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Background: Studies have identified individual blood biomarkers associated with chronic obstructive pulmonary disease (COPD) and related phenotypes. However, complex diseases such as COPD typically involve changes in multiple molecules with interconnections that may not be captured when considering single molecular features., Methods: Leveraging proteomic data from 3,173 COPDGene Non-Hispanic White (NHW) and African American (AA) participants, we applied sparse multiple canonical correlation network analysis (SmCCNet) to 4,776 proteins assayed on the SomaScan v4.0 platform to derive sparse networks of proteins associated with current vs. former smoking status, airflow obstruction, and emphysema quantitated from high-resolution computed tomography scans. We then used NetSHy, a dimension reduction technique leveraging network topology, to produce summary scores of each proteomic network, referred to as NetSHy scores. We next performed a genome-wide association study (GWAS) to identify variants associated with the NetSHy scores, or network quantitative trait loci (nQTLs). Finally, we evaluated the replicability of the networks in an independent cohort, SPIROMICS., Results: We identified networks of 13 to 104 proteins for each phenotype and exposure in NHW and AA, and the derived NetSHy scores significantly associated with the variable of interests. Networks included known (sRAGE, ALPP, MIP1) and novel molecules (CA10, CPB1, HIS3, PXDN) and interactions involved in COPD pathogenesis. We observed 7 nQTL loci associated with NetSHy scores, 4 of which remained after conditional analysis. Networks for smoking status and emphysema, but not airflow obstruction, demonstrated a high degree of replicability across race groups and cohorts., Conclusions: In this work, we apply state-of-the-art molecular network generation and summarization approaches to proteomic data from COPDGene participants to uncover protein networks associated with COPD phenotypes. We further identify genetic associations with networks. This work discovers protein networks containing known and novel proteins and protein interactions associated with clinically relevant COPD phenotypes across race groups and cohorts., (© 2024. The Author(s).)

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/ ., (© 2024. The Author(s).)

As terabytes of multi-omics data are being generated, there is an ever-increasing need for methods facilitating the integration and interpretation of such data. Current multi-omics integration methods typically output lists, clusters, or subnetworks of molecules related to an outcome. Even with expert domain knowledge, discerning the biological processes involved is a time-consuming activity. Here we propose PathIntegrate, a method for integrating multi-omics datasets based on pathways, designed to exploit knowledge of biological systems and thus provide interpretable models for such studies. PathIntegrate employs single-sample pathway analysis to transform multi-omics datasets from the molecular to the pathway-level, and applies a predictive single-view or multi-view model to integrate the data. Model outputs include multi-omics pathways ranked by their contribution to the outcome prediction, the contribution of each omics layer, and the importance of each molecule in a pathway. Using semi-synthetic data we demonstrate the benefit of grouping molecules into pathways to detect signals in low signal-to-noise scenarios, as well as the ability of PathIntegrate to precisely identify important pathways at low effect sizes. Finally, using COPD and COVID-19 data we showcase how PathIntegrate enables convenient integration and interpretation of complex high-dimensional multi-omics datasets. PathIntegrate is available as an open-source Python package., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Wieder et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Aim: Assess if cord blood differentially methylated regions (DMRs) representing human metastable epialleles (MEs) associate with offspring adiposity in 588 maternal-infant dyads from the Colorado Health Start Study. Materials & methods: DNA methylation was assessed via the Illumina 450K array (~439,500 CpG sites). Offspring adiposity was obtained via air displacement plethysmography. Linear regression modeled the association of DMRs potentially representing MEs with adiposity. Results & conclusion: We identified two potential MEs, ZFP57 , which associated with infant adiposity change and B4GALNT4 , which associated with infancy and childhood adiposity change. Nine DMRs annotating to genes that annotated to MEs associated with change in offspring adiposity (false discovery rate <0.05). Methylation of approximately 80% of DMRs identified associated with decreased change in adiposity.

Objective: Fat content of adipocytes derived from infant umbilical cord mesenchymal stem cells (MSCs) predicts adiposity in children through 4 to 6 years of age. This study tested the hypothesis that MSCs from infants born to mothers with obesity (Ob-MSCs) exhibit adipocyte hypertrophy and perturbations in genes regulating adipogenesis compared with MSCs from infants of mothers with normal weight (NW-MSCs)., Methods: Adipogenesis was induced in MSCs embedded in three-dimensional hydrogel structures, and cell size and number were measured by three-dimensional imaging. Proliferation and protein markers of proliferation and adipogenesis in undifferentiated and adipocyte differentiating cells were measured. RNA sequencing was performed to determine pathways linked to adipogenesis phenotype., Results: In undifferentiated MSCs, greater zinc finger protein (Zfp)423 protein content was observed in Ob- versus NW-MSCs. Adipocytes from Ob-MSCs were larger but fewer than adipocytes from NW-MSCs. RNA sequencing analysis showed that Zfp423 protein correlated with mRNA expression of genes enriched for cell cycle, MSC lineage specification, inflammation, and metabolism pathways. MSC proliferation was not different before differentiation but declined faster in Ob-MSCs upon adipogenic induction., Conclusions: Ob-MSCs have an intrinsic propensity for adipocyte hypertrophy and reduced hyperplasia during adipogenesis in vitro, perhaps linked to greater Zfp423 content and changes in cell cycle pathway gene expression., (© 2023 The Authors. Obesity published by Wiley Periodicals LLC on behalf of The Obesity Society.)

