Longitudinal Analysis of the Human B Cell Response to Ebola Virus Infection.

Ebola virus (EBOV) remains a public health threat. We performed a longitudinal study of B cell responses to EBOV in four survivors of the 2014 West African outbreak. Infection induced lasting EBOV-specific immunoglobulin G (IgG) antibodies, but their subclass composition changed over time, with IgG1 persisting, IgG3 rapidly declining, and IgG4 appearing late. Striking changes occurred in the immunoglobulin repertoire, with massive recruitment of naive B cells that subsequently underwent hypermutation. We characterized a large panel of EBOV glycoprotein-specific monoclonal antibodies (mAbs). Only a small subset of mAbs that bound glycoprotein by ELISA recognized cell-surface glycoprotein. However, this subset contained all neutralizing mAbs. Several mAbs protected against EBOV disease in animals, including one mAb that targeted an epitope under evolutionary selection during the 2014 outbreak. Convergent antibody evolution was seen across multiple donors, particularly among VH3-13 neutralizing antibodies specific for the GP1 core. Our study provides a benchmark for assessing EBOV vaccine-induced immunity.

Metabolic Phenotypes of Response to Vaccination in Humans.

Herpes zoster (shingles) causes significant morbidity in immune compromised hosts and older adults. Whereas a vaccine is available for prevention of shingles, its efficacy declines with age. To help to understand the mechanisms driving vaccinal responses, we constructed a multiscale, multifactorial response network (MMRN) of immunity in healthy young and older adults immunized with the live attenuated shingles vaccine Zostavax. Vaccination induces robust antigen-specific antibody, plasmablasts, and CD4+ T cells yet limited CD8+ T cell and antiviral responses. The MMRN reveals striking associations between orthogonal datasets, such as transcriptomic and metabolomics signatures, cell populations, and cytokine levels, and identifies immune and metabolic correlates of vaccine immunity. Networks associated with inositol phosphate, glycerophospholipids, and sterol metabolism are tightly coupled with immunity. Critically, the sterol regulatory binding protein 1 and its targets are key integrators of antibody and T follicular cell responses. Our approach is broadly applicable to study human immunity and can help to identify predictors of efficacy as well as mechanisms controlling immunity to vaccination.

A new framework for exploratory network mediator analysis in omics data.

Omics methods are widely used in basic biology and translational medicine research. More and more omics data are collected to explain the impact of certain risk factors on clinical outcomes. To explain the mechanism of the risk factors, a core question is how to find the genes/proteins/metabolites that mediate their effects on the clinical outcome. Mediation analysis is a modeling framework to study the relationship between risk factors and pathological outcomes, via mediator variables. However, high-dimensional omics data are far more challenging than traditional data: (1) From tens of thousands of genes, can we overcome the curse of dimensionality to reliably select a set of mediators? (2) How do we ensure that the selected mediators are functionally consistent? (3) Many biological mechanisms contain nonlinear effects. How do we include nonlinear effects in the high-dimensional mediation analysis? (4) How do we consider multiple risk factors at the same time? To meet these challenges, we propose a new exploratory mediation analysis framework, medNet, which focuses on finding mediators through predictive modeling. We propose new definitions for predictive exposure, predictive mediator, and predictive network mediator, using a statistical hypothesis testing framework to identify predictive exposures and mediators. Additionally, two heuristic search algorithms are proposed to identify network mediators, essentially subnetworks in the genome-scale biological network that mediate the effects of single or multiple exposures. We applied medNet on a breast cancer data set and a metabolomics data set combined with food intake questionnaire data. It identified functionally consistent network mediators for the exposures' impact on the outcome, facilitating data interpretation.

Bayesian functional analysis for untargeted metabolomics data with matching uncertainty and small sample sizes.

