Inferring metabolic pathway activity levels from RNA-Seq data

Dynamic factor analysis with dependent Gaussian processes for high-dimensional gene expression trajectories

The increasing availability of high-dimensional, longitudinal measures of gene expression can facilitate understanding of biological mechanisms, as required for precision medicine. Biological knowledge suggests that it may be best to describe complex diseases at the level of underlying pathways, which may interact with one another. We propose a Bayesian approach that allows for characterizing such correlation among different pathways through dependent Gaussian processes (DGP) and mapping the observed high-dimensional gene expression trajectories into unobserved low-dimensional pathway expression trajectories via Bayesian sparse factor analysis. Our proposal is the first attempt to relax the classical assumption of independent factors for longitudinal data and has demonstrated a superior performance in recovering the shape of pathway expression trajectories, revealing the relationships between genes and pathways, and predicting gene expressions (closer point estimates and narrower predictive intervals), as demonstrated through simulations and real data analysis. To fit the model, we propose a Monte Carlo expectation maximization (MCEM) scheme that can be implemented conveniently by combining a standard Markov Chain Monte Carlo sampler and an R package GPFDA,which returns the maximum likelihood estimates of DGP hyperparameters. The modular structure of MCEM makes it generalizable to other complex models involving the DGP model component. Our R package DGP4LCF that implements the proposed approach is available on the Comprehensive R Archive Network (CRAN).

Optimisation Models for Pathway Activity Inference in Cancer

BACKGROUND

With advances in high-throughput technologies, there has been an enormous increase in data related to profiling the activity of molecules in disease. While such data provide more comprehensive information on cellular actions, their large volume and complexity pose difficulty in accurate classification of disease phenotypes. Therefore, novel modelling methods that can improve accuracy while offering interpretable means of analysis are required. Biological pathways can be used to incorporate a priori knowledge of biological interactions to decrease data dimensionality and increase the biological interpretability of machine learning models.

METHODOLOGY

A mathematical optimisation model is proposed for pathway activity inference towards precise disease phenotype prediction and is applied to RNA-Seq datasets. The model is based on mixed-integer linear programming (MILP) mathematical optimisation principles and infers pathway activity as the linear combination of pathway member gene expression, multiplying expression values with model-determined gene weights that are optimised to maximise discrimination of phenotype classes and minimise incorrect sample allocation.

RESULTS

The model is evaluated on the transcriptome of breast and colorectal cancer, and exhibits solution results of good optimality as well as good prediction performance on related cancer subtypes. Two baseline pathway activity inference methods and three advanced methods are used for comparison. Sample prediction accuracy, robustness against noise expression data, and survival analysis suggest competitive prediction performance of our model while providing interpretability and insight on key pathways and genes. Overall, our work demonstrates that the flexible nature of mathematical programming lends itself well to developing efficient computational strategies for pathway activity inference and disease subtype prediction.

Transcriptome Profiling of Stenotrophomonas sp. Strain WZN-1 Reveals Mechanisms of 2,2',4,4'-Tetrabromodiphenyl Ether (BDE-47) Biotransformation

The brominated flame retardant 2,2',4,4'-tetrabromodiphenyl ether (BDE-47) is extensively used, stable, and difficult to degrade in the environment. The existence of BDE-47 could pose a certain risk to the environment and human health. However, the biotransformation mechanisms of BDE-47 by microorganisms remain unclear. In this study, aerobic degradation of BDE-47 by Stenotrophomonas sp. strain WZN-1 and transcriptome analysis were carried out. BDE-47 degradation by Stenotrophomonas sp. strain WZN-1 was mainly through the biological action of intracellular enzymes via the route of debromination and hydroxylation. The results of the transcriptome sequencing indicated the differentially expressed genes were related to transport, metabolism, and stress response. The key processes involved the microbial transmembrane transportation of BDE-47, energy anabolism, synthesis, and metabolism of functional enzymes, stress response, and other biological processes of gene regulation. In particular, bacterial chemotaxis played a potential role in biodegradation of BDE-47 by Stenotrophomonas sp. strain WZN-1. This study provides the first insights into the biotransformation of Stenotrophomonas sp. strain WZN-1 to BED-47 stress and shows potential for application in remediation of polluted environments.