Privacy protection is a core principle of genomic but not proteomic research. We identified independent single nucleotide polymorphism (SNP) quantitative trait loci (pQTL) from COPDGene and Jackson Heart Study (JHS), calculated continuous protein level genotype probabilities, and then applied a naïve Bayesian approach to link SomaScan 1.3K proteomes to genomes for 2812 independent subjects from COPDGene, JHS, SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS) and Multi-Ethnic Study of Atherosclerosis (MESA). We correctly linked 90-95% of proteomes to their correct genome and for 95-99% we identify the 1% most likely links. The linking accuracy in subjects with African ancestry was lower (~ 60%) unless training included diverse subjects. With larger profiling (SomaScan 5K) in the Atherosclerosis Risk Communities (ARIC) correct identification was > 99% even in mixed ancestry populations. We also linked proteomes-to-proteomes and used the proteome only to determine features such as sex, ancestry, and first-degree relatives. When serial proteomes are available, the linking algorithm can be used to identify and correct mislabeled samples. This work also demonstrates the importance of including diverse populations in omics research and that large proteomic datasets (> 1000 proteins) can be accurately linked to a specific genome through pQTL knowledge and should not be considered unidentifiable., (© 2023. The Author(s).)

Motivation: Biological networks can provide a system-level understanding of underlying processes. In many contexts, networks have a high degree of modularity, i.e. they consist of subsets of nodes, often known as subnetworks or modules, which are highly interconnected and may perform separate functions. In order to perform subsequent analyses to investigate the association between the identified module and a variable of interest, a module summarization, that best explains the module's information and reduces dimensionality is often needed. Conventional approaches for obtaining network representation typically rely only on the profiles of the nodes within the network while disregarding the inherent network topological information., Results: In this article, we propose NetSHy, a hybrid approach which is capable of reducing the dimension of a network while incorporating topological properties to aid the interpretation of the downstream analyses. In particular, NetSHy applies principal component analysis (PCA) on a combination of the node profiles and the well-known Laplacian matrix derived directly from the network similarity matrix to extract a summarization at a subject level. Simulation scenarios based on random and empirical networks at varying network sizes and sparsity levels show that NetSHy outperforms the conventional PCA approach applied directly on node profiles, in terms of recovering the true correlation with a phenotype of interest and maintaining a higher amount of explained variation in the data when networks are relatively sparse. The robustness of NetSHy is also demonstrated by a more consistent correlation with the observed phenotype as the sample size decreases. Lastly, a genome-wide association study is performed as an application of a downstream analysis, where NetSHy summarization scores on the biological networks identify more significant single nucleotide polymorphisms than the conventional network representation., Availability and Implementation: R code implementation of NetSHy is available at https://github.com/thaovu1/NetSHy., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2022. Published by Oxford University Press.)

The study of complex behavior of biological systems has become increasingly dependent on evolutionary network modeling. In particular, multi-omics networks capture interactions between biomolecules such as proteins and metabolites, providing a basis for predicting relationships between such biomolecules and various phenotypic traits of complex diseases. In this paper, we introduce an integrative framework that given a multi-omics network representing a cohort of subjects, learns expressive representations for network nodes, and combines the learned nodes representations with the biological profiles of individual subjects for enriched representation of the subjects. With extensive empirical evaluation using real-world multi-omics networks, we show that our proposed framework significantly outperforms existing and baseline methods in terms of subject representation accuracy, particularly when the multi-omics network representing the cohort is sparse and structured and therefore, more informative.

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death in the United States. COPD represents one of many areas of research where identifying complex pathways and networks of interacting biomarkers is an important avenue toward studying disease progression and potentially discovering cures. Recently, sparse multiple canonical correlation network analysis (SmCCNet) was developed to identify complex relationships between omics associated with a disease phenotype, such as lung function. SmCCNet uses two sets of omics datasets and an associated output phenotypes to generate a multi-omics graph, which can then be used to explore relationships between omics in the context of a disease. Detecting significant subgraphs within this multi-omics network, i.e., subgraphs which exhibit high correlation to a disease phenotype and high inter-connectivity, can help clinicians identify complex biological relationships involved in disease progression. The current approach to identifying significant subgraphs relies on hierarchical clustering, which can be used to inform clinicians about important pathways involved in the disease or phenotype of interest. The reliance on a hierarchical clustering approach can hinder subgraph quality by biasing toward finding more compact subgraphs and removing larger significant subgraphs. This study aims to introduce new significant subgraph detection techniques. In particular, we introduce two subgraph detection methods, dubbed Correlated PageRank and Correlated Louvain, by extending the Personalized PageRank Clustering and Louvain algorithms, as well as a hybrid approach combining the two proposed methods, and compare them to the hierarchical method currently in use. The proposed methods show significant improvement in the quality of the subgraphs produced when compared to the current state of the art., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Abdel-Hafiz, Najafi, Helmi, Pratte, Zhuang, Liu, Kechris, Bowler, Lange and Banaei-Kashani.)