Untargeted metabolomics based on liquid chromatography-mass spectrometry technology is quickly gaining widespread application, given its ability to depict the global metabolic pattern in biological samples. However, the data are noisy and plagued by the lack of clear identity of data features measured from samples. Multiple potential matchings exist between data features and known metabolites, while the truth can only be one-to-one matches. Some existing methods attempt to reduce the matching uncertainty, but are far from being able to remove the uncertainty for most features. The existence of the uncertainty causes major difficulty in downstream functional analysis. To address these issues, we develop a novel approach for Bayesian Analysis of Untargeted Metabolomics data (BAUM) to integrate previously separate tasks into a single framework, including matching uncertainty inference, metabolite selection and functional analysis. By incorporating the knowledge graph between variables and using relatively simple assumptions, BAUM can analyze datasets with small sample sizes. By allowing different confidence levels of feature-metabolite matching, the method is applicable to datasets in which feature identities are partially known. Simulation studies demonstrate that, compared with other existing methods, BAUM achieves better accuracy in selecting important metabolites that tend to be functionally consistent and assigning confidence scores to feature-metabolite matches. We analyze a COVID-19 metabolomics dataset and a mouse brain metabolomics dataset using BAUM. Even with a very small sample size of 16 mice per group, BAUM is robust and stable. It finds pathways that conform to existing knowledge, as well as novel pathways that are biologically plausible.

An integrated deep learning framework for the interpretation of untargeted metabolomics data.

Untargeted metabolomics is gaining widespread applications. The key aspects of the data analysis include modeling complex activities of the metabolic network, selecting metabolites associated with clinical outcome and finding critical metabolic pathways to reveal biological mechanisms. One of the key roadblocks in data analysis is not well-addressed, which is the problem of matching uncertainty between data features and known metabolites. Given the limitations of the experimental technology, the identities of data features cannot be directly revealed in the data. The predominant approach for mapping features to metabolites is to match the mass-to-charge ratio (m/z) of data features to those derived from theoretical values of known metabolites. The relationship between features and metabolites is not one-to-one since some metabolites share molecular composition, and various adduct ions can be derived from the same metabolite. This matching uncertainty causes unreliable metabolite selection and functional analysis results. Here we introduce an integrated deep learning framework for metabolomics data that take matching uncertainty into consideration. The model is devised with a gradual sparsification neural network based on the known metabolic network and the annotation relationship between features and metabolites. This architecture characterizes metabolomics data and reflects the modular structure of biological system. Three goals can be achieved simultaneously without requiring much complex inference and additional assumptions: (1) evaluate metabolite importance, (2) infer feature-metabolite matching likelihood and (3) select disease sub-networks. When applied to a COVID metabolomics dataset and an aging mouse brain dataset, our method found metabolic sub-networks that were easily interpretable.

Long-term abuse of caffeine sodium benzoate induces endothelial cells injury and leads to coagulation dysfunction.

Our hospital admitted a patient who had difficulty in coagulation even after blood replacement, and the patient had abused caffeine sodium benzoate (CSB) for more than 20 years. Hence, we aimed to explore whether CSB may cause dysfunction in vascular endothelial cells and its possible mechanism. Low, medium, and high concentrations of serum of long-term CSB intake patients were used to treat HUVECs, with LPS as the positive control. MTT and CCK8 were performed to verify CSB's damaging effect on HUVECs. The expression of ET-1, ICAM-1, VCAM-1, and E-selectin were measured by ELISA. TUNEL assay and Matrigel tube formation assay were carried out to detect apoptosis and angiogenesis of HUVECs. Flow cytometry was applied to analyze cell cycles and expression of CD11b, PDGF, and ICAM-1. Expression of PDGF-BB and PCNA were examined by western blot. The activation of MAPK signaling pathway was detected by qRT-PCR and western blot. Intracellular Ca2+ density was detected by fluorescent probes. CCK8 assay showed high concentration of CSB inhibited cell viability. Cell proliferation and angiogenesis were inhibited by CSB. ET-1, ICAM-1, VCAM-1, and E-selectin upregulated in CSB groups. CSB enhanced apoptosis of HUVECs. CD11b, ICAM-1 increased and PDGF reduced in CSB groups. The expression level and phosphorylation level of MEK, ERK, JUN, and p38 in MAPK pathway elevated in CSB groups. The expression of PCNA and PDGF-BB was suppressed by CSB. Intracellular Ca2+ intensity was increased by CSB. Abuse of CSB injured HUVECs and caused coagulation disorders.

Accurate feature selection improves single-cell RNA-seq cell clustering.

Cell clustering is one of the most important and commonly performed tasks in single-cell RNA sequencing (scRNA-seq) data analysis. An important step in cell clustering is to select a subset of genes (referred to as 'features'), whose expression patterns will then be used for downstream clustering. A good set of features should include the ones that distinguish different cell types, and the quality of such set could have a significant impact on the clustering accuracy. All existing scRNA-seq clustering tools include a feature selection step relying on some simple unsupervised feature selection methods, mostly based on the statistical moments of gene-wise expression distributions. In this work, we carefully evaluate the impact of feature selection on cell clustering accuracy. In addition, we develop a feature selection algorithm named FEAture SelecTion (FEAST), which provides more representative features. We apply the method on 12 public scRNA-seq datasets and demonstrate that using features selected by FEAST with existing clustering tools significantly improve the clustering accuracy.

