Improved statistical inference from DNA microarray data using analysis of variance and a Bayesian statistical framework. Analysis of global gene expression in Escherichia coli K12

PLS-based gene subset augmentation and tumor-specific gene identification

In the study of tumor disease pathogenesis, the identification of genes specifically expressed in disease states is pivotal, yet challenges arise from high-dimensional datasets with limited samples. Conventional gene (feature) selection methods often fall short of capturing the complexity of gene-phenotype and gene-gene interactions, necessitating a more robust analysis method. To address these challenges, a gene subset augmentation strategy is proposed in this paper. Our approach introduces diverse perturbation mechanisms to generate distinct gene subsets. The partial least squares-based multiple gene measurement algorithm considers gene-phenotype and gene-gene correlations, identifying differentially expressed genes, including those with weak signals. The constructed gene networks derived from the augmented subsets unveil regulatory patterns, enabling association analysis to explore gene associations comprehensively. Our algorithm excels in identifying small-sized gene subsets with strong discriminative power, surpassing traditional methods that yield a single gene subset. Unlike conventional approaches, our algorithm reveals a spectrum of different gene subsets and their weakly differentially expressed genes. This nuanced perspective aids in unraveling the molecular characteristics and specific expression patterns of tumor genes. The versatility of our approach not only contributes to the advancement of tumor-specific gene identification but also holds promise for addressing challenges in various fields characterized by high-dimensional datasets and limited samples. The Python implementation is available at http://github.com/wenjieyou/PLSGSA.

A Review of Feature Selection Methods for Machine Learning-Based Disease Risk Prediction

Machine learning has shown utility in detecting patterns within large, unstructured, and complex datasets. One of the promising applications of machine learning is in precision medicine, where disease risk is predicted using patient genetic data. However, creating an accurate prediction model based on genotype data remains challenging due to the so-called “curse of dimensionality” (i.e., extensively larger number of features compared to the number of samples). Therefore, the generalizability of machine learning models benefits from feature selection, which aims to extract only the most “informative” features and remove noisy “non-informative,” irrelevant and redundant features. In this article, we provide a general overview of the different feature selection methods, their advantages, disadvantages, and use cases, focusing on the detection of relevant features (i.e., SNPs) for disease risk prediction.

Feature Selection and Feature Stability Measurement Method for High-Dimensional Small Sample Data Based on Big Data Technology

With the rapid development of artificial intelligence in recent years, the research on image processing, text mining, and genome informatics has gradually deepened, and the mining of large-scale databases has begun to receive more and more attention. The objects of data mining have also become more complex, and the data dimensions of mining objects have become higher and higher. Compared with the ultra-high data dimensions, the number of samples available for analysis is too small, resulting in the production of high-dimensional small sample data. High-dimensional small sample data will bring serious dimensional disasters to the mining process. Through feature selection, redundancy and noise features in high-dimensional small sample data can be effectively eliminated, avoiding dimensional disasters and improving the actual efficiency of mining algorithms. However, the existing feature selection methods emphasize the classification or clustering performance of the feature selection results and ignore the stability of the feature selection results, which will lead to unstable feature selection results, and it is difficult to obtain real and understandable features. Based on the traditional feature selection method, this paper proposes an ensemble feature selection method, Random Bits Forest Recursive Clustering Eliminate (RBF-RCE) feature selection method, combined with multiple sets of basic classifiers to carry out parallel learning and screen out the best feature classification results, optimizes the classification performance of traditional feature selection methods, and can also improve the stability of feature selection. Then, this paper analyzes the reasons for the instability of feature selection and introduces a feature selection stability measurement method, the Intersection Measurement (IM), to evaluate whether the feature selection process is stable. The effectiveness of the proposed method is verified by experiments on several groups of high-dimensional small sample data sets.

Gene Correlation Guided Gene Selection for Microarray Data Classification

The microarray cancer data obtained by DNA microarray technology play an important role for cancer prevention, diagnosis, and treatment. However, predicting the different types of tumors is a challenging task since the sample size in microarray data is often small but the dimensionality is very high. Gene selection, which is an effective means, is aimed at mitigating the curse of dimensionality problem and can boost the classification accuracy of microarray data. However, many of previous gene selection methods focus on model design, but neglect the correlation between different genes. In this paper, we introduce a novel unsupervised gene selection method by taking the gene correlation into consideration, named gene correlation guided gene selection (G³CS). Specifically, we calculate the covariance of different gene dimension pairs and embed it into our unsupervised gene selection model to regularize the gene selection coefficient matrix. In such a manner, redundant genes can be effectively excluded. In addition, we utilize a matrix factorization term to exploit the cluster structure of original microarray data to assist the learning process. We design an iterative updating algorithm with convergence guarantee to solve the resultant optimization problem. Experimental results on six publicly available microarray datasets are conducted to validate the efficacy of our proposed method.

Personal Health Information Inference Using Machine Learning on RNA Expression Data from Patients With Cancer: Algorithm Validation Study

BACKGROUND

As the need for sharing genomic data grows, privacy issues and concerns, such as the ethics surrounding data sharing and disclosure of personal information, are raised.

OBJECTIVE

The main purpose of this study was to verify whether genomic data is sufficient to predict a patient's personal information.

METHODS

RNA expression data and matched patient personal information were collected from 9538 patients in The Cancer Genome Atlas program. Five personal information variables (age, gender, race, cancer type, and cancer stage) were recorded for each patient. Four different machine learning algorithms (support vector machine, decision tree, random forest, and artificial neural network) were used to determine whether a patient's personal information could be accurately predicted from RNA expression data. Performance measurement of the prediction models was based on the accuracy and area under the receiver operating characteristic curve. We selected five cancer types (breast carcinoma, kidney renal clear cell carcinoma, head and neck squamous cell carcinoma, low-grade glioma, and lung adenocarcinoma) with large samples sizes to verify whether predictive accuracy would differ between them. We also validated the efficacy of our four machine learning models in analyzing normal samples from 593 cancer patients.

RESULTS

In most samples, personal information with high genetic relevance, such as gender and cancer type, could be predicted from RNA expression data alone. The prediction accuracies for gender and cancer type, which were the best models, were 0.93-0.99 and 0.78-0.94, respectively. Other aspects of personal information, such as age, race, and cancer stage, were difficult to predict from RNA expression data, with accuracies ranging from 0.0026-0.29, 0.76-0.96, and 0.45-0.79, respectively. Among the tested machine learning methods, the highest predictive accuracy was obtained using the support vector machine algorithm (mean accuracy 0.77), while the lowest accuracy was obtained using the random forest method (mean accuracy 0.65). Gender and race were predicted more accurately than other variables in the samples. On average, the accuracy of cancer stage prediction ranged between 0.71-0.67, while the age prediction accuracy ranged between 0.18-0.23 for the five cancer types.

CONCLUSIONS

We attempted to predict patient information using RNA expression data. We found that some identifiers could be predicted, but most others could not. This study showed that personal information available from RNA expression data is limited and this information cannot be used to identify specific patients.

Metabolism of biosynthetic oligosaccharides by human-derived Bifidobacterium breve UCC2003 and Bifidobacterium longum NCIMB 8809

This work aimed to investigate the ability of two human-derived bifidobacterial strains, i.e. Bifidobacterium breve UCC2003 and Bifidobacterium longum NCIMB 8809, to utilize various oligosaccharides (i.e., 4-galactosyl-kojibiose, lactulosucrose, lactosyl-oligofructosides, raffinosyl-oligofructosides and lactulose-derived galacto-oligosaccharides) synthesized by means of microbial glycoside hydrolases. With the exception of raffinosyl-oligofructosides, these biosynthetic oligosaccharides were shown to support growth acting as a sole carbon and energy source of at least one of the two studied strains. Production of short-chain fatty acids (SCFAs) as detected by HPLC analysis corroborated the suitability of most of the studied novel oligosaccharides as fermentable growth substrates for the two bifidobacterial strains, showing that acetic acid is the main metabolic end product followed by lactic and formic acids. Transcriptomic and functional genomic approaches carried out for B. breve UCC2003 allowed the identification of key genes encoding glycoside hydrolases and carbohydrate transport systems involved in the metabolism of 4-galactosyl-kojibiose and lactulosucrose. In particular, the role of β-galactosidases in the hydrolysis of these particular trisaccharides was demonstrated, highlighting their importance in oligosaccharide metabolism by human bifidobacterial strains.