Chronic Obstructive Pulmonary Disease (COPD) is the third leading cause of mortality in the United States; however, COPD has heterogeneous clinical phenotypes. This is the first large scale attempt which uses transcriptomics, proteomics, and metabolomics (multi-omics) to determine whether there are molecularly defined clusters with distinct clinical phenotypes that may underlie the clinical heterogeneity. Subjects included 3,278 subjects from the COPDGene cohort with at least one of the following profiles: whole blood transcriptomes (2,650 subjects); plasma proteomes (1,013 subjects); and plasma metabolomes (1,136 subjects). 489 subjects had all three contemporaneous -omics profiles. Autoencoder embeddings were performed individually for each -omics dataset. Embeddings underwent subspace clustering using MineClus, either individually by -omics or combined, followed by recursive feature selection based on Support Vector Machines. Clusters were tested for associations with clinical variables. Optimal single -omics clustering typically resulted in two clusters. Although there was overlap for individual -omics cluster membership, each -omics cluster tended to be defined by unique molecular pathways. For example, prominent molecular features of the metabolome-based clustering included sphingomyelin, while key molecular features of the transcriptome-based clusters were related to immune and bacterial responses. We also found that when we integrated the -omics data at a later stage, we identified subtypes that varied based on age, severity of disease, in addition to diffusing capacity of the lungs for carbon monoxide, and precent on atrial fibrillation. In contrast, when we integrated the -omics data at an earlier stage by treating all data sets equally, there were no clinical differences between subtypes. Similar to clinical clustering, which has revealed multiple heterogenous clinical phenotypes, we show that transcriptomics, proteomics, and metabolomics tend to define clusters of COPD patients with different clinical characteristics. Thus, integrating these different -omics data sets affords additional insight into the molecular nature of COPD and its heterogeneity., Competing Interests: The authors have declared that no competing interests exist.

Susceptibility and progression of lung disease, as well as response to treatment, often differ by sex, yet the metabolic mechanisms driving these sex-specific differences are still poorly understood. Women with chronic obstructive pulmonary disease (COPD) have less emphysema and more small airway disease on average than men, though these differences become less pronounced with more severe airflow limitation. While small studies of targeted metabolites have identified compounds differing by sex and COPD status, the sex-specific effect of COPD on systemic metabolism has yet to be interrogated. Significant sex differences were observed in 9 of the 11 modules identified in COPDGene. Sex-specific associations by COPD status and emphysema were observed in 3 modules for each phenotype. Sex stratified individual metabolite associations with COPD demonstrated male-specific associations in sphingomyelins and female-specific associations in acyl carnitines and phosphatidylethanolamines. There was high preservation of module assignments in SPIROMICS (SubPopulations and InteRmediate Outcome Measures In COPD Study) and similar female-specific shift in acyl carnitines. Several COPD associated metabolites differed by sex. Acyl carnitines and sphingomyelins demonstrate sex-specific abundances and may represent important metabolic signatures of sex differences in COPD. Accurately characterizing the sex-specific molecular differences in COPD is vital for personalized diagnostics and therapeutics.

An adverse intrauterine environment is associated with the future risk of obesity and type 2 diabetes. Changes in placental function may underpin the intrauterine origins of adult disease, but longitudinal studies linking placental function with childhood outcomes are rare. Here, we determined the abundance and phosphorylation of protein intermediates involved in insulin signaling, inflammation, cortisol metabolism, protein glycosylation, and mitochondrial biogenesis in placental villus samples from healthy mothers from the Healthy Start cohort. Using MANOVA, we tested the association between placental proteins and offspring adiposity (fat mass percentage) at birth ( n = 109) and infancy (4-6 months, n = 104), and adiposity, skinfold thickness, triglycerides, and insulin in children (4-6 years, n = 66). Placental IGF-1 receptor protein was positively associated with serum triglycerides in children. GSK3β phosphorylation at serine 9, a readout of insulin and growth factor signaling, and the ratio of phosphorylated to total JNK2 were both positively associated with midthigh skinfold thickness in children. Moreover, peroxisome proliferator-activated receptor γ coactivator (PGC)-1α abundance was positively associated with insulin in children. In conclusion, placental insulin/IGF-1 signaling, PGC-1α, and inflammation pathways were positively associated with metabolic outcomes in 4- to 6-year-old children, identifying a novel link between placental function and long-term metabolic outcomes., (© 2021 by the American Diabetes Association.)

Genomic approaches hold great promise for resolving unanswered questions about transmission patterns and responses to control efforts for schistosomiasis and other neglected tropical diseases. However, the cost of generating genomic data and the challenges associated with obtaining sufficient DNA from individual schistosome larvae (miracidia) from mammalian hosts have limited the application of genomic data for studying schistosomes and other complex macroparasites. Here, we demonstrate the feasibility of utilizing whole genome amplification and sequencing (WGS) to analyze individual archival miracidia. As an example, we sequenced whole genomes of 22 miracidia from 11 human hosts representing two villages in rural Sichuan, China, and used these data to evaluate patterns of relatedness and genetic diversity. We also down-sampled our dataset to test how lower coverage sequencing could increase the cost effectiveness of WGS while maintaining power to accurately infer relatedness. Collectively, our results illustrate that population-level WGS datasets are attainable for individual miracidia and represent a powerful tool for ultimately providing insight into overall genetic diversity, parasite relatedness, and transmission patterns for better design and evaluation of disease control efforts., Competing Interests: The authors have declared that no competing interests exist.