Coupled analysis of transcriptome and BCR mutations reveals role of OXPHOS in affinity maturation

Antigen-activated B cells diversify variable regions of B cell antigen receptors by somatic hypermutation in germinal centers (GCs). The positive selection of GC B cells that acquire high-affinity mutations enables antibody affinity maturation. In spite of considerable progress, the genomic states underlying this process remain to be elucidated. Single-cell RNA sequencing and topic modeling revealed increased expression of the oxidative phosphorylation (OXPHOS) module in GC B cells undergoing mitoses. Coupled analysis of somatic hypermutation in immunoglobulin heavy chain (Igh) variable gene regions showed that GC B cells acquiring higher-affinity mutations had further elevated expression of OXPHOS genes. Deletion of mitochondrial Cox10 in GC B cells resulted in reduced cell division and impaired positive selection. Correspondingly, augmentation of OXPHOS activity with oltipraz promoted affinity maturation. We propose that elevated OXPHOS activity promotes B cell clonal expansion and also positive selection by tuning cell division times.

Multi-layered network-based pathway activity inference using directed random walks: application to predicting clinical outcomes in urologic cancer

MOTIVATION

To better understand the molecular features of cancers, a comprehensive analysis using multiomics data has been conducted. Additionally, a pathway activity inference method has been developed to facilitate the integrative effects of multiple genes. In this respect, we have recently proposed a novel integrative pathway activity inference approach, iDRW, and demonstrated the effectiveness of the method with respect to dichotomizing two survival groups. However, there were several limitations, such as a lack of generality. In this study, we designed a directed gene-gene graph using pathway information by assigning interactions between genes in multiple layers of networks.

RESULTS

: As a proof-of-concept study, it was evaluated using three genomic profiles of urologic cancer patients. The proposed integrative approach achieved improved outcome prediction performances compared with a single genomic profile alone and other existing pathway activity inference methods. The integrative approach also identified common/cancer-specific candidate driver pathways as predictive prognostic features in urologic cancers. Furthermore, it provides better biological insights into the prioritized pathways and genes in an integrated view using a multi-layered gene-gene network. Our framework is not specifically designed for urologic cancers and can be generally applicable for various datasets.

AVAILABILITY

iDRW is implemented as the R software package. The source codes are available at https://github.com/sykim122/iDRW.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Transcriptome Analysis of Pyrus betulaefolia Seedling Root Responses to Short-Term Potassium Deficiency

Potassium (K) plays a crucial role in multiple physiological and developmental processes in plants. Its deficiency is a common abiotic stress that inhibits plant growth and reduces crop productivity. A better understanding of the mechanisms involved in plant responses to low K could help to improve the efficiency of K use in plants. However, such responses remain poorly characterized in fruit tree species such as pears (Pyrus sp). We analyzed the physiological and transcriptome responses of a commonly used pear rootstock, Pyrus betulaefolia, to K-deficiency stress (0 mM). Potassium deprivation resulted in apparent changes in root morphology, with short-term low-K stress resulting in rapidly enhanced root growth. Transcriptome analyses indicated that the root transcriptome was coordinately altered within 6 h after K deprivation, a process that continued until 15 d after treatment. Potassium deprivation resulted in the enhanced expression (up to 5-fold) of a putative high-affinity K+ transporter, PbHAK5 (Pbr037826.1), suggesting the up-regulation of mechanisms associated with K+ acquisition. The enhanced root growth in response to K-deficiency stress was associated with a rapid and sustained decrease in the expression of a transcription factor, PbMYB44 (Pbr015309.1), potentially involved in mediating auxin responses, and the increased expression of multiple genes associated with regulating root growth. The concentrations of several phytohormones including indoleacetic acid (IAA), ABA, ETH, gibberellin (GA3), and jasmonic acid (JA) were higher in response to K deprivation. Furthermore, genes coding for enzymes associated with carbon metabolism such as SORBITOL DEHYDROGENASE (SDH) and SUCROSE SYNTHASE (SUS) displayed greatly enhanced expression in the roots under K deprivation, presumably indicating enhanced metabolism to meet the increased energy demands for growth and K+ acquisition. Together, these data suggest that K deprivation in P. betulaefolia results in the rapid re-programming of the transcriptome to enhance root growth and K+ acquisition. These data provide key insights into the molecular basis for understanding low-K-tolerance mechanisms in pears and in other related fruit trees and identifying potential candidates that warrant further analyses.

Time-course transcriptome analysis reveals the mechanisms of Burkholderia sp. adaptation to high phenol concentrations

Microbial tolerance to phenolic pollutants is the key to their efficient biodegradation. However, the metabolic mechanisms that allow some microorganisms to adapt to high phenol concentrations remain unclear. In this study, to reveal the underlying mechanisms of how Burkholderia sp. adapt to high phenol concentrations, the strain's tolerance ability and time-course transcriptome in combination with cell phenotype were evaluated. Surprisingly, Burkholderia sp. still grew normally after a long adaptation to a relatively high phenol concentration (1500 mg/L) and exhibited some time-dependent changes compared to unstressed cells prior to the phenol addition. Time-course transcriptome analysis results revealed that the mechanism of adaptations to phenol was an evolutionary process that transitioned from tolerance to positive degradation through precise gene regulation at appropriate times. Specifically, basal stress gene expression was upregulated and contributed to phenol tolerance, which involved stress, DNA repair, membrane, efflux pump and antioxidant protein-coding genes, while a phenol degradation gene cluster was specifically induced. Interestingly, both the catechol and protocatechuate branches of the β-ketoadipate pathway contributed to the early stage of phenol degradation, but only the catechol branch was used in the late stage. In addition, pathways involving flagella, chemotaxis, ATP-binding cassette transporters and two-component systems were positively associated with strain survival under phenolic stress. This study provides the first insights into the specific response of Burkholderia sp. to high phenol stress and shows potential for application in remediation of polluted environments. KEY POINTS: • Shock, DNA repair and antioxidant-related genes contributed to phenol tolerance. • β-Ketoadipate pathway branches differed at different stages of phenol degradation. • Adaptation mechanisms transitioned from negative tolerance to positive degradation.

Assessing the Effect of Pretreatments on the Structure and Functionality of Microbial Communities for the Bioconversion of Microalgae to Biogas

Microalgae biomethanization is driven by anaerobic sludge associated microorganisms and is generally limited by the incomplete hydrolysis of the microalgae cell wall, which results in a low availability of microalgal biomass for the methanogenic community. The application of enzymatic pretreatments, e.g., with hydrolytic enzymes, is among the strategies used to work around the incomplete hydrolysis of the microalgae cell wall. Despite the proven efficacy of these pretreatments in increasing biomethanization, the changes that a given pretreatment may cause to the anaerobic sludge associated microorganisms during biomethanization are still unknown. This study evaluated the changes in the expression of the metatranscriptome of anaerobic sludge associated microorganisms during Chlorella sorokiniana biomethanization without pretreatment (WP) (control) and pretreated with commercial cellulase in order to increase the solubilization of the microalgal organic matter. Pretreated microalgal biomass experienced significant increases in biogas the production. The metatranscriptomic analysis of control samples showed functionally active microalgae cells, a bacterial community dominated by γ- and δ-proteobacteria, and a methanogenic community dominated by Methanospirillum hungatei. In contrast, pretreated samples were characterized by the absence of active microalgae cells and a bacteria population dominated by species of the Clostridia class. These differences are also related to the differential activation of metabolic pathways e.g., those associated with the degradation of organic matter during its biomethanization.