Metapone: a Bioconductor package for joint pathway testing for untargeted metabolomics data.

MOTIVATION: Testing for pathway enrichment is an important aspect in the analysis of untargeted metabolomics data. Due to the unique characteristics of untargeted metabolomics data, some key issues have not been fully addressed in existing pathway testing algorithms: (i) matching uncertainty between data features and metabolites; (ii) lacking of method to analyze positive mode and negative mode liquid chromatography-mass spectrometry (LC/MS) data simultaneously on the same set of subjects; (iii) the incompleteness of pathways in individual software packages. RESULTS: We developed an innovative R/Bioconductor package: metabolic pathway testing with positive and negative mode data (metapone), which can perform two novel statistical tests that take matching uncertainty into consideration-(i) a weighted gene set enrichment analysis-type test and (ii) a permutation-based weighted hypergeometric test. The package is capable of combining positive- and negative-ion mode results in a single testing scheme. For comprehensiveness, the built-in pathways were manually curated from three sources: Kyoto Encyclopedia of Genes and Genomes, Mummichog and The Small Molecule Pathway Database. AVAILABILITY AND IMPLEMENTATION: The package is available at https://bioconductor.org/packages/devel/bioc/html/metapone.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

AIME: Autoencoder-based integrative multi-omics data embedding that allows for confounder adjustments.

In the integrative analyses of omics data, it is often of interest to extract data representation from one data type that best reflect its relations with another data type. This task is traditionally fulfilled by linear methods such as canonical correlation analysis (CCA) and partial least squares (PLS). However, information contained in one data type pertaining to the other data type may be complex and in nonlinear form. Deep learning provides a convenient alternative to extract low-dimensional nonlinear data embedding. In addition, the deep learning setup can naturally incorporate the effects of clinical confounding factors into the integrative analysis. Here we report a deep learning setup, named Autoencoder-based Integrative Multi-omics data Embedding (AIME), to extract data representation for omics data integrative analysis. The method can adjust for confounder variables, achieve informative data embedding, rank features in terms of their contributions, and find pairs of features from the two data types that are related to each other through the data embedding. In simulation studies, the method was highly effective in the extraction of major contributing features between data types. Using two real microRNA-gene expression datasets, one with confounder variables and one without, we show that AIME excluded the influence of confounders, and extracted biologically plausible novel information. The R package based on Keras and the TensorFlow backend is available at https://github.com/tianwei-yu/AIME.

Metabolomic analysis of the sera of patients with the long-term inhalation of caffeine-sodium benzoate using LC-MS.

The present study aimed to systematically assess the potential biomarkers in the serum samples of patients with long-term inhalation of caffeine-sodium benzoate (CSB). LC-MS was applied to analyze the metabolic profiles of serum samples of patients with the long-term intake of CSB (n = 35) and other volunteers with no intake of CSB treated as the control group (n = 35). The raw data of metabolic profiles were analyzed via principal component analysis, partial least squares analysis, and orthogonal partial least squares analysis. MBRole 2.0 online tools were used to analyze the Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis of different metabolites. The serum metabolic profiles showed several metabolites with large variations, including 2-propyl-2,4-pentadienoic acid, 24-hydroxycholesterol, 3-O-sulfogalactosylceramide (d18:1/24:1(15Z)), 3-O-sulfogalactosylceramide (d18:1/12:0), 3-O-sulfogalactosylceramide (d18:1/14:0), 3a,7a-dihydroxy-5b-cholestan-26-al, 3a,7a-dihydroxy-5b-cholestane, 7a,25-dihydroxycholesterol, bilirubin, and dehydroepiandrosterone sulfate. The Kyoto Encyclopedia of Genes and Genomes pathways involved in metabolism included 'choline metabolism in cancer' and 'glycerophospholipid metabolism'. In conclusion, the present study provides a basis with which to explore the molecular-specific mechanisms concerning the effects of the long-term inhalation of CSB on human physical and mental health.

Sphinganine is associated with 24-h MAP in the non-sleepy with OSA.