Background: Small studies have recently suggested that there are specific plasma metabolic signatures in chronic obstructive pulmonary disease (COPD), but there have been no large comprehensive study of metabolomic signatures in COPD that also integrate genetic variants. Materials and Methods: Fresh frozen plasma from 957 non-Hispanic white subjects in COPDGene was used to quantify 995 metabolites with Metabolon's global metabolomics platform. Metabolite associations with five COPD phenotypes (chronic bronchitis, exacerbation frequency, percent emphysema, post-bronchodilator forced expiratory volume at one second [FEV 1 ]/forced vital capacity [FVC], and FEV 1 percent predicted) were assessed. A metabolome-wide association study was performed to find genetic associations with metabolite levels. Significantly associated single-nucleotide polymorphisms were tested for replication with independent metabolomic platforms and independent cohorts. COPD phenotype-driven modules were identified in network analysis integrated with genetic associations to assess gene-metabolite-phenotype interactions. Results: Of metabolites tested, 147 (14.8%) were significantly associated with at least 1 COPD phenotype. Associations with airflow obstruction were enriched for diacylglycerols and branched chain amino acids. Genetic associations were observed with 109 (11%) metabolites, 72 (66%) of which replicated in an independent cohort. For 20 metabolites, more than 20% of variance was explained by genetics. A sparse network of COPD phenotype-driven modules was identified, often containing metabolites missed in previous testing. Of the 26 COPD phenotype-driven modules, 6 contained metabolites with significant met-QTLs, although little module variance was explained by genetics. Conclusion: A dysregulation of systemic metabolism was predominantly found in COPD phenotypes characterized by airflow obstruction, where we identified robust heritable effects on individual metabolite abundances. However, network analysis, which increased the statistical power to detect associations missed previously in classic regression analyses, revealed that the genetic influence on COPD phenotype-driven metabolomic modules was modest when compared with clinical and environmental factors., Competing Interests: No competing financial interests exist., (© Lucas A. Gillenwater et al., 2020; Published by Mary Ann Liebert, Inc.)

Hypertensive disorders of pregnancy (HDP) are associated with low birth weight, shorter gestational age, and increased risk of maternal and offspring cardiovascular diseases later in life. The mechanisms involved are poorly understood, but epigenetic regulation of gene expression may play a part. We performed meta-analyses in the Pregnancy and Childhood Epigenetics Consortium to test the association between either maternal HDP (10 cohorts; n=5242 [cases=476]) or preeclampsia (3 cohorts; n=2219 [cases=135]) and epigenome-wide DNA methylation in cord blood using the Illumina HumanMethylation450 BeadChip. In models adjusted for confounders, and with Bonferroni correction, HDP and preeclampsia were associated with DNA methylation at 43 and 26 CpG sites, respectively. HDP was associated with higher methylation at 27 (63%) of the 43 sites, and across all 43 sites, the mean absolute difference in methylation was between 0.6% and 2.6%. Epigenome-wide associations of HDP with offspring DNA methylation were modestly consistent with the equivalent epigenome-wide associations of preeclampsia with offspring DNA methylation (R 2 =0.26). In longitudinal analyses conducted in 1 study (n=108 HDP cases; 550 controls), there were similar changes in DNA methylation in offspring of those with and without HDP up to adolescence. Pathway analysis suggested that genes located at/near HDP-associated sites may be involved in developmental, embryogenesis, or neurological pathways. HDP is associated with offspring DNA methylation with potential relevance to development.

Chronic obstructive pulmonary disease (COPD) occurs typically in current or former smokers, but only a minority of people with smoking history develops the disease. Besides environmental factors, genetics is an important risk factor for COPD. However, the relationship between genetics, environment and phenotypes is not well understood. Sample sizes for genome-wide expression studies based on lung tissue have been small due to the invasive nature of sample collection. Increasing evidence for the systemic nature of the disease makes blood a good alternative source to study the disease, but there have also been few large-scale blood genomic studies in COPD. Due to the complexity and heterogeneity of COPD, examining groups of interacting genes may have more relevance than identifying individual genes. Therefore, we used Weighted Gene Co-expression Network Analysis to find groups of genes (modules) that are highly connected. However, module definitions may vary between individual data sets. To alleviate this problem, we used a consensus module definition based on two cohorts, COPDGene and ECLIPSE. We studied the relationship between the consensus modules and COPD phenotypes airflow obstruction and emphysema. We also used these consensus module definitions on an independent cohort (TESRA) and performed a meta analysis involving all data sets. We found several modules that are associated with COPD phenotypes, are enriched in functional categories and are overrepresented for cell-type specific genes. Of the 14 consensus modules, three were strongly associated with airflow obstruction (meta p ≤ 0.0002), and two had some association with emphysema (meta p ≤ 0.06); some associations were stronger in the case-control cohorts, and others in the cases-only subcohorts. Gene Ontology terms that were overrepresented included "immune response" and "defense response." The cell types whose type-specific genes were overrepresented in modules (p < 0.05) included natural killer cells, dendritic cells, and neutrophils. Together, this is the largest investigation of gene blood expression in COPD with 469 cases in COPDGene, ECLIPSE and TESRA combined, with 6267 genes common to all data sets. Additional, we have 42 and 83 controls in COPDGene and ECLIPSE, respectively.

Human papillomavirus (HPV) infection distinctly alters methylation patterns in HPV-associated cancer. We have recently reported that HPV E7-dependent promoter hypermethylation leads to downregulation of the chemokine CXCL14 and suppression of antitumor immune responses. To investigate the extent of gene expression dysregulated by HPV E7-induced DNA methylation, we analyzed parallel global gene expression and DNA methylation using normal immortalized keratinocyte lines, NIKS, NIKS-16, NIKS-18, and NIKS-16∆E7. We show that expression of the MHC class I genes is downregulated in HPV-positive keratinocytes in an E7-dependent manner. Methylome analysis revealed hypermethylation at a distal CpG island (CGI) near the HLA-E gene in NIKS-16 cells compared to either NIKS cells or NIKS-16∆E7 cells, which lack E7 expression. The HLA-E CGI functions as an active promoter element which is dramatically repressed by DNA methylation. HLA-E protein expression on cell surface is downregulated by high-risk HPV16 and HPV18 E7 expression, but not by low-risk HPV6 and HPV11 E7 expression. Conversely, demethylation at the HLA-E CGI restores HLA-E protein expression in HPV-positive keratinocytes. Because HLA-E plays an important role in antiviral immunity by regulating natural killer and CD8 + T cells, epigenetic downregulation of HLA-E by high-risk HPV E7 may contribute to virus-induced immune evasion during HPV persistence.

