cell functional organization across cell types, as well as to elucidating pathogenic processes and identifying molecular drug targets. Although significant effort has been devoted towards this direction, existing computational methods mainly rely on gene expression levels, possibly ignoring the information conveyed by mechanistic biochemical knowledge. Moreover, except for a few recent attempts, most of the existing approaches only consider the information of the organism under analysis, without exploiting the information of related model organisms.

RESULTS

We propose a novel method for the reconstruction of the human gene regulatory network, based on a transfer learning strategy that synergically exploits information from human and mouse, conveyed by gene-related metabolic features generated in silico from gene expression data. Specifically, we learn a predictive model from metabolic activity inferred via tissue-specific metabolic modelling of artificial gene knockouts. Our experiments show that the combination of our transfer learning approach with the constructed metabolic features provides a significant advantage in terms of reconstruction accuracy, as well as additional clues on the contribution of each constructed metabolic feature.

AVAILABILITY AND IMPLEMENTATION

The method, the datasets and all the results obtained in this study are available at: https://doi.org/10.6084/m9.figshare.c.5237687.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A Practical Guide to Integrating Multimodal Machine Learning and Metabolic Modeling

Complex, distributed, and dynamic sets of clinical biomedical data are collectively referred to as multimodal clinical data. In order to accommodate the volume and heterogeneity of such diverse data types and aid in their interpretation when they are combined with a multi-scale predictive model, machine learning is a useful tool that can be wielded to deconstruct biological complexity and extract relevant outputs. Additionally, genome-scale metabolic models (GSMMs) are one of the main frameworks striving to bridge the gap between genotype and phenotype by incorporating prior biological knowledge into mechanistic models. Consequently, the utilization of GSMMs as a foundation for the integration of multi-omic data originating from different domains is a valuable pursuit towards refining predictions. In this chapter, we show how cancer multi-omic data can be analyzed via multimodal machine learning and metabolic modeling. Firstly, we focus on the merits of adopting an integrative systems biology led approach to biomedical data mining. Following this, we propose how constraint-based metabolic models can provide a stable yet adaptable foundation for the integration of multimodal data with machine learning. Finally, we provide a step-by-step tutorial for the combination of machine learning and GSMMs, which includes: (i) tissue-specific constraint-based modeling; (ii) survival analysis using time-to-event prediction for cancer; and (iii) classification and regression approaches for multimodal machine learning. The code associated with the tutorial can be found at https://github.com/Angione-Lab/Tutorials_Combining_ML_and_GSMM .

Protocol for hybrid flux balance, statistical, and machine learning analysis of multi-omic data from the cyanobacterium Synechococcus sp. PCC 7002

Combining a computational framework for flux balance analysis with machine learning improves the accuracy of predicting metabolic activity across conditions, while enabling mechanistic interpretation. This protocol presents a guide to condition-specific metabolic modeling that integrates regularized flux balance analysis with machine learning approaches to extract key features from transcriptomic and fluxomic data. We demonstrate the protocol as applied to Synechococcus sp. PCC 7002; we also outline how it can be adapted to any species or community with available multi-omic data. For complete details on the use and execution of this protocol, please refer to Vijayakumar et al. (2020).

Path to improving the life cycle and quality of genome-scale models of metabolism

Genome-scale models of metabolism (GEMs) are key computational tools for the systems-level study of metabolic networks. Here, we describe the "GEM life cycle," which we subdivide into four stages: inception, maturation, specialization, and amalgamation. We show how different types of GEM reconstruction workflows fit in each stage and proceed to highlight two fundamental bottlenecks for GEM quality improvement: GEM maturation and content removal. We identify common characteristics contributing to increasing quality of maturing GEMs drawing from past independent GEM maturation efforts. We then shed some much-needed light on the latent and unrecognized but pervasive issue of content removal, demonstrating the substantial effects of model pruning on its solution space. Finally, we propose a novel framework for content removal and associated confidence-level assignment which will help guide future GEM development efforts, reduce duplication of effort across groups, potentially aid automated reconstruction platforms, and boost the reproducibility of model development.

Leveraging genome-scale metabolic models for human health applications

Genome-scale metabolic modeling is a scalable and extensible computational method for analyzing and predicting biological function. With the ongoing improvements in computational methods and experimental capabilities, genome-scale metabolic models (GEMs) are demonstrating utility in addressing human health applications. The initial areas of highest impact are likely to be health applications where disease states involve metabolic changes. In this review, we focus on recent application of GEMs to studying cancer and the human microbiome by describing the enabling methodologies and outcomes of these studies. We conclude with proposing some areas of research that are likely to arise as a result of recent methodological advances.

A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth

Metabolic modeling and machine learning are key components in the emerging next generation of systems and synthetic biology tools, targeting the genotype-phenotype-environment relationship. Rather than being used in isolation, it is becoming clear that their value is maximized when they are combined. However, the potential of integrating these two frameworks for omic data augmentation and integration is largely unexplored. We propose, rigorously assess, and compare machine-learning-based data integration techniques, combining gene expression profiles with computationally generated metabolic flux data to predict yeast cell growth. To this end, we create strain-specific metabolic models for 1,143 Saccharomyces cerevisiae mutants and we test 27 machine-learning methods, incorporating state-of-the-art feature selection and multiview learning approaches. We propose a multiview neural network using fluxomic and transcriptomic data, showing that the former increases the predictive accuracy of the latter and reveals functional patterns that are not directly deducible from gene expression alone. We test the proposed neural network on a further 86 strains generated in a different experiment, therefore verifying its robustness to an additional independent dataset. Finally, we show that introducing mechanistic flux features improves the predictions also for knockout strains whose genes were not modeled in the metabolic reconstruction. Our results thus demonstrate that fusing experimental cues with in silico models, based on known biochemistry, can contribute with disjoint information toward biologically informed and interpretable machine learning. Overall, this study provides tools for understanding and manipulating complex phenotypes, increasing both the prediction accuracy and the extent of discernible mechanistic biological insights.

Digital microbiology

BACKGROUND

Digitalization and artificial intelligence have an important impact on the way microbiology laboratories will work in the near future. Opportunities and challenges lie ahead to digitalize the microbiological workflows. Making efficient use of big data, machine learning, and artificial intelligence in clinical microbiology requires a profound understanding of data handling aspects.

OBJECTIVE

This review article summarizes the most important concepts of digital microbiology. The article gives microbiologists, clinicians and data scientists a viewpoint and practical examples along the diagnostic process.

SOURCES

We used peer-reviewed literature identified by a PubMed search for digitalization, machine learning, artificial intelligence and microbiology.

CONTENT

We describe the opportunities and challenges of digitalization in microbiological diagnostic processes with various examples. We also provide in this context key aspects of data structure and interoperability, as well as legal aspects. Finally, we outline the way for applications in a modern microbiology laboratory.

IMPLICATIONS

We predict that digitalization and the usage of machine learning will have a profound impact on the daily routine of laboratory staff. Along the analytical process, the most important steps should be identified, where digital technologies can be applied and provide a benefit. The education of all staff involved should be adapted to prepare for the advances in digital microbiology.