Transcriptional control of central carbon metabolic flux in Bifidobacteria by two functionally similar, yet distinct LacI-type regulators

Bifidobacteria resident in the gastrointestinal tract (GIT) are subject to constantly changing environmental conditions, which require rapid adjustments in gene expression. Here, we show that two predicted LacI-type transcription factors (TFs), designated AraQ and MalR1, are involved in regulating the central, carbohydrate-associated metabolic pathway (the so-called phosphoketolase pathway or bifid shunt) of the gut commensal Bifidobacterium breve UCC2003. These TFs appear to not only control transcription of genes involved in the bifid shunt and each other, but also seem to commonly and directly affect transcription of other TF-encoding genes, as well as genes related to uptake and metabolism of various carbohydrates. This complex and interactive network of AraQ/MalR1-mediated gene regulation provides previously unknown insights into the governance of carbon metabolism in bifidobacteria.

Metabolism of the predominant human milk oligosaccharide fucosyllactose by an infant gut commensal

A number of bifidobacterial species are found at a particularly high prevalence and abundance in faecal samples of healthy breastfed infants, a phenomenon that is believed to be, at least partially, due to the ability of bifidobacteria to metabolize Human Milk Oligosaccharides (HMOs). In the current study, we isolated a novel strain of Bifidobacterium kashiwanohense, named APCKJ1, from the faeces of a four-week old breastfed infant, based on the ability of the strain to utilise the HMO component fucosyllactose. We then determined the full genome sequence of this strain, and employed the generated data to analyze fucosyllactose metabolism in B. kashiwanohense APCKJ1. Transcriptomic and growth analyses, combined with metabolite analysis, in vitro hydrolysis assays and heterologous expression, allowed us to elucidate the pathway for fucosyllactose metabolism in B. kashiwanohense APCKJ1. Homologs of the key genes for this metabolic pathway were identified in particular in infant-derived members of the Bifdobacterium genus, revealing the apparent niche-specific nature of this pathway, and allowing a broad perspective on bifidobacterial fucosyllactose and L-fucose metabolism.

Prediction of regulatory targets of alternative isoforms of the epidermal growth factor receptor in a glioblastoma cell line

Background

The epidermal growth factor receptor (EGFR) is a major regulator of proliferation in tumor cells. Elevated expression levels of EGFR are associated with prognosis and clinical outcomes of patients in a variety of tumor types. There are at least four splice variants of the mRNA encoding four protein isoforms of EGFR in humans, named I through IV. EGFR isoform I is the full-length protein, whereas isoforms II-IV are shorter protein isoforms. Nevertheless, all EGFR isoforms bind the epidermal growth factor (EGF). Although EGFR is an essential target of long-established and successful tumor therapeutics, the exact function and biomarker potential of alternative EGFR isoforms II-IV are unclear, motivating more in-depth analyses. Hence, we analyzed transcriptome data from glioblastoma cell line SF767 to predict target genes regulated by EGFR isoforms II-IV, but not by EGFR isoform I nor other receptors such as HER2, HER3, or HER4.

Results

We analyzed the differential expression of potential target genes in a glioblastoma cell line in two nested RNAi experimental conditions and one negative control, contrasting expression with EGF stimulation against expression without EGF stimulation. In one RNAi experiment, we selectively knocked down EGFR splice variant I, while in the other we knocked down all four EGFR splice variants, so the associated effects of EGFR II-IV knock-down can only be inferred indirectly. For this type of nested experimental design, we developed a two-step bioinformatics approach based on the Bayesian Information Criterion for predicting putative target genes of EGFR isoforms II-IV. Finally, we experimentally validated a set of six putative target genes, and we found that qPCR validations confirmed the predictions in all cases.

Conclusions

By performing RNAi experiments for three poorly investigated EGFR isoforms, we were able to successfully predict 1140 putative target genes specifically regulated by EGFR isoforms II-IV using the developed Bayesian Gene Selection Criterion (BGSC) approach. This approach is easily utilizable for the analysis of data of other nested experimental designs, and we provide an implementation in R that is easily adaptable to similar data or experimental designs together with all raw datasets used in this study in the BGSC repository, https://github.com/GrosseLab/BGSC.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2944-9) contains supplementary material, which is available to authorized users.

Comparative genome and methylome analysis reveals restriction/modification system diversity in the gut commensal Bifidobacterium breve

Bifidobacterium breve represents one of the most abundant bifidobacterial species in the gastro-intestinal tract of breast-fed infants, where their presence is believed to exert beneficial effects. In the present study whole genome sequencing, employing the PacBio Single Molecule, Real-Time (SMRT) sequencing platform, combined with comparative genome analysis allowed the most extensive genetic investigation of this taxon. Our findings demonstrate that genes encoding Restriction/Modification (R/M) systems constitute a substantial part of the B. breve variable gene content (or variome). Using the methylome data generated by SMRT sequencing, combined with targeted Illumina bisulfite sequencing (BS-seq) and comparative genome analysis, we were able to detect methylation recognition motifs and assign these to identified B. breve R/M systems, where in several cases such assignments were confirmed by restriction analysis. Furthermore, we show that R/M systems typically impose a very significant barrier to genetic accessibility of B. breve strains, and that cloning of a methyltransferase-encoding gene may overcome such a barrier, thus allowing future functional investigations of members of this species.

Distinct Antibody Signatures Associated with Different Malaria Transmission Intensities in Zambia and Zimbabwe

Antibodies to Plasmodium falciparum are specific biomarkers that can be used to monitor parasite exposure over broader time frames than microscopy, rapid diagnostic tests, or molecular assays. Consequently, seroprevalence surveys can assist with monitoring the impact of malaria control interventions, particularly in the final stages of elimination, when parasite incidence is low. The protein array format to measure antibodies to diverse P. falciparum antigens requires only small sample volumes and is high throughput, permitting the monitoring of malaria transmission on large spatial and temporal scales. We expanded the use of a protein microarray to assess malaria transmission in settings beyond those with a low malaria incidence. Antibody responses in children and adults were profiled, using a P. falciparum protein microarray, through community-based surveys in three areas in Zambia and Zimbabwe at different stages of malaria control and elimination. These three epidemiological settings had distinct serological profiles reflective of their malaria transmission histories. While there was little correlation between transmission intensity and antibody signals (magnitude or breadth) in adults, there was a clear correlation in children younger than 5 years of age. Antibodies in adults appeared to be durable even in the absence of significant recent transmission, whereas antibodies in children provided a more accurate picture of recent levels of transmission intensity. Seroprevalence studies in children could provide a valuable marker of progress toward malaria elimination.IMPORTANCE As malaria approaches elimination in many areas of the world, monitoring the effect of control measures becomes more important but challenging. Low-level infections may go undetected by conventional tests that depend on parasitemia, particularly in immune individuals, who typically show no symptoms of malaria. In contrast, antibodies persist after parasitemia and may provide a more accurate picture of recent exposure. Only a few parasite antigens-mainly vaccine candidates-have been evaluated in seroepidemiological studies. We examined antibody responses to 500 different malaria proteins in blood samples collected through community-based surveillance from areas with low, medium, and high malaria transmission intensities. The breadth of the antibody responses in adults was broad in all three settings and was a poor correlate of recent exposure. In contrast, children represented a better sentinel population for monitoring recent malaria transmission. These data will help inform the use of multiplex serology for malaria surveillance.