Gene co-expression analysis has proven to be a powerful tool for ascertaining the organization of gene products into networks that are important for organ function. An organ, such as the liver, engages in a multitude of functions important for the survival of humans, rats, and other animals; these liver functions include energy metabolism, metabolism of xenobiotics, immune system function, and hormonal homeostasis. With the availability of organ-specific transcriptomes, we can now examine the role of RNA transcripts (both protein-coding and non-coding) in these functions. A systems genetic approach for identifying and characterizing liver gene networks within a recombinant inbred panel of rats was used to identify genetically regulated transcriptional networks (modules). For these modules, biological consensus was found between functional enrichment analysis and publicly available phenotypic quantitative trait loci (QTL). In particular, the biological function of two liver modules could be linked to immune response. The eigengene QTLs for these co-expression modules were located at genomic regions coincident with highly significant phenotypic QTLs; these phenotypes were related to rheumatoid arthritis, food preference, and basal corticosterone levels in rats. Our analysis illustrates that genetically and biologically driven RNA-based networks, such as the ones identified as part of this research, provide insight into the genetic influences on organ functions. These networks can pinpoint phenotypes that manifest through the interaction of many organs/tissues and can identify unannotated or under-annotated RNA transcripts that play a role in these phenotypes.

In vitiligo, the autoimmune destruction of epidermal melanocytes produces white spots that can be repigmented by melanocyte precursors from the hair follicles, following stimulation with UV light. We examined by immunofluorescence the distribution of melanocyte markers (C-KIT, DCT, PAX3, and TYR) coupled with markers of proliferation (KI-67) and migration (MCAM) in precursors and mature melanocytes from the hair follicle and the epidermis of untreated and narrow band UVB (NBUVB)-treated human vitiligo skin. NBUVB was associated with a significant increase in the number of melanocytes in the infundibulum and with restoration of the normal melanocyte population in the epidermis, which was lacking in the untreated vitiligo. We identified several precursor populations (melanocyte stem cells, melanoblasts, and other immature phenotypes), and progressively differentiating melanocytes, some with putative migratory and/or proliferative abilities. The primary melanocyte germ was present in the untreated and treated hair follicle bulge, whereas a possible secondary melanocyte germ composed of C-KIT+ melanocytes was found in the infundibulum and interfollicular epidermis of UV-treated vitiligo. This is an exceptional model for studying the mobilization of melanocyte stem cells in human skin. Improved understanding of this process is essential for designing better treatments for vitiligo, ultimately based on melanocyte stem cell activation and mobilization.

microRNAs (miRNAs) regulate expression by promoting degradation or repressing translation of target transcripts. miRNA target sites have been catalogued in databases based on experimental validation and computational prediction using various algorithms. Several online resources provide collections of multiple databases but need to be imported into other software, such as R, for processing, tabulation, graphing and computation. Currently available miRNA target site packages in R are limited in the number of databases, types of databases and flexibility. We present multiMiR, a new miRNA-target interaction R package and database, which includes several novel features not available in existing R packages: (i) compilation of nearly 50 million records in human and mouse from 14 different databases, more than any other collection; (ii) expansion of databases to those based on disease annotation and drug microRNAresponse, in addition to many experimental and computational databases; and (iii) user-defined cutoffs for predicted binding strength to provide the most confident selection. Case studies are reported on various biomedical applications including mouse models of alcohol consumption, studies of chronic obstructive pulmonary disease in human subjects, and human cell line models of bladder cancer metastasis. We also demonstrate how multiMiR was used to generate testable hypotheses that were pursued experimentally., (© The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Interleukin-16 (IL-16) is a multifunctional cytokine that has been associated with autoimmune and allergic diseases. To investigate comprehensively whether IL-16 is also associated with chronic obstructive pulmonary disease (COPD) and emphysema, we performed an integrated analysis of multiple "omics" data. Over 500 subjects participating in the COPDGene® study donated blood and were clinically characterized and genetically profiled. IL-16 mRNA levels were measured in peripheral blood mononuclear cells (PBMC), and protein levels were measured in fresh frozen plasma. A multivariate analysis found plasma IL-16 positively associated with age and body mass index, and negatively associated with current smoking and emphysema in the upper lobes. PBMC IL-16 expression was positively associated with gender and a composite score for airflow obstruction, emphysema, and gas trapping. Whole-genome expression quantitative trait locus (eQTL) analysis identified a novel IL-16 missense SNP (rs11556218) associated with lower IL-16 in plasma. In summary, an integrated "omics" analysis in a very large cohort identified an association between decreased IL-16 and emphysema and discovered a novel IL-16 cis-eQTL. Thus IL-16 plasma levels and IL-16 genotyping may be useful in a personalized medicine approach for lung disease.