INTRODUCTION: Excessive daytime sleepiness is a debilitating symptom of obstructive sleep apnea (OSA) linked to cardiovascular disease, and metabolomic mechanisms underlying this relationship remain unknown. We examine whether metabolites from inflammatory and oxidative stress-related pathways that were identified in our prior work could be involved in connecting the two phenomena. METHODS: This study included 57 sleepy (Epworth Sleepiness Scale (ESS) ≥ 10) and 37 non-sleepy (ESS < 10) participants newly diagnosed and untreated for OSA that completed an overnight in-lab or at home sleep study who were recruited from the Emory Mechanisms of Sleepiness Symptoms Study (EMOSS). Differences in fasting blood samples of metabolites were explored in participants with sleepiness versus those without and multiple linear regression models were utilized to examine the association between metabolites and mean arterial pressure (MAP). RESULTS: The 24-h MAP was higher in sleepy 92.8 mmHg (8.4) as compared to non-sleepy 88.8 mmHg (8.1) individuals (P = 0.03). Although targeted metabolites were not significantly associated with MAP, when we stratified by sleepiness group, we found that sphinganine is significantly associated with MAP (Estimate = 8.7, SE = 3.7, P = 0.045) in non-sleepy patients when controlling for age, BMI, smoking status, and apnea-hypopnea index (AHI). CONCLUSION: This is the first study to evaluate the relationship of inflammation and oxidative stress related metabolites in sleepy versus non-sleepy participants with newly diagnosed OSA and their association with 24-h MAP. Our study suggests that Sphinganine is associated with 24 hour MAP in the non-sleepy participants with OSA.

Feature selection and classification over the network with missing node observations.

Jointly analyzing transcriptomic data and the existing biological networks can yield more robust and informative feature selection results, as well as better understanding of the biological mechanisms. Selecting and classifying node features over genome-scale networks has become increasingly important in genomic biology and genomic medicine. Existing methods have some critical drawbacks. The first is they do not allow flexible modeling of different subtypes of selected nodes. The second is they ignore nodes with missing values, very likely to increase bias in estimation. To address these limitations, we propose a general modeling framework for Bayesian node classification (BNC) with missing values. A new prior model is developed for the class indicators incorporating the network structure. For posterior computation, we resort to the Swendsen-Wang algorithm for efficiently updating class indicators. BNC can naturally handle missing values in the Bayesian modeling framework, which improves the node classification accuracy and reduces the bias in estimating gene effects. We demonstrate the advantages of our methods via extensive simulation studies and the analysis of the cutaneous melanoma dataset from The Cancer Genome Atlas.

The Oxidative Potential of Fine Particulate Matter and Biological Perturbations in Human Plasma and Saliva Metabolome.

Particulate oxidative potential may comprise a key health-relevant parameter of particulate matter (PM) toxicity. To identify biological perturbations associated with particulate oxidative potential and examine the underlying molecular mechanisms, we recruited 54 participants from two dormitories near and far from a congested highway in Atlanta, GA. Fine particulate matter oxidative potential ("FPMOP") levels at the dormitories were measured using dithiothreitol assay. Plasma and saliva samples were collected from participants four times for longitudinal high-resolution metabolic profiling. We conducted metabolome-wide association studies to identify metabolic signals with FPMOP. Leukotriene metabolism and galactose metabolism were top pathways associated with ≥5 FPMOP-related indicators in plasma, while vitamin E metabolism and leukotriene metabolism were found associated with most FPMOP indicators in saliva. We observed different patterns of perturbed pathways significantly associated with water-soluble and -insoluble FPMOPs, respectively. We confirmed five metabolites directly associated with FPMOP, including hypoxanthine, histidine, pyruvate, lactate/glyceraldehyde, and azelaic acid, which were implications of perturbations in acute inflammation, nucleic acid damage and repair, and energy perturbation. The unique metabolic signals were specific to FPMOP, but not PM mass, providing initial indication that FPMOP might constitute a more sensitive, health-relevant measure for elucidating etiologies related to PM2.5 exposures.

Evaluation of the Use of Saliva Metabolome as a Surrogate of Blood Metabolome in Assessing Internal Exposures to Traffic-Related Air Pollution.

