Thomas Skov

Reduced diversity of the intestinal microbiota during infancy is associated with increased risk of allergic disease at school age

BACKGROUND Changes in the human microbiome have been suggested as a risk factor for a number of lifestyle-related disorders, such as atopic diseases, possibly through a modifying influence on immune maturation in infancy. OBJECTIVES We aimed to explore the association between neonatal fecal flora and the development of atopic disorders until age 6 years, hypothesizing that the diversity of the intestinal microbiota influences disease development. METHODS We studied the intestinal microbiota in infants in the Copenhagen Prospective Study on Asthma in Childhood, a clinical study of a birth cohort of 411 high-risk children followed for 6 years by clinical assessments at 6-month intervals, as well as at acute symptom exacerbations. Bacterial flora was analyzed at 1 and 12 months of age by using molecular techniques based on 16S rRNA PCR combined with denaturing gradient gel electrophoresis, as well as conventional culturing. The main outcome measures were the development of allergic sensitization (skin test and specific serum IgE), allergic rhinitis, peripheral blood eosinophil counts, asthma, and atopic dermatitis during the first 6 years of life. RESULTS We found that bacterial diversity in the early intestinal flora 1 and 12 months after birth was inversely associated with the risk of allergic sensitization (serum specific IgE P = .003; skin prick test P = .017), peripheral blood eosinophils (P = .034), and allergic rhinitis (P = .007). There was no association with the development of asthma or atopic dermatitis. CONCLUSIONS Reduced bacterial diversity of the infants intestinal flora was associated with increased risk of allergic sensitization, allergic rhinitis, and peripheral blood eosinophilia, but not asthma or atopic dermatitis, in the first 6 years of life. These results support the general hypothesis that an imbalance in the intestinal microbiome is influencing the development of lifestyle-related disorders, such as allergic disease.

Establishment of Intestinal Microbiota during Early Life: a Longitudinal, Explorative Study of a Large Cohort of Danish Infants

ABSTRACT Fecal samples were obtained from a cohort of 330 healthy Danish infants at 9, 18, and 36 months after birth, enabling characterization of interbacterial relationships by use of quantitative PCR targeting 31 selected bacterial 16S rRNA gene targets representing different phylogenetic levels. Nutritional parameters and measures of growth and body composition were determined and investigated in relation to the observed development in microbiota composition. We found that significant changes in the gut microbiota occurred, particularly from age 9 to 18 months, when cessation of breastfeeding and introduction of a complementary feeding induce replacement of a microbiota characterized by lactobacilli, bifidobacteria, and Enterobacteriaceae with a microbiota dominated by Clostridium spp. and Bacteroides spp. Classification of samples by a proxy enterotype based on the relative levels of Bacteroides spp. and Prevotella spp. showed that enterotype establishment occurs between 9 and 36 months. Thirty percent of the individuals shifted enterotype between 18 and 36 months. The composition of the microbiota was most pronouncedly influenced by the time of cessation of breastfeeding. From 9 to 18 months, a positive correlation was observed between the increase in body mass index and the increase of the short-chain-fatty-acid-producing clostridia, the Clostridum leptum group, and Eubacterium hallii. Considering previously established positive associations between rapid infant weight gain, early breastfeeding discontinuation, and later-life obesity, the corresponding microbial findings seen here warrant attention.

ChroMATHography: Solving Chromatographic Issues with Mathematical Models and Intuitive Graphics

1.3. Objectives of This Work 4584 2. Chromatographic Data 4584 2.1. The Chromatographic Signal 4584 2.2. Common Chromatographic Artifacts 4584 2.3. A Basic Model of Chromatographic Data 4585 3. Removing Artifacts by Preprocessing 4587 3.1. Baseline Offset/Background Contribution 4587 3.1.1. Curve-Fitting 4587 3.1.2. Factor Model Approach 4588 3.1.3. Limitations and Things To Consider 4589 3.2. Retention Time Shifts across Samples 4589 3.2.1. Synchronizing Signals 4589 3.2.2. Factor Model Approach 4592 3.2.3. Limitations and Things To Consider 4593 4. Co-eluting Peaks 4593 4.1. The Importance of Multichannel Detectors To Solve Overlapping Issues 4595