Although genome-wide expression data sets from multiple species are now more commonly generated, there have been few studies on how to best integrate this type of correlated data into models. Starting with a single-species, linear regression model that predicts transcription factor binding sites as a case study, we investigated how best to take into account the correlated expression data when extending this model to multiple species. Using a multivariate regression model, we accounted for the phylogenetic relationships among the species in two ways: (i) a repeated-measures model, where the error term is constrained; and (ii) a Bayesian hierarchical model, where the prior distributions of the regression coefficients are constrained. We show that both multiple-species models improve predictive performance over the single-species model. When compared with each other, the repeated-measures model outperformed the Bayesian model. We suggest a possible explanation for the better performance of the model with the constrained error term., (Copyright © 2013 John Wiley & Sons, Ltd.)

Amino acid residues critical for a protein's structure-function are retained by natural selection and these residues are identified by the level of variance in co-aligned homologous protein sequences. The relevant residues in the nitrogen fixation Component 1 α- and β-subunits were identified by the alignment of 95 protein sequences. Proteins were included from species encompassing multiple microbial phyla and diverse ecological niches as well as the nitrogen fixation genotypes, anf, nif, and vnf, which encode proteins associated with cofactors differing at one metal site. After adjusting for differences in sequence length, insertions, and deletions, the remaining >85% of the sequence co-aligned the subunits from the three genotypes. Six Groups, designated Anf, Vnf , and Nif I-IV, were assigned based upon genetic origin, sequence adjustments, and conserved residues. Both subunits subdivided into the same groups. Invariant and single variant residues were identified and were defined as "core" for nitrogenase function. Three species in Group Nif-III, Candidatus Desulforudis audaxviator, Desulfotomaculum kuznetsovii, and Thermodesulfatator indicus, were found to have a seleno-cysteine that replaces one cysteinyl ligand of the 8Fe:7S, P-cluster. Subsets of invariant residues, limited to individual groups, were identified; these unique residues help identify the gene of origin (anf, nif, or vnf) yet should not be considered diagnostic of the metal content of associated cofactors. Fourteen of the 19 residues that compose the cofactor pocket are invariant or single variant; the other five residues are highly variable but do not correlate with the putative metal content of the cofactor. The variable residues are clustered on one side of the cofactor, away from other functional centers in the three dimensional structure. Many of the invariant and single variant residues were not previously recognized as potentially critical and their identification provides the bases for new analyses of the three-dimensional structure and for mutagenesis studies.

Although most cases of chronic obstructive pulmonary disease (COPD) occur in smokers, only a fraction of smokers develop the disease. We hypothesized distinct molecular signatures for COPD and emphysema in the peripheral blood mononuclear cells (PBMCs) of current and former smokers. To test this hypothesis, we identified and validated PBMC gene expression profiles in smokers with and without COPD. We generated expression data on 136 subjects from the COPDGene study, using Affymetrix U133 2.0 microarrays (Affymetrix, Santa Clara, CA). Multiple linear regression with adjustment for covariates (gender, age, body mass index, family history, smoking status, and pack-years) was used to identify candidate genes, and ingenuity pathway analysis was used to identify candidate pathways. Candidate genes were validated in 149 subjects according to multiplex quantitative real-time polymerase chain reaction, which included 75 subjects not previously profiled. Pathways that were differentially expressed in subjects with COPD and emphysema included those that play a role in the immune system, inflammatory responses, and sphingolipid (ceramide) metabolism. Twenty-six of the 46 candidate genes (e.g., FOXP1, TCF7, and ASAH1) were validated in the independent cohort. Plasma metabolomics was used to identify a novel glycoceramide (galabiosylceramide) as a biomarker of emphysema, supporting the genomic association between acid ceramidase (ASAH1) and emphysema. COPD is a systemic disease whose gene expression signatures in PBMCs could serve as novel diagnostic or therapeutic targets.

Summary: comb-p is a command-line tool and a python library that manipulates BED files of possibly irregularly spaced P-values and (1) calculates auto-correlation, (2) combines adjacent P-values, (3) performs false discovery adjustment, (4) finds regions of enrichment (i.e. series of adjacent low P-values) and (5) assigns significance to those regions. In addition, tools are provided for visualization and assessment. We provide validation and example uses on bisulfite-seq with P-values from Fisher's exact test, tiled methylation probes using a linear model and Dam-ID for chromatin binding using moderated t-statistics. Because the library accepts input in a simple, standardized format and is unaffected by the origin of the P-values, it can be used for a wide variety of applications., Availability: comb-p is maintained under the BSD license. The documentation and implementation are available at https://github.com/brentp/combined-pvalues., Contact: bpederse@gmail.com

Background: Prenatal alcohol exposure can result in fetal alcohol spectrum disorders (FASD). Not all women who consume alcohol during pregnancy have children with FASD and studies have shown that genetic factors can play a role in ethanol teratogenesis. We examined gene expression in embryos and placentae from C57BL/6J (B6) and DBA/2J (D2) mice following prenatal alcohol exposure. B6 fetuses are susceptible to morphological malformations following prenatal alcohol exposure while D2 are relatively resistant., Methods: Male and female B6 and D2 mice were mated for 2 hours in the morning, producing 4 embryonic genotypes: true-bred B6B6 and D2D2, and reciprocal B6D2 and D2B6. On gestational day 9, dams were intubated with 5.8 g/kg ethanol, an isocaloric amount of maltose dextrin, or nothing. Four hours later, dams were sacrificed and embryos and placentae were harvested. RNA was extracted, labeled and hybridized to Affymetrix Mouse Genome 430 v2 microarray chips. Data were normalized, subjected to analysis of variance and tested for enrichment of gene ontology molecular function and biological process using the Database for Annotation, Visualization and Integrated Discovery (DAVID)., Results: Several gene classes were differentially expressed in B6 and D2 regardless of treatment, including genes involved in polysaccharide binding and mitosis. Prenatal alcohol exposure altered expression of a subset of genes, including genes involved in methylation, chromatin remodeling, protein synthesis, and mRNA splicing. Very few genes were differentially expressed between maltose-exposed tissues and tissues that received nothing, so we combined these groups for comparisons with ethanol. While we observed many expression changes specific to B6 following prenatal alcohol exposure, none were specific for D2. Gene classes up- or down-regulated in B6 following prenatal alcohol exposure included genes involved in mRNA splicing, transcription, and translation., Conclusions: Our study identified several classes of genes with altered expression following prenatal alcohol exposure, including many specific for B6, a strain susceptible to ethanol teratogenesis. Lack of strain specific effects in D2 suggests there are few gene expression changes that confer resistance. Future studies will begin to analyze functional significance of the expression changes., (Copyright © 2012 by the Research Society on Alcoholism.)