In the omics era, saliva, a filtrate of blood, may serve as an alternative, noninvasive biospecimen to blood, although its use for specific metabolomic applications has not been fully evaluated. We demonstrated that the saliva metabolome may provide sensitive measures of traffic-related air pollution (TRAP) and associated biological responses via high-resolution, longitudinal metabolomics profiling. We collected 167 pairs of saliva and plasma samples from a cohort of 53 college student participants and measured corresponding indoor and outdoor concentrations of six air pollutants for the dormitories where the students lived. Grand correlation between common metabolic features in saliva and plasma was moderate to high, indicating a relatively consistent association between saliva and blood metabolites across subjects. Although saliva was less associated with TRAP compared to plasma, 25 biological pathways associated with TRAP were detected via saliva and accounted for 69% of those detected via plasma. Given the slightly higher feature reproducibility found in saliva, these findings provide some indication that the saliva metabolome offers a sensitive and practical alternative to blood for characterizing individual biological responses to environmental exposures.

Decreased humoral immunity to mumps in young adults immunized with MMR vaccine in childhood.

In the past decade, multiple mumps outbreaks have occurred in the United States, primarily in close-contact, high-density settings such as colleges, with a high attack rate among young adults, many of whom had the recommended 2 doses of mumps-measles-rubella (MMR) vaccine. Waning humoral immunity and the circulation of divergent wild-type mumps strains have been proposed as contributing factors to mumps resurgence. Blood samples from 71 healthy 18- to 23-year-old college students living in a non-outbreak area were assayed for antibodies and memory B cells (MBCs) to mumps, measles, and rubella. Seroprevalence rates of mumps, measles, and rubella determined by IgG enzyme-linked immunosorbent assay (ELISA) were 93, 93, and 100%, respectively. The index standard ratio indicated that the concentration of IgG was significantly lower for mumps than rubella. High IgG avidity to mumps Enders strain was detected in sera of 59/71 participants who had sufficient IgG levels. The frequency of circulating mumps-specific MBCs was 5 to 10 times lower than measles and rubella, and 10% of the participants had no detectable MBCs to mumps. Geometric mean neutralizing antibody titers (GMTs) by plaque reduction neutralization to the predominant circulating wild-type mumps strain (genotype G) were 6-fold lower than the GMTs against the Jeryl Lynn vaccine strain (genotype A). The majority of the participants (80%) received their second MMR vaccine ≥10 years prior to study participation. Additional efforts are needed to fully characterize B and T cell immune responses to mumps vaccine and to develop strategies to improve the quality and durability of vaccine-induced immunity.

Elevated levels of inflammatory plasma biomarkers are associated with risk of HIV infection.

BACKGROUND: To determine if individuals, from HIV-1 serodiscordant couple cohorts from Rwanda and Zambia, who become HIV-positive have a distinct inflammatory biomarker profile compared to individuals who remain HIV-negative, we compared levels of biomarkers in plasma of HIV-negative individuals who either seroconverted (pre-infection) and became HIV-positive or remained HIV-negative (uninfected). RESULTS: We observed that individuals in the combined cohort, as well as those in the individual country cohorts, who later became HIV-1 infected had significantly higher baseline levels of multiple inflammatory cytokines/chemokines compared to individuals who remained HIV-negative. Genital inflammation/ulceration or schistosome infections were not associated with this elevated profile. Defined levels of ITAC and IL-7 were significant predictors of later HIV acquisition in ROC predictive analyses, whereas the classical Th1 and Th2 inflammatory cytokines such as IL-12 and interferon-γ or IL-4, IL-5 and Il-13 were not. CONCLUSIONS: Overall, the data show a significant association between increased plasma biomarkers linked to inflammation and immune activation and HIV acquisition and suggests that pre-existing conditions that increase systemic biomarkers represent a factor for increased risk of HIV infection.

forgeNet: a graph deep neural network model using tree-based ensemble classifiers for feature graph construction.

MOTIVATION: A unique challenge in predictive model building for omics data has been the small number of samples (n) versus the large amount of features (p). This 'n≪p' property brings difficulties for disease outcome classification using deep learning techniques. Sparse learning by incorporating known functional relationships between the biological units, such as the graph-embedded deep feedforward network (GEDFN) model, has been a solution to this issue. However, such methods require an existing feature graph, and potential mis-specification of the feature graph can be harmful on classification and feature selection. RESULTS: To address this limitation and develop a robust classification model without relying on external knowledge, we propose a forest graph-embedded deep feedforward network (forgeNet) model, to integrate the GEDFN architecture with a forest feature graph extractor, so that the feature graph can be learned in a supervised manner and specifically constructed for a given prediction task. To validate the method's capability, we experimented the forgeNet model with both synthetic and real datasets. The resulting high classification accuracy suggests that the method is a valuable addition to sparse deep learning models for omics data. AVAILABILITY AND IMPLEMENTATION: The method is available at https://github.com/yunchuankong/forgeNet. CONTACT: tianwei.yu@emory.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

scBatch: batch-effect correction of RNA-seq data through sample distance matrix adjustment.