Identification of Cytauxzoon felis antigens via protein microarray and assessment of expression library immunization against cytauxzoonosis

Background

Cytauxzoonosis is a disease of felids in North America caused by the tick-transmitted apicomplexan parasite Cytauxzoon felis. Cytauxzoonosis is particularly virulent for domestic cats, but no vaccine currently exists. The parasite cannot be cultivated in vitro, presenting a significant limitation for vaccine development.

Methods

Recent sequencing of the C. felis genome has identified over 4300 putative protein-encoding genes. From this pool we constructed a protein microarray containing 673 putative C. felis proteins. This microarray was probed with sera from C. felis-infected and naïve cats to identify differentially reactive antigens which were incorporated into two expression library vaccines, one polyvalent and one monovalent. We assessed the efficacy of these vaccines to prevent of infection and/or disease in a tick-challenge model.

Results

Probing of the protein microarray resulted in identification of 30 differentially reactive C. felis antigens that were incorporated into the two expression library vaccines. However, expression library immunization failed to prevent infection or disease in cats challenged with C. felis.

Conclusions

Protein microarray facilitated high-throughput identification of novel antigens, substantially increasing the pool of characterized C. felis antigens. These antigens should be considered for development of C. felis vaccines, diagnostics, and therapeutics.

Electronic supplementary material

The online version of this article (10.1186/s12014-018-9218-9) contains supplementary material, which is available to authorized users.

Carbohydrate Syntrophy enhances the establishment of Bifidobacterium breve UCC2003 in the neonatal gut

The non-digestible oligosaccharide fraction of maternal milk represents an important of carbohydrate and energy source for saccharolytic bifidobacteria in the gastrointestinal tract during early life. However, not all neonatal bifidobacteria isolates can directly metabolise the complex sialylated, fucosylated, sulphated and/or N-acetylglucosamine-containing oligosaccharide structures present in mothers milk. For some bifidobacterial strains, efficient carbohydrate syntrophy or crossfeeding is key to their establishment in the gut. In this study, we have adopted advanced functional genomic approaches to create single and double in-frame deletions of the N-acetyl glucosamine 6-phosphate deacetylase encoding genes, nagA1 and nagA2, of B. breve UCC2003. In vitro phenotypic analysis followed by in vivo studies on co-colonisation, mother to infant transmission, and evaluation of the relative co-establishment of B. bifidum and B. breve UCC2003 or UCC2003ΔnagA1ΔnagA2 in dam-reared neonatal mice demonstrates the importance of crossfeeding on sialic acid, fucose and N-acetylglucosamine-containing oligosaccharides for the establishment of B. breve UCC2003 in the neonatal gut. Furthermore, transcriptomic analysis of in vivo gene expression shows upregulation of genes associated with the utilisation of lactose, sialic acid, GlcNAc-6-S and fucose in B. breve UCC2003, while for UCC2003ΔnagA1ΔnagA2 only genes for lactose metabolism were upregulated.

Bifidobacterium breve UCC2003 Employs Multiple Transcriptional Regulators To Control Metabolism of Particular Human Milk Oligosaccharides

Bifidobacterial carbohydrate metabolism has been studied in considerable detail for a variety of both plant- and human-derived glycans, particularly involving the bifidobacterial prototype strain Bifidobacterium breve UCC2003. We recently elucidated the metabolic pathways by which the human milk oligosaccharide (HMO) constituents lacto-N-tetraose (LNT), lacto-N-neotetraose (LNnT) and lacto-N-biose (LNB) are utilized by B. breve UCC2003. However, to date, no work has been carried out on the regulatory mechanisms that control the expression of the genetic loci involved in these HMO metabolic pathways. In this study, we describe the characterization of three transcriptional regulators and the corresponding operator and associated (inducible) promoter sequences, with the latter governing the transcription of the genetic elements involved in LN(n)T/LNB metabolism. The activity of these regulators is dependent on the release of specific monosaccharides, which are believed to act as allosteric effectors and which are derived from the corresponding HMOs targeted by the particular locus.IMPORTANCE Human milk oligosaccharides (HMOs) are a key factor in the development of the breastfed-infant microbiota. They function as prebiotics, selecting for a specific range of microbes, including a number of infant-associated species of bifidobacteria, which are thought to provide a range of health benefits to the infant host. While much research has been carried out on elucidating the mechanisms of HMO metabolism in infant-associated bifidobacteria, to date there is very little understanding of the transcriptional regulation of these pathways. This study reveals a multicomponent transcriptional regulation system that controls the recently identified pathways of HMO metabolism in the infant-associated Bifidobacterium breve prototype strain UCC2003. This not only provides insight into the regulatory mechanisms present in other infant-associated bifidobacteria but also provides an example of a network of sequential steps regulating microbial carbohydrate metabolism.

Gene selection for microarray data classification via subspace learning and manifold regularization

With the rapid development of DNA microarray technology, large amount of genomic data has been generated. Classification of these microarray data is a challenge task since gene expression data are often with thousands of genes but a small number of samples. In this paper, an effective gene selection method is proposed to select the best subset of genes for microarray data with the irrelevant and redundant genes removed. Compared with original data, the selected gene subset can benefit the classification task. We formulate the gene selection task as a manifold regularized subspace learning problem. In detail, a projection matrix is used to project the original high dimensional microarray data into a lower dimensional subspace, with the constraint that the original genes can be well represented by the selected genes. Meanwhile, the local manifold structure of original data is preserved by a Laplacian graph regularization term on the low-dimensional data space. The projection matrix can serve as an importance indicator of different genes. An iterative update algorithm is developed for solving the problem. Experimental results on six publicly available microarray datasets and one clinical dataset demonstrate that the proposed method performs better when compared with other state-of-the-art methods in terms of microarray data classification. Graphical Abstract The graphical abstract of this work.

An Ensemble Feature Selection Method for Biomarker Discovery

Feature selection in Liquid Chromatography-Mass Spectrometry (LC-MS)-based metabolomics data (biomarker discovery) have become an important topic for machine learning researchers. High dimensionality and small sample size of LC-MS data make feature selection a challenging task. The goal of biomarker discovery is to select the few most discriminative features among a large number of irreverent ones. To improve the reliability of the discovered biomarkers, we use an ensemble-based approach. Ensemble learning can improve the accuracy of feature selection by combining multiple algorithms that have complementary information. In this paper, we propose an ensemble approach to combine the results of filter-based feature selection methods. To evaluate the proposed approach, we compared it to two commonly used methods, t-test and PLS-DA, using a real data set.

The effect of Nipped-B-like (Nipbl) haploinsufficiency on genome-wide cohesin binding and target gene expression: modeling Cornelia de Lange syndrome

BACKGROUND

Cornelia de Lange syndrome (CdLS) is a multisystem developmental disorder frequently associated with heterozygous loss-of-function mutations of Nipped-B-like (NIPBL), the human homolog of Drosophila Nipped-B. NIPBL loads cohesin onto chromatin. Cohesin mediates sister chromatid cohesion important for mitosis but is also increasingly recognized as a regulator of gene expression. In CdLS patient cells and animal models, expression changes of multiple genes with little or no sister chromatid cohesion defect suggests that disruption of gene regulation underlies this disorder. However, the effect of NIPBL haploinsufficiency on cohesin binding, and how this relates to the clinical presentation of CdLS, has not been fully investigated. Nipbl haploinsufficiency causes CdLS-like phenotype in mice. We examined genome-wide cohesin binding and its relationship to gene expression using mouse embryonic fibroblasts (MEFs) from Nipbl+/- mice that recapitulate the CdLS phenotype.