The identification of genes that contribute to polygenic (complex) behavioral phenotypes is a key goal of current genetic research. One approach to this goal is to combine gene expression information with genetic information, i.e. to map chromosomal regions that regulate gene expression levels. This approach has been termed 'genetical genomics', and, when used in conjunction with the identification of genomic regions (QTLs) that regulate the complex physiological trait under investigation, provides a strong basis for candidate gene discovery. In this paper, we describe the implementation of the genetical genomic/phenotypic approach to identify candidate genes for sensitivity to the analgesic effect of morphine in BXD recombinant inbred mice. Our analysis was performed 'in silico', using an online interactive resource called PhenoGen (http://phenogen.ucdenver.edu). We describe in detail the use of this resource, which identified a set of candidate genes, some of whose products regulate the cellular localization and activity of the mu opiate receptor. The results demonstrate how PhenoGen can be used to identify a novel set of genes that can be further investigated for their potential role in pain, morphine analgesia and/or morphine tolerance., (© 2010 The Authors, Addiction Biology © 2010 Society for the Study of Addiction.)

Our laboratory has developed an online interactive resource called PhenoGen ( http://phenogen.ucdenver.edu ) which provides an archive of brain and other organ gene expression data from a panel of 20 common inbred mouse strains, and three recombinant inbred (RI) panels (two mouse and one rat). DNA microarray data can also be uploaded to the site where numerous analytical tools can be implemented. An important advantage to the archived data is that each array represents data from a single animal and each strain was sampled 4-7 times, providing an estimate of genetic variance (heritability) of individual transcript levels. These panels also allow genetic mapping of expression QTLs. Overlap of eQTLs with phenotypic QTLs provides a powerful approach to candidate gene identification. These methods are briefly described here and we encourage the use of our site for both scientific discovery and as a teaching tool in quantitative genetics.

Transcriptional regulation depends upon the binding of transcription factor (TF) proteins to DNA in a sequence-dependent manner. Although many experimental methods address the interaction between DNA and proteins, they generally do not comprehensively and accurately assess the full binding repertoire (the complete set of sequences that might be bound with at least moderate strength). Here, we develop and evaluate through simulation an experimental approach that allows simultaneous high-throughput quantitative analysis of TF binding affinity to thousands of potential DNA ligands. Tens of thousands of putative binding targets can be mixed with a TF, and both the pre-bound and bound target pools sequenced. A hierarchical Bayesian Markov chain Monte Carlo approach determines posterior estimates for the dissociation constants, sequence-specific binding energies, and free TF concentrations. A unique feature of our approach is that dissociation constants are jointly estimated from their inferred degree of binding and from a model of binding energetics, depending on how many sequence reads are available and the explanatory power of the energy model. Careful experimental design is necessary to obtain accurate results over a wide range of dissociation constants. This approach, which we call Simultaneous Ultra high-throughput Ligand Dissociation EXperiment (SULDEX), is theoretically capable of rapid and accurate elucidation of an entire TF-binding repertoire.

High density tiling arrays are an effective strategy for genome-wide identification of transcription factor binding regions. Sliding window methods that calculate moving averages of log ratios or t-statistics have been useful for the analysis of tiling array data. Here, we present a method that generalizes the moving average approach to evaluate sliding windows of p-values by using combined p-value statistics. In particular, the combined p-value framework can be useful in situations when taking averages of the corresponding test-statistic for the hypothesis may not be appropriate or when it is difficult to assess the significance of these averages. We exhibit the strengths of the combined p-values methods on Drosophila tiling array data and assess their ability to predict genomic regions enriched for transcription factor binding. The predictions are evaluated based on their proximity to target genes and their enrichment of known transcription factor binding sites. We also present an application for the generalization of the moving average based on integrating two different tiling array experiments.