Handling within run retention time shifts in two-dimensional chromatography data using shift correction and modeling

The use of PARAFAC for modeling GC x GC-TOFMS peaks is well documented. This success is due to the trilinear structure of these data under ideal, or sufficiently close to ideal, chromatographic conditions. However, using temperature programming to cope with the general elution problem, deviations from trilinearity within a run are more likely to be seen for the following three cases: (1) compounds (i.e., analytes) severely broadened on the first column hence defined by many modulation periods, (2) analytes with a very high retention factor on the second column and likely wrapped around in that dimension, or (3) with fast temperature program rates. This deviation from trilinearity is seen as retention time-shifted peak profiles in subsequent modulation periods (first column fractions). In this report, a relaxed yet powerful version of PARAFAC, known as PARAFAC2 has been applied to handle this shift within the model step by allowing generation of individual peak profiles in subsequent first column fractions. An alternative approach was also studied, utilizing a standard retention time shift correction to restore the data trilinearity structure followed by PARAFAC. These two approaches are compared when identifying and quantifying a known analyte over a large concentration series where a certain shift is simulated in the successive first column fractions. Finally, the methods are applied to real chromatographic data showing severely shifted peak profiles. The pros and cons of the presented approaches are discussed in relation to the model parameters, the signal-to-noise ratio and the degree of shift.

The Effect of LC-MS Data Preprocessing Methods on the Selection of Plasma Biomarkers in Fed vs. Fasted Rats.

The metabolic composition of plasma is affected by time passed since the last meal and by individual variation in metabolite clearance rates. Rat plasma in fed and fasted states was analyzed with liquid chromatography quadrupole-time-of-flight mass spectrometry (LC-QTOF) for an untargeted investigation of these metabolite patterns. The dataset was used to investigate the effect of data preprocessing on biomarker selection using three different softwares, MarkerLynxTM, MZmine, XCMS along with a customized preprocessing method that performs binning of m/z channels followed by summation through retention time. Direct comparison of selected features representing the fed or fasted state showed large differences between the softwares. Many false positive markers were obtained from custom data preprocessing compared with dedicated softwares while MarkerLynxTM provided better coverage of markers. However, marker selection was more reliable with the gap filling (or peak finding) algorithms present in MZmine and XCMS. Further identification of the putative markers revealed that many of the differences between the markers selected were due to variations in features representing adducts or daughter ions of the same metabolites or of compounds from the same chemical subclasses, e.g., lyso-phosphatidylcholines (LPCs) and lyso-phosphatidylethanolamines (LPEs). We conclude that despite considerable differences in the performance of the preprocessing tools we could extract the same biological information by any of them. Carnitine, branched-chain amino acids, LPCs and LPEs were identified by all methods as markers of the fed state whereas acetylcarnitine was abundant during fasting in rats.

Multiblock variance partitioning: A new approach for comparing variation in multiple data blocks

More than one multi-informative analytical technique is often applied when describing the condition of a set of samples. Often a part of the information found in these data blocks is redundant and can be extracted from more blocks. This study puts forward a method (multiblock variance partitioning-MVP) to compare the information/variation in different data blocks using simple quantitative measures. These measures are the unique part of the variation only found in one data block and the common part that can be found in more data blocks. These different parts are found using PLS models between predictor blocks and a common response. MVP provides a different view on the information in different blocks than normal multiblock analysis. It will be shown that this has many applications in very diverse fields such as process control, assessor performance in sensory analysis, efficiency of preprocessing methods and as complementary information to an interval PLS analysis. Here the ideas of the MVP approach are presented in detail using a study of red wines from different regions measured with GC-MS and FT-IR instruments providing different kinds of data representations.