RESULTS

We found a global decrease in cohesin binding, including at CCCTC-binding factor (CTCF) binding sites and repeat regions. Cohesin-bound genes were found to be enriched for histone H3 lysine 4 trimethylation (H3K4me3) at their promoters; were disproportionately downregulated in Nipbl mutant MEFs; and displayed evidence of reduced promoter-enhancer interaction. The results suggest that gene activation is the primary cohesin function sensitive to Nipbl reduction. Over 50% of significantly dysregulated transcripts in mutant MEFs come from cohesin target genes, including genes involved in adipogenesis that have been implicated in contributing to the CdLS phenotype.

CONCLUSIONS

Decreased cohesin binding at the gene regions is directly linked to disease-specific expression changes. Taken together, our Nipbl haploinsufficiency model allows us to analyze the dosage effect of cohesin loading on CdLS development.

Genome-Wide Search for Genes Required for Bifidobacterial Growth under Iron-Limitation

Bacteria evolved over millennia in the presence of the vital micronutrient iron. Iron is involved in numerous processes within the cell and is essential for nearly all living organisms. The importance of iron to the survival of bacteria is obvious from the large variety of mechanisms by which iron may be acquired from the environment. Random mutagenesis and global gene expression profiling led to the identification of a number of genes, which are essential for Bifidobacterium breve UCC2003 survival under iron-restrictive conditions. These genes encode, among others, Fe-S cluster-associated proteins, a possible ferric iron reductase, a number of cell wall-associated proteins, and various DNA replication and repair proteins. In addition, our study identified several presumed iron uptake systems which were shown to be essential for B. breve UCC2003 growth under conditions of either ferric and/or ferrous iron chelation. Of these, two gene clusters encoding putative iron-uptake systems, bfeUO and sifABCDE, were further characterised, indicating that sifABCDE is involved in ferrous iron transport, while the bfeUO-encoded transport system imports both ferrous and ferric iron. Transcription studies showed that bfeUO and sifABCDE constitute two separate transcriptional units that are induced upon dipyridyl-mediated iron limitation. In the anaerobic gastrointestinal environment ferrous iron is presumed to be of most relevance, though a mutation in the sifABCDE cluster does not affect B. breve UCC2003's ability to colonise the gut of a murine model.

Robust differential expression analysis by learning discriminant boundary in multi-dimensional space of statistical attributes

Background

Performing statistical tests is an important step in analyzing genome-wide datasets for detecting genomic features differentially expressed between conditions. Each type of statistical test has its own advantages in characterizing certain aspects of differences between population means and often assumes a relatively simple data distribution (e.g., Gaussian, Poisson, negative binomial, etc.), which may not be well met by the datasets of interest. Making insufficient distributional assumptions can lead to inferior results when dealing with complex differential expression patterns.

Results

We propose to capture differential expression information more comprehensively by integrating multiple test statistics, each of which has relatively limited capacity to summarize the observed differential expression information. This work addresses a general application scenario, in which users want to detect as many as DEFs while requiring the false discovery rate (FDR) to be lower than a cut-off. We treat each test statistic as a basic attribute, and model the detection of differentially expressed genomic features as learning a discriminant boundary in a multi-dimensional space of basic attributes. We mathematically formulated our goal as a constrained optimization problem aiming to maximize discoveries satisfying a user-defined FDR. An effective algorithm, Discriminant-Cut, has been developed to solve an instantiation of this problem. Extensive comparisons of Discriminant-Cut with 13 existing methods were carried out to demonstrate its robustness and effectiveness.

Conclusions

We have developed a novel machine learning methodology for robust differential expression analysis, which can be a new avenue to significantly advance research on large-scale differential expression analysis.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1386-x) contains supplementary material, which is available to authorized users.

Bifidobacterium breve UCC2003 metabolises the human milk oligosaccharides lacto-N-tetraose and lacto-N-neo-tetraose through overlapping, yet distinct pathways

In this study, we demonstrate that the prototype B. breve strain UCC2003 possesses specific metabolic pathways for the utilisation of lacto-N-tetraose (LNT) and lacto-N-neotetraose (LNnT), which represent the central moieties of Type I and Type II human milk oligosaccharides (HMOs), respectively. Using a combination of experimental approaches, the enzymatic machinery involved in the metabolism of LNT and LNnT was identified and characterised. Homologs of the key genetic loci involved in the utilisation of these HMO substrates were identified in B. breve, B. bifidum, B. longum subsp. infantis and B. longum subsp. longum using bioinformatic analyses, and were shown to be variably present among other members of the Bifidobacterium genus, with a distinct pattern of conservation among human-associated bifidobacterial species.

Feature Subset Selection for Cancer Classification Using Weight Local Modularity

Microarray is recently becoming an important tool for profiling the global gene expression patterns of tissues. Gene selection is a popular technology for cancer classification that aims to identify a small number of informative genes from thousands of genes that may contribute to the occurrence of cancers to obtain a high predictive accuracy. This technique has been extensively studied in recent years. This study develops a novel feature selection (FS) method for gene subset selection by utilizing the Weight Local Modularity (WLM) in a complex network, called the WLMGS. In the proposed method, the discriminative power of gene subset is evaluated by using the weight local modularity of a weighted sample graph in the gene subset where the intra-class distance is small and the inter-class distance is large. A higher local modularity of the gene subset corresponds to a greater discriminative of the gene subset. With the use of forward search strategy, a more informative gene subset as a group can be selected for the classification process. Computational experiments show that the proposed algorithm can select a small subset of the predictive gene as a group while preserving classification accuracy.

Power and sample size estimation in high dimensional biology

Genomic scientists often test thousands of hypotheses in a single experiment. One example is a microarray experiment that seeks to determine differential gene expression among experimental groups. Planning such experiments involves a determination of sample size that will allow meaningful interpretations. Traditional power analysis methods may not be well suited to this task when thousands of hypotheses are tested in a discovery oriented basic research. We introduce the concept of expected discovery rate (EDR) and an approach that combines parametric mixture modelling with parametric bootstrapping to estimate the sample size needed for a desired accuracy of results. While the examples included are derived from microarray studies, the methods, herein, are ‘extraparadigmatic’ in the approach to study design and are applicable to most high dimensional biological situations. Pilot data from three different microarray experiments are used to extrapolate EDR as well as the related false discovery rate at different sample sizes and thresholds.

Evaluation of quantum dot immunofluorescence and a digital CMOS imaging system as an alternative to conventional organic fluorescence dyes and laser scanning for quantifying protein microarrays

Organic fluorescent dyes are widely used for the visualization of bound antibody in a variety of immunofluorescence assays. However, the detection equipment is often expensive, fragile, and hard to deploy widely. Quantum dots (Qdot) are nanocrystals made of semiconductor materials that emit light at different wavelengths according to the size of the crystal, with increased brightness and stability. Here, we have evaluated a small benchtop "personal" optical imager (ArrayCAM) developed for quantification of protein arrays probed by Qdot-based indirect immunofluorescence. The aim was to determine if the Qdot imager system provides equivalent data to the conventional organic dye-labeled antibody/laser scanner system. To do this, duplicate proteome microarrays of Vaccinia virus, Brucella melitensis and Plasmodium falciparum were probed with identical samples of immune sera, and IgG, IgA, and IgM profiles visualized using biotinylated secondary antibodies followed by a tertiary reagent of streptavidin coupled to either P3 (an organic cyanine dye typically used for microarrays) or Q800 (Qdot). The data show excellent correlation for all samples tested (R > 0.8) with no significant change of antibody reactivity profiles. We conclude that Qdot detection provides data equivalent to that obtained using conventional organic dye detection. The portable imager offers an economical, more robust, and deployable alternative to conventional laser array scanners.

Transcriptional Profile of Bacillus subtilis sigF-Mutant during Vegetative Growth

Sigma factor F is the first forespore specific transcription factor in Bacillus subtilis and controls genes required for the early stages of prespore development. The role of sigF is well studied under conditions that induce sporulation. Here, the impact of sigF disruption on the transcriptome of exponentially growing cultures is studied by micro-array analysis. Under these conditions that typically don’t induce sporulation, the transcriptome showed minor signs of sporulation initiation. The number of genes differentially expressed and the magnitude of expression were, as expected, quite small in comparison with sporulation conditions. The genes mildly down-regulated were mostly involved in anabolism and the genes mildly up-regulated, in particular fatty acid degradation genes, were mostly involved in catabolism. This is probably related to the arrest at sporulation stage II occurring in the sigF mutant, because continuation of growth from the formed disporic sporangia may require additional energy. The obtained knowledge is relevant for various experiments, such as industrial fermentation, prolonged experimental evolution or zero-growth studies, where sporulation is an undesirable trait that should be avoided, e.g by a sigF mutation.

Probing the regulatory effects of specific mutations in three major binding domains of the pleiotropic regulator CcpA of Bacillus subtilis

Carbon catabolite control is required for efficient use of available carbon sources to ensure rapid growth of bacteria. CcpA is a global regulator of carbon metabolism in Gram-positive bacteria like Bacillus subtilis. In this study the genome-wide gene regulation of a CcpA knockout and three specific CcpA mutants were studied by transcriptome analysis, to further elucidate the function of specific binding sites in CcpA. The following three amino acids were mutated to characterize their function: M17(R) which is involved in DNA binding, T62(H) which is important for the allosteric switch in CcpA upon HPr-Ser46-P binding, and R304(W) which is important for binding of the coeffectors HPr-Ser46-P and fructose-1,6-bisphosphate. The results confirm that CcpA was also involved in gene regulation in the absence of glucose. CcpA-M17R showed a small relief of Carbon Catabolite Control; the CcpA-M17R mutant regulates fewer genes than the CcpA-wt and the palindromicity of the cre site is less important for CcpA-M17R. CcpA-T62H was a stronger repressor than CcpA-wt and also acted as a strong repressor in the absence of glucose. CcpA-R304W was shown here to be less dependent on HPr-Ser46-P for its carbon catabolite control activities. The results presented here provide detailed information on alterations in gene regulation for each CcpA-mutant.

Identification of genes associated with resilience/vulnerability to sleep deprivation and starvation in Drosophila

BACKGROUND AND STUDY OBJECTIVES

Flies mutant for the canonical clock protein cycle (cyc(01)) exhibit a sleep rebound that is ∼10 times larger than wild-type flies and die after only 10 h of sleep deprivation. Surprisingly, when starved, cyc(01) mutants can remain awake for 28 h without demonstrating negative outcomes. Thus, we hypothesized that identifying transcripts that are differentially regulated between waking induced by sleep deprivation and waking induced by starvation would identify genes that underlie the deleterious effects of sleep deprivation and/or protect flies from the negative consequences of waking.

DESIGN

We used partial complementary DNA microarrays to identify transcripts that are differentially expressed between cyc(01) mutants that had been sleep deprived or starved for 7 h. We then used genetics to determine whether disrupting genes involved in lipid metabolism would exhibit alterations in their response to sleep deprivation.

SETTING

Laboratory.

PATIENTS OR PARTICIPANTS

Drosophila melanogaster.

INTERVENTIONS

Sleep deprivation and starvation.

MEASUREMENTS AND RESULTS

We identified 84 genes with transcript levels that were differentially modulated by 7 h of sleep deprivation and starvation in cyc(01) mutants and were confirmed in independent samples using quantitative polymerase chain reaction. Several of these genes were predicted to be lipid metabolism genes, including bubblegum, cueball, and CG4500, which based on our data we have renamed heimdall (hll). Using lipidomics we confirmed that knockdown of hll using RNA interference significantly decreased lipid stores. Importantly, genetically modifying bubblegum, cueball, or hll resulted in sleep rebound alterations following sleep deprivation compared to genetic background controls.

CONCLUSIONS

We have identified a set of genes that may confer resilience/vulnerability to sleep deprivation and demonstrate that genes involved in lipid metabolism modulate sleep homeostasis.

Mechanisms of growth and patterns of gene expression in oxygen-deprived rice coleoptiles

Coleoptiles of rice (Oryza sativa) seedlings grown under water commonly elongate by up to 1 mm h(-1) to reach the atmosphere. We initially analysed this highly specialized phenomenon by measuring epidermal cell lengths along the coleoptile axis to determine elongation rates. This revealed a cohort of cells in the basal zone that elongated rapidly following emergence from the embryo, reaching 200 μm within 12 h. After filming coleoptiles in vivo for a day, kinematic analysis was applied. Eight time-sliced 'segments' were defined by their emergence from the embryo at four-hourly intervals, revealing a mathematically simple growth model. Each segment entering the coleoptile from the embryo elongated at a constant velocity, resulting in accelerating growth for the entire organ. Consistent with the epidermal cell lengths, relative rates of elongation (mm mm(-1) h(-1)) were tenfold greater in the small, newly emerged basal segments than the older distal tip segments. This steep axial gradient defined two contrasting growth zones (bases versus tips) in which we measured ATP production and protein, RNA and DNA content, and analysed the global transcriptome under steady-state normoxia, hypoxia (3% O2) and anoxia. Determination of the transcriptome revealed tip-specific induction of genes encoding TCP [Teosinte Branched1 (Tb1) of maize, Cycloidea (Cyc), and Proliferating Cell Factor (Pcf)] transcription factors, RNA helicases, ribosomal proteins and proteins involved in protein folding, whilst expression of F-box domain-containing proteins in the ubiquitin E3-SCF complex (Skp, Cullin, F-box containing complex) was induced specifically in bases under low oxygen conditions. We ascribed the sustained elongation under hypoxia to hypoxia-specific responses such as controlled suppression of photosystem components and induction of RNA binding/splicing functions, indicating preferential allocation of energy to cell extension.

Mucosal effects of tenofovir 1% gel

Tenofovir gel is being evaluated for vaginal and rectal pre-exposure prophylaxis against HIV transmission. Because this is a new prevention strategy, we broadly assessed its effects on the mucosa. In MTN-007, a phase-1, randomized, double-blinded rectal microbicide trial, we used systems genomics/proteomics to determine the effect of tenofovir 1% gel, nonoxynol-9 2% gel, placebo gel or no treatment on rectal biopsies (15 subjects/arm). We also treated primary vaginal epithelial cells from four healthy women with tenofovir in vitro. After seven days of administration, tenofovir 1% gel had broad-ranging effects on the rectal mucosa, which were more pronounced than, but different from, those of the detergent nonoxynol-9. Tenofovir suppressed anti-inflammatory mediators, increased T cell densities, caused mitochondrial dysfunction, altered regulatory pathways of cell differentiation and survival, and stimulated epithelial cell proliferation. The breadth of mucosal changes induced by tenofovir indicates that its safety over longer-term topical use should be carefully monitored.

Transcription of two adjacent carbohydrate utilization gene clusters in Bifidobacterium breve UCC2003 is controlled by LacI- and repressor open reading frame kinase (ROK)-type regulators

Members of the genus Bifidobacterium are commonly found in the gastrointestinal tracts of mammals, including humans, where their growth is presumed to be dependent on various diet- and/or host-derived carbohydrates. To understand transcriptional control of bifidobacterial carbohydrate metabolism, we investigated two genetic carbohydrate utilization clusters dedicated to the metabolism of raffinose-type sugars and melezitose. Transcriptomic and gene inactivation approaches revealed that the raffinose utilization system is positively regulated by an activator protein, designated RafR. The gene cluster associated with melezitose metabolism was shown to be subject to direct negative control by a LacI-type transcriptional regulator, designated MelR1, in addition to apparent indirect negative control by means of a second LacI-type regulator, MelR2. In silico analysis, DNA-protein interaction, and primer extension studies revealed the MelR1 and MelR2 operator sequences, each of which is positioned just upstream of or overlapping the correspondingly regulated promoter sequences. Similar analyses identified the RafR binding operator sequence located upstream of the rafB promoter. This study indicates that transcriptional control of gene clusters involved in carbohydrate metabolism in bifidobacteria is subject to conserved regulatory systems, representing either positive or negative control.

Cross-feeding by Bifidobacterium breve UCC2003 during co-cultivation with Bifidobacterium bifidum PRL2010 in a mucin-based medium

BACKGROUND

Bifidobacteria constitute a specific group of commensal bacteria that commonly inhabit the mammalian gastrointestinal tract. Bifidobacterium breve UCC2003 was previously shown to utilize a variety of plant/diet/host-derived carbohydrates, including cellodextrin, starch and galactan, as well as the mucin and HMO-derived monosaccharide, sialic acid. In the current study, we investigated the ability of this strain to utilize parts of a host-derived source of carbohydrate, namely the mucin glycoprotein, when grown in co-culture with the mucin-degrading Bifidobacterium bifidum PRL2010.

RESULTS

B. breve UCC2003 was shown to exhibit growth properties in a mucin-based medium, but only when grown in the presence of B. bifidum PRL2010, which is known to metabolize mucin. A combination of HPAEC-PAD and transcriptome analyses identified some of the possible monosaccharides and oligosaccharides which support this enhanced co-cultivation growth/viability phenotype.

CONCLUSION

This study describes the potential existence of a gut commensal relationship between two bifidobacterial species. We demonstrate the in vitro ability of B. breve UCC2003 to cross-feed on sugars released by the mucin-degrading activity of B. bifidum PRL2010, thus advancing our knowledge on the metabolic adaptability which allows the former strain to colonize the (infant) gut by its extensive metabolic abilities to (co-)utilize available carbohydrate sources.

Toxicity prediction from toxicogenomic data based on class association rule mining

While the recent advent of new technologies in biology such as DNA microarray and next-generation sequencer has given researchers a large volume of data representing genome-wide biological responses, it is not necessarily easy to derive knowledge that is accurate and understandable at the same time. In this study, we applied the Classification Based on Association (CBA) algorithm, one of the class association rule mining techniques, to the TG-GATEs database, where both toxicogenomic and toxicological data of more than 150 compounds in rat and human are stored. We compared the generated classifiers between CBA and linear discriminant analysis (LDA) and showed that CBA is superior to LDA in terms of both predictive performances (accuracy: 83% for CBA vs. 75% for LDA, sensitivity: 82% for CBA vs. 72% for LDA, specificity: 85% for CBA vs. 75% for LDA) and interpretability.

Genetic dissection of the ity3 locus identifies a role for ncf2 co-expression modules and suggests selp as a candidate gene underlying the ity3.2 locus

Typhoid fever and salmonellosis, which are caused by Salmonella typhi and typhimurium, respectively, are responsible for significant morbidity and mortality in both developed and developing countries. We model typhoid fever using mice infected with Salmonella typhimurium, which results in a systemic disease, whereby the outcome of infection is variable in different inbred strains of mice. This model recapitulates several clinical aspects of the human disease and allows the study of the host response to Salmonella typhimurium infection in vivo. Previous work in our laboratory has identified three loci (Ity, Ity2, and Ity3) in the wild-derived MOLF/Ei mice influencing survival after infection with Salmonella typhimurium. Fine mapping of the Ity3 locus indicated that two sub-loci contribute collectively to the susceptibility of B6.MOLF-Ity/Ity3 congenic mice to Salmonella infection. In the current paper, we provided further evidence supporting a role for Ncf2 (neutrophil cytosolic factor 2 a subunit of NADPH oxidase) as the gene underlying the Ity3.1 sub-locus. Gene expression profiling indicated that the Ity3.1 sub-locus defined a global gene expression signature with networks articulated around Ncf2. Furthermore, based on differential expression and complementation analysis using Selp (selectin-P) knock-out mice, Selp was identified as a strong candidate gene for the Ity3.2 sub-locus.

Large-scale gene expression profiling data for the model moss Physcomitrella patens aid understanding of developmental progression, culture and stress conditions

The moss Physcomitrella patens is an important model organism for studying plant evolution, development, physiology and biotechnology. Here we have generated microarray gene expression data covering the principal developmental stages, culture forms and some environmental/stress conditions. Example analyses of developmental stages and growth conditions as well as abiotic stress treatments demonstrate that (i) growth stage is dominant over culture conditions, (ii) liquid culture is not stressful for the plant, (iii) low pH might aid protoplastation by reduced expression of cell wall structure genes, (iv) largely the same gene pool mediates response to dehydration and rehydration, and (v) AP2/EREBP transcription factors play important roles in stress response reactions. With regard to the AP2 gene family, phylogenetic analysis and comparison with Arabidopsis thaliana shows commonalities as well as uniquely expressed family members under drought, light perturbations and protoplastation. Gene expression profiles for P. patens are available for the scientific community via the easy-to-use tool at https://www.genevestigator.com. By providing large-scale expression profiles, the usability of this model organism is further enhanced, for example by enabling selection of control genes for quantitative real-time PCR. Now, gene expression levels across a broad range of conditions can be accessed online for P. patens.

Lactobacillus reuteri 100-23 modulates urea hydrolysis in the murine stomach

Comparisons of in vivo (mouse stomach) and in vitro (laboratory culture) transcriptomes of Lactobacillus reuteri strain 100-23 were made by microarray analysis. These comparisons revealed the upregulation of genes associated with acid tolerance, including urease production, in the mouse stomach. Inactivation of the ureC gene reduced the acid tolerance of strain 100-23 in vitro, and the mutant was outcompeted by the wild type in the gut of ex-Lactobacillus-free mice. Urine analysis showed that stable isotope-labeled urea, administered by gavage, was metabolized to a greater extent in Lactobacillus-free mice than animals colonized by strain 100-23. This surprising observation was associated with higher levels of urease activity and fecal-type bacteria in the stomach digesta of Lactobacillus-free mice. Despite the modulation of urea hydrolysis in the stomach, recycling of urea nitrogen in the murine host was not affected since the essential amino acid isoleucine, labeled with a stable isotope, was detected in the livers of both Lactobacillus-free and 100-23-colonized animals. Therefore, our experiments reveal a new and unexpected impact of Lactobacillus colonization on urea hydrolysis in the murine gut.

Transcriptional mechanisms link epithelial plasticity to adhesion and differentiation of epidermal progenitor cells

During epithelial tissue morphogenesis, developmental progenitor cells undergo dynamic adhesive and cytoskeletal remodeling to trigger proliferation and migration. Transcriptional mechanisms that restrict such a mild form of epithelial plasticity to maintain lineage-restricted differentiation in committed epithelial tissues are poorly understood. Here, we report that simultaneous ablation of transcriptional repressor-encoding Ovol1 and Ovol2 results in expansion and blocked terminal differentiation of embryonic epidermal progenitor cells. Conversely, mice overexpressing Ovol2 in their skin epithelia exhibit precocious differentiation accompanied by smaller progenitor cell compartments. We show that Ovol1/Ovol2-deficient epidermal cells fail to undertake α-catenin-driven actin cytoskeletal reorganization and adhesive maturation and exhibit changes that resemble epithelial-to-mesenchymal transition (EMT). Remarkably, these alterations and defective terminal differentiation are reversed upon depletion of EMT-promoting transcriptional factor Zeb1. Collectively, our findings reveal Ovol-Zeb1-α-catenin sequential repression and highlight Ovol1 and Ovol2 as gatekeepers of epithelial adhesion and differentiation by inhibiting progenitor-like traits and epithelial plasticity.

Autoinducer-2 plays a crucial role in gut colonization and probiotic functionality of Bifidobacterium breve UCC2003

In the present study we show that luxS of Bifidobacterium breve UCC2003 is involved in the production of the interspecies signaling molecule autoinducer-2 (AI-2), and that this gene is essential for gastrointestinal colonization of a murine host, while it is also involved in providing protection against Salmonella infection in Caenorhabditis elegans. We demonstrate that a B. breve luxS-insertion mutant is significantly more susceptible to iron chelators than the WT strain and that this sensitivity can be partially reverted in the presence of the AI-2 precursor DPD. Furthermore, we show that several genes of an iron starvation-induced gene cluster, which are downregulated in the luxS-insertion mutant and which encodes a presumed iron-uptake system, are transcriptionally upregulated under in vivo conditions. Mutation of two genes of this cluster in B. breve UCC2003 renders the derived mutant strains sensitive to iron chelators while deficient in their ability to confer gut pathogen protection to Salmonella-infected nematodes. Since a functional luxS gene is present in all tested members of the genus Bifidobacterium, we conclude that bifidobacteria operate a LuxS-mediated system for gut colonization and pathogen protection that is correlated with iron acquisition.

Metabolism of sialic acid by Bifidobacterium breve UCC2003

Bifidobacteria constitute a specific group of commensal bacteria that inhabit the gastrointestinal tracts of humans and other mammals. Bifidobacterium breve UCC2003 has previously been shown to utilize several plant-derived carbohydrates that include cellodextrins, starch, and galactan. In the present study, we investigated the ability of this strain to utilize the mucin- and human milk oligosaccharide (HMO)-derived carbohydrate sialic acid. Using a combination of transcriptomic and functional genomic approaches, we identified a gene cluster dedicated to the uptake and metabolism of sialic acid. Furthermore, we demonstrate that B. breve UCC2003 can cross feed on sialic acid derived from the metabolism of 3'-sialyllactose, an abundant HMO, by another infant gut bifidobacterial strain, Bifidobacterium bifidum PRL2010.

Immunoproteomic analysis of antibody in lymphocyte supernatant in patients with typhoid fever in Bangladesh

We have previously shown that an assay based on detection of anti-Salmonella enterica serotype Typhi antibodies in supernatant of lymphocytes harvested from patients presenting with typhoid fever (antibody in lymphocyte supernatant [ALS] assay) can identify 100% of patients with blood culture-confirmed typhoid fever in Bangladesh. In order to define immunodominant proteins within the S. Typhi membrane preparation used as antigen in these prior studies and to identify potential biomarkers unique to S. Typhi bacteremic patients, we probed microarrays containing 2,724 S. Typhi proteins with ALS collected at the time of clinical presentation from 10 Bangladeshis with acute typhoid fever. We identified 62 immunoreactive antigens when evaluating both the IgG and IgA responses. Immune responses to 10 of these antigens discriminated between individuals with acute typhoid infection and healthy control individuals from areas where typhoid infection is endemic, as well as Bangladeshi patients presenting with fever who were subsequently confirmed to have a nontyphoid illness. Using an ALS enzyme-linked immunosorbent assay (ELISA) format and purified antigen, we then confirmed that immune responses against the antigen with the highest immunoreactivity (hemolysin E [HlyE]) correctly identified individuals with acute typhoid or paratyphoid fever in Dhaka, Bangladesh. These observations suggest that purified antigens could be used with ALS and corresponding acute-phase activated B lymphocytes in diagnostic platforms to identify acutely infected patients, even in areas where enteric fever is endemic.

Pre-erythrocytic antibody profiles induced by controlled human malaria infections in healthy volunteers under chloroquine prophylaxis

Complete sterile protection to Plasmodium falciparum (Pf) infection mediated by pre-erythrocytic immunity can be experimentally induced under chloroquine prophylaxis, through immunization with sporozoites from infected mosquitoes' bites (CPS protocol). To characterize the profile of CPS induced antibody (Ab) responses, we developed a proteome microarray containing 809 Pf antigens showing a distinct Ab profile with recognition of antigens expressed in pre-erythrocytic life-cycle stages. In contrast, plasma from naturally exposed semi-immune individuals from Kenya was skewed toward antibody reactivity against asexual blood stage antigens. CPS-immunized and semi-immune individuals generated antibodies against 192 and 202 Pf antigens, respectively, but only 60 antigens overlapped between the two groups. Although the number of reactive antigens varied between the CPS-immunized individuals, all volunteers reacted strongly against the pre-erythrocytic antigens circumsporozoite protein (CSP) and liver stage antigen 1 (LSA1). Well classified merozoite and erythrocytic antigens were strongly reactive in semi-immune individuals but lacking in the CPS immunized group. These data show that the antibody profile of CPS-immunized and semi-immune groups have quite distinct profiles reflecting their protective immunity; antibodies from CPS immunized individuals react strongly against pre-erythrocytic while semi-immune individuals mainly react against erythrocytic antigens.

Metabolism of four α-glycosidic linkage-containing oligosaccharides by Bifidobacterium breve UCC2003

Members of the genus Bifidobacterium are common inhabitants of the gastrointestinal tracts of humans and other mammals, where they ferment many diet-derived carbohydrates that cannot be digested by their hosts. To extend our understanding of bifidobacterial carbohydrate utilization, we investigated the molecular mechanisms by which 11 strains of Bifidobacterium breve metabolize four distinct α-glucose- and/or α-galactose-containing oligosaccharides, namely, raffinose, stachyose, melibiose, and melezitose. Here we demonstrate that all B. breve strains examined possess the ability to utilize raffinose, stachyose, and melibiose. However, the ability to metabolize melezitose was not common to all B. breve strains tested. Transcriptomic and functional genomic approaches identified a gene cluster dedicated to the metabolism of α-galactose-containing carbohydrates, while an adjacent gene cluster, dedicated to the metabolism of α-glucose-containing melezitose, was identified in strains that are able to use this carbohydrate.

A membrane-bound NAC transcription factor, ANAC017, mediates mitochondrial retrograde signaling in Arabidopsis

Plants require daily coordinated regulation of energy metabolism for optimal growth and survival and therefore need to integrate cellular responses with both mitochondrial and plastid retrograde signaling. Using a forward genetic screen to characterize regulators of alternative oxidase1a (rao) mutants, we identified RAO2/Arabidopsis NAC domain-containing protein17 (ANAC017) as a direct positive regulator of AOX1a. RAO2/ANAC017 is targeted to connections and junctions in the endoplasmic reticulum (ER) and F-actin via a C-terminal transmembrane (TM) domain. A consensus rhomboid protease cleavage site is present in ANAC017 just prior to the predicted TM domain. Furthermore, addition of the rhomboid protease inhibitor N-p-Tosyl-l-Phe chloromethyl abolishes the induction of AOX1a upon antimycin A treatment. Simultaneous fluorescent tagging of ANAC017 with N-terminal red fluorescent protein (RFP) and C-terminal green fluorescent protein (GFP) revealed that the N-terminal RFP domain migrated into the nucleus, while the C-terminal GFP tag remained in the ER. Genome-wide analysis of the transcriptional network regulated by RAO2/ANAC017 under stress treatment revealed that RAO2/ANAC017 function was necessary for >85% of the changes observed as a primary response to cytosolic hydrogen peroxide (H2O2), but only ~33% of transcriptional changes observed in response to antimycin A treatment. Plants with mutated rao2/anac017 were more stress sensitive, whereas a gain-of-function mutation resulted in plants that had lower cellular levels of H2O2 under untreated conditions.

MicroRNA profiling implicates the insulin-like growth factor pathway in bleomycin-induced pulmonary fibrosis in mice

BACKGROUND

Idiopathic pulmonary fibrosis is a disease characterized by alveolar epithelial cell injury, inflammatory cell infiltration and deposition of extracellular matrix in lung tissue. As mouse models of bleomycin-induced pulmonary fibrosis display many of the same phenotypes observed in patients with idiopathic pulmonary fibrosis, they have been used to study various aspects of the disease, including altered expression of microRNAs.

RESULTS

In this work, microRNA expression profiling of the lungs from treated C57BL/6J mice, relative to that of untreated controls, was undertaken to determine which alterations in microRNAs could in part regulate the fibrosis phenotype induced by bleomycin delivered through mini-osmotic pumps. We identified 11 microRNAs, including miR-21 and miR-34a, to be significantly differentially expressed (P < 0.01) in lungs of bleomycin treated mice and confirmed these data with real time PCR measurements. In situ hybridization of both miR-21 and miR-34a indicated that they were expressed in alveolar macrophages. Using a previously reported gene expression profile, we identified 195 genes to be both predicted targets of the 11 microRNAs and of altered expression in bleomycin-induced lung disease of C57BL/6J mice. Pathway analysis with these 195 genes indicated that altered microRNA expression may be associated with hepatocyte growth factor signaling, cholecystokinin/gastrin-mediated signaling, and insulin-like growth factor (IGF-1) signaling, among others, in fibrotic lung disease. The relevance of the IGF-1 pathway in this model was then demonstrated by showing lung tissue of bleomycin treated C57BL/6J mice had increased expression of Igf1 and that increased numbers of Igf-1 positive cells, predominantly in macrophages, were detected in the lungs.

CONCLUSIONS

We conclude that altered microRNA expression in macrophages is a feature which putatively influences the insulin-like growth factor signaling component of bleomycin-induced pulmonary fibrosis.

Geographic distribution and taxonomic circumscription of populations within Coryphantha section Robustispina (Cactaceae)

PREMISE OF STUDY

Taxonomic circumscription of subspecific taxa within Coryphantha robustispina was evaluated with morphological data and microsatellites. This study was the first to compare adequately sampled morphological and DNA analyses at the population level in the Cactaceae. This comparison was important to test reliability of both methods and to gain a better understanding of phytogeography, evolution, and systematics of the species, knowledge that could prove useful for other taxa as well. Populations of C. robustispina subsp. robustispina are listed as endangered by the U. S. Fish and Wildlife Service. Our primary goal was to explore correlations among geographical distribution, morphology, and genetics of selected populations throughout the range of the species and the outgroup, C. poselgeriana. •

METHODS

Stem characters were measured for 638 individuals among 16 populations. Flower characters were measured for 180 individuals among 12 populations. Ten microsatellite DNA loci were isolated and characterized for 204 individuals among 13 populations. Data were analyzed using various multivariate analyses. •

RESULTS

Our results indicated that, within Coryphantha robustispina, there were three morphologically, genetically, and geographically coherent groups represented by the names C. robustispina subsp. robustispina, C. robustispina subsp. uncinata, and C. robustispina subsp. scheeri. For most analyses, distinctions among the three groups were primarily not as great as those between any one of them and the outgroup. •

CONCLUSIONS

Results suggested that the three subspecific taxa within Coryphantha robustispina are good subspecies but should not be elevated to species rank. The closely aligned results between morphology and microsatellite data support the design and utility of both methods.

How unique is the low oxygen response? An analysis of the anaerobic response during germination and comparison with abiotic stress in rice and Arabidopsis

Plants face a variety of environmental stresses and have evolved molecular mechanisms to survive these challenges. One of these stresses is low oxygen conditions, which can occur under flooding conditions. Rice (Oryza sativa) is somewhat unique for its ability to tolerate and even germinate under low to no oxygen conditions. In this study, we examined global transcriptomic responses over the course of germination and in response to low oxygen and other abiotic stress in rice and Arabidopsis (Arabidopsis thaliana). Over 150 microarray datasets were analyzed in parallel to determine just how unique the low oxygen response is in rice. Comparison of aerobic germination in rice and Arabidopsis, with anaerobic germination in rice revealed conserved transcriptomic responses that are not only conserved across both species but also occur in the absence of oxygen in rice. Thus, these genes may represent functions necessary for the developmental progression of germination, whether or not oxygen is present in rice. Analysis of genes that responded differently in rice compared to Arabidopsis revealed responses specific to anaerobic germination in rice, including the down-regulation of genes encoding redox functions and up-regulation of receptor kinases. Comparison of a range of hypoxia/anoxia studies within and across Arabidopsis and rice revealed both conserved and species specific changes in gene expression (e.g., Arabidopsis specific up-regulation of WRKYs and rice specific down-regulation of heme), unveiling unique transcriptomic signatures of the low oxygen response. Lastly, a comparison of the low oxygen response with cold, salt, drought and heat stress revealed some similarity with the response to heat stress in Arabidopsis, which was not seen in rice. Comparison of these heat-responsive, abiotic stress marker genes in Arabidopsis with their rice orthologs revealed that while low oxygen may be perceived as an abiotic stress in Arabidopsis, this is not the case in rice.

Transcriptional regulation of fatty acid biosynthesis in Lactococcus lactis

Here we study the influence of the putative fatty acid biosynthesis (FAB) regulator FabT (originally called RmaG [Llmg_1788]) on gene transcription in Lactococcus lactis MG1363. A strain with a knockout mutation of the putative regulator was constructed, and its transcriptome was compared to that of the wild-type strain. Almost all FAB genes were significantly upregulated in the knockout. Using electrophoretic mobility shift assays (EMSAs) and DNase I footprinting, the binding motif of the regulator and the binding locations in the genome were characterized. Fatty acid composition analysis revealed that a strain lacking FabT contained significantly more saturated acyl chains in its phospholipids. This observation demonstrates that the vital pathway of FAB in L. lactis is regulated by the repressor FabT.

Transcriptional and functional characterization of genetic elements involved in galacto-oligosaccharide utilization by Bifidobacterium breve UCC2003

Several prebiotics, such as inulin, fructo-oligosaccharides and galacto-oligosaccharides, are widely used commercially in foods and there is convincing evidence, in particular for galacto-oligosaccharides, that prebiotics can modulate the microbiota and promote bifidobacterial growth in the intestinal tract of infants and adults. In this study we describe the identification and functional characterization of the genetic loci responsible for the transport and metabolism of purified galacto-oligosaccharides (PGOS) by Bifidobacterium breve UCC2003. We further demonstrate that an extracellular endogalactanase specified by several B. breve strains, including B. breve UCC2003, is essential for partial degradation of PGOS components with a high degree of polymerization. These partially hydrolysed PGOS components are presumed to be transported into the bifidobacterial cell via various ABC transport systems and sugar permeases where they are further degraded to galactose and glucose monomers that feed into the bifid shunt. This work significantly advances our molecular understanding of bifidobacterial PGOS metabolism and its associated genetic machinery to utilize this prebiotic.

Dietary fat composition influences tissue lipid profile and gene expression in Fischer-344 rats

The AIN-76A diet causes fatty liver in rodents when fed for long periods of time. The aim of this study was to utilize fatty acid analysis and transcriptomics to investigate the effects of different fat sources in the AIN-76A diet on tissue lipid profiles and gene expression in male, weanling Fischer-344 rats. Animals were fed isocaloric diets that differed only in the fat source: (1) corn oil (CO) (2) anhydrous milk fat (AMF), and (3) AMF supplemented with 10% phospholipids from the milk fat globule membrane (AMF-MFGM). There were no differences in food intake, body weight, growth rate, or body fat composition among the groups, and the fatty acid compositions of red blood cells (RBC), plasma, muscle, and visceral adipose tissues reflected the dietary fat sources. Modifying the fat source resulted in 293 genes differentially regulated in skeletal muscle, 1,124 in adipose, and 831 in liver as determined by analysis of variance (ANOVA). Although tissue fatty acid profiles mostly reflected the diet, there were several quantitative differences in lipid classes in the liver and plasma. The AMF diet resulted in the highest level of hepatic triacylglycerols, but the lowest level in plasma. The CO diet resulted in significant accumulation of hepatic unesterified fatty acids and decreased DGAT expression and activity, a potential trigger for steatohepatitis. These results indicate that the fatty acid composition and presence of polar lipids in the AIN-76A diets have significant effects on lipid partitioning, gene expression, and potentially the development of liver pathology.