Background: This study examines the structural features and phylogeny of the alpha subunits of 69 full-length NifD (MoFe subunit), VnfD (VFe subunit), and AnfD (FeFe subunit) sequences., Methodology/principal Findings: The analyses of this set of sequences included BLAST scores, multiple sequence alignment, examination of patterns of covariant residues, phylogenetic analysis and comparison of the sequences flanking the conserved Cys and His residues that attach the FeMo cofactor to NifD and that are also conserved in the alternative nitrogenases. The results show that NifD nitrogenases fall into two distinct groups. Group I includes NifD sequences from many genera within Bacteria, including all nitrogen-fixing aerobes examined, as well as strict anaerobes and some facultative anaerobes, but no archaeal sequences. In contrast, Group II NifD sequences were limited to a small number of archaeal and bacterial sequences from strict anaerobes. The VnfD and AnfD sequences fall into two separate groups, more closely related to Group II NifD than to Group I NifD. The pattern of perfectly conserved residues, distributed along the full length of the Group I and II NifD, VnfD, and AnfD, confirms unambiguously that these polypeptides are derived from a common ancestral sequence., Conclusions/significance: There is no indication of a relationship between the patterns of covariant residues specific to each of the four groups discussed above that would give indications of an evolutionary pathway leading from one type of nitrogenase to another. Rather the totality of the data, along with the phylogenetic analysis, is consistent with a radiation of Group I and II NifDs, VnfD and AnfD from a common ancestral sequence. All the data presented here strongly support the suggestion made by some earlier investigators that the nitrogenase family had already evolved in the last common ancestor of the Archaea and Bacteria.

De novo identification of transcription factor binding sites (TFBS) is a challenging computational problem because TFBSs are relatively short sequences buried in long genomic regions. Earlier methods incorporated genome-wide expression data and promoter sequences into a linear-model framework, regressing expression on counts of putative TFBSs in promoters for a single species. More recently, it has been shown that examining sequence data across multiple species improves the prediction of TFBSs. In this work, we describe an extension of the single-species, linear-model framework for the analysis of paired cross-species sequence and expression data. A repeated measures model for gene-expression measurements across species is used, accounting for phylogenetic relationships among species through the error covariance structure. This multiple-species algorithm is applied to a data set of four yeast species grown under heat-shock conditions and comparisons are made to the single species algorithm. Using evaluations based on transcription factor binding strength and an independent source of expression data, we find the multiple species results show an improvement in the prediction of TFBS.

Ribonuclease L (RNase L) is an antiviral endoribonuclease that cleaves hepatitis C virus (HCV) RNA at single-stranded UA and UU dinucleotides throughout the open reading frame (ORF). To determine whether RNase L exerts evolutionary pressure on HCV we examined the frequencies of UA and UU dinucleotides in 162 RNA sequences from the Los Alamos National Labs HCV Database (http://hcv.lanl.gov). Considering the base composition of the HCV ORFs, both UA and UU dinucleotides were less frequent than predicted in each of 162 HCV RNAs. UA dinucleotides were significantly less frequent than predicted at each of the three codon positions while UU dinucleotides were less frequent than predicted predominantly at the wobble position of codons. UA and UU dinucleotides were among the least abundant dinucleotides in HCV RNA ORFs. Furthermore, HCV genotype 1 RNAs have a lower frequency of UA and UU dinucleotides than genotype 2 and 3 RNAs, perhaps contributing to increased resistance of HCV genotype 1 infections to interferon therapy. In vitro, RNase L cleaved both HCV genotype 1 and 2 RNAs efficiently. Thus, RNase L can cleave HCV RNAs efficiently and variably reduced frequencies of UA and UU dinucleotides in HCV RNA ORFs are consistent with the selective pressure of RNase L.

Lateral gene transfer (LGT) is now accepted as an important factor in the evolution of prokaryotes. Establishment of the occurrence of LGT is typically attempted by a variety of methods that includes the comparison of reconstructed phylogenetic trees, the search for unusual GC composition or codon usage within a genome, and identification of similarities between distant species as determined by best blast hits. We explore quantitative assessments of these strategies to study the prokaryotic trait of nitrogen fixation, the enzyme-catalyzed reduction of N(2) to ammonia. Phylogenies constructed on nitrogen fixation genes are not in agreement with the tree-of-life based on 16S rRNA but do not conclusively distinguish between gene loss and LGT hypotheses. Using a series of analyses on a set of complete genomes, our results distinguish two structurally distinct classes of MoFe nitrogenases whose distribution cuts across lines of vertical inheritance and makes us believe that a conclusive case for LGT has been made.

Transcription factors and many other DNA-binding proteins recognize more than one specific sequence. Among sequences recognized by a given DNA-binding protein, different positions exhibit varying degrees of conservation. The reason is that base pairs that are more extensively contacted by the protein tend to be more conserved. This observation can be used in the discovery of transcription factor binding sites. Here we present a rigorous means to accomplish this. In particular, we constrain the order of the information (entropy) in the columns of the position specific weight matrix (PWM) which characterizes the motif being sought. We then show how to compute the maximum likelihood estimate of a PWM under such order restrictions. This computation is easily integrated with the EM algorithm or the Gibbs sampler to enhance performance in the search for motifs in unaligned sequences. We demonstrate our method on a well-known data set of binding sites of the transcription factor Crp in E. coli.

On the basis of the observation that conserved positions in transcription factor binding sites are often clustered together, we propose a simple extension to the model-based motif discovery methods. We assign position-specific prior distributions to the frequency parameters of the model, penalizing deviations from a specified conservation profile. Examples with both simulated and real data show that this extension helps discover motifs as the data become noisier or when there is a competing false motif.

By using sequence information from an aligned protein family, a procedure is exhibited for finding sites that may be functionally or structurally critical to the protein. Features based on sequence conservation within subfamilies in the alignment and associations between sites are used to select the sites. The sites are subject to statistical evaluation correcting for phylogenetic bias in the collection of sequences. This method is applied to two families: the phycobiliproteins, light-harvesting proteins in cyanobacteria, red algae, and cryptomonads, and the globins that function in oxygen storage and transport. The sites identified by the procedure are located in key structural positions and merit further experimental study.