Quantification of individual fatty acids in bovine milk by infrared spectroscopy and chemometrics: Understanding predictions of highly collinear reference variables

Predicting individual fatty acids (FA) in bovine milk from Fourier transform infrared (FT-IR) measurements is desirable. However, such predictions may rely on covariance structures among individual FA and total fat content. These covariance structures may change with factors such as breed and feed, among others. The aim of this study was to estimate how spectral variation associated with total fat content and breed contributes to predictions of individual FA. This study comprised 890 bovine milk samples from 2 breeds (455 Holstein and 435 Jersey). Holstein samples were collected from 20 Danish dairy herds from October to December 2009; Jersey samples were collected from 22 Danish dairy herds from February to April 2010. All samples were from conventional herds and taken while cows were housed. Moreover, in a spiking experiment, FA (C14:0, C16:0, and C18:1 cis-9) were added (spiked) to a background of commercial skim milk to determine whether signals specific to those individual FA could be obtained from the FT-IR measurements. This study demonstrated that variation associated with total fat content and breed was responsible for successful FT-IR-based predictions of FA in the raw milk samples. This was confirmed in the spiking experiment, which showed that signals specific to individual FA could not be identified in FT-IR measurements when several FA were present in the same mixture. Hence, predicted concentrations of individual FA in milk rely on covariance structures with total fat content rather than absorption bands directly associated with individual FA. If covariance structures between FA and total fat used to calibrate partial least squares (PLS) models are not conserved in future samples, these samples will show incorrect and biased FA predictions. This was demonstrated by using samples of one breed to calibrate and samples of the other breed to validate PLS models for individual FA. The 2 breeds had different covariance structures between individual FA and total fat content. The results showed that the validation samples yielded biased predictions. This may limit the usefulness of FT-IR-based predictions of individual FA in milk recording as indirect covariance structures in the calibration set must be valid for future samples. Otherwise, future samples will show incorrect predictions.

Investigation of the early intestinal microflora in premature infants with/without necrotizing enterocolitis using two different methods

Introduction:The pathophysiology of necrotizing enterocolitis (NEC) is multifactorial, and gastrointestinal bacteria are thought to play an important role. In this study, the role of microflora in the gastrointestinal tract of neonates with NEC was assessed by comparing cases with controls.Results:Of the 163 neonates, 21 developed NEC. The risk of NEC decreased by 8% with each additional day of gestational age.Discussion:Typically, very few bacterial species could be cultured from the fecal specimens obtained. Gram-positive (G+) bacteria dominated the samples in the NEC group, whereas in the control group mixed flora of G+ and Gram-negative (G−) bacteria were isolated. Surprisingly, molecular analysis using PCR-DGGE profiles did not confirm these differences. Our data suggest that G+ bacteria in the intestine may play a role in the development of NEC in premature infants.Methods:One hundred and sixty three neonates born at <30 weeks of gestation were enrolled. Fecal samples taken during the first month of life were subjected to culture and PCR-denaturing gradient gel electrophoresis (PCR-DGGE). A total of 482 fecal samples were examined.

Chemometrics: an important tool for monitoring interactions of vitamin B7 with bovine serum albumin with the aim of developing an efficient biosensing system for the analysis of protein.

For the first time, interaction of vitamin B7 (VB7) with bovine serum albumin (BSA) was investigated with the aim of developing a method for the analysis of BSA. The interaction of VB7 with BSA was investigated by cyclic voltammetry (CV), linear sweep voltammetry (LSV), and differential pulse voltammetry (DPV) at a multi-walled carbon nanotubes-modified glassy carbon electrode (MWCNTs/GCE). The recorded electrochemical data was combined with UVvis and fluorescence (F) spectroscopic data into a row- and column-wise augmented matrix and resolved by multivariate curve resolution-alternating least squares (MCR-ALS) as an efficient chemometric tool, and this assisted in the further