MOTIVATION: Batch effect is a frequent challenge in deep sequencing data analysis that can lead to misleading conclusions. Existing methods do not correct batch effects satisfactorily, especially with single-cell RNA sequencing (RNA-seq) data. RESULTS: We present scBatch, a numerical algorithm for batch-effect correction on bulk and single-cell RNA-seq data with emphasis on improving both clustering and gene differential expression analysis. scBatch is not restricted by assumptions on the mechanism of batch-effect generation. As shown in simulations and real data analyses, scBatch outperforms benchmark batch-effect correction methods. AVAILABILITY AND IMPLEMENTATION: The R package is available at github.com/tengfei-emory/scBatch. The code to generate results and figures in this article is available at github.com/tengfei-emory/scBatch-paper-scripts. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Macronutrient, Energy, and Bile Acid Metabolism Pathways Altered Following a Physiological Meal Challenge, Relative to Fasting, among Guatemalan Adults.

BACKGROUND: The healthy human metabolome, including its physiological responses after meal consumption, remains incompletely understood. One major research gap is the limited literature assessing how human metabolomic profiles differ between fasting and postprandial states after physiological challenges. OBJECTIVES: Our study objective was to evaluate alterations in high-resolution metabolomic profiles following a standardized meal challenge, relative to fasting, in Guatemalan adults. METHODS: We studied 123 Guatemalan adults without obesity, hypertension, diabetes, metabolic syndrome, or comorbidities. Every participant received a standardized meal challenge (520 kcal, 67.4 g carbohydrates, 24.3 g fat, 8.0 g protein) and provided blood samples while fasting and at 2 h postprandial. Plasma samples were assayed by high-resolution metabolomics with dual-column LC [C18 (negative electrospray ionization), hydrophilic interaction LC (HILIC, positive electrospray ionization)] coupled to ultra-high-resolution MS. Associations between metabolomic features and the meal challenge timepoint were assessed in feature-by-feature multivariable linear mixed regression models. Two algorithms (mummichog, gene set enrichment analysis) were used for pathway analysis, and P values were combined by the Fisher method. RESULTS: Among participants (62.6% male, median age 43.0 y), 1130 features (C18: 777; HILIC: 353) differed between fasting and postprandial states (all false discovery rate-adjusted q < 0.05). Based on differing C18 features, top pathways included: tricarboxylic acid cycle (TCA), primary bile acid biosynthesis, and linoleic acid metabolism (all Pcombined < 0.05). Mass spectral features included: taurine and cholic acid in primary bile acid biosynthesis; and fumaric acid, malic acid, and citric acid in the TCA. HILIC features that differed in the meal challenge reflected linoleic acid metabolism (Pcombined < 0.05). CONCLUSIONS: Energy, macronutrient, and bile acid metabolism pathways were responsive to a standardized meal challenge in adults without cardiometabolic diseases. Our findings reflect metabolic flexibility in disease-free individuals.

DNLC: differential network local consistency analysis.

BACKGROUND: The biological network is highly dynamic. Functional relations between genes can be activated or deactivated depending on the biological conditions. On the genome-scale network, subnetworks that gain or lose local expression consistency may shed light on the regulatory mechanisms related to the changing biological conditions, such as disease status or tissue developmental stages. RESULTS: In this study, we develop a new method to select genes and modules on the existing biological network, in which local expression consistency changes significantly between clinical conditions. The method is called DNLC: Differential Network Local Consistency. In simulations, our algorithm detected artificially created local consistency changes effectively. We applied the method on two publicly available datasets, and the method detected novel genes and network modules that were biologically plausible. CONCLUSIONS: The new method is effective in finding modules in which the gene expression consistency change between clinical conditions. It is a useful tool that complements traditional differential expression analyses to make discoveries from gene expression data. The R package is available at https://cran.r-project.org/web/packages/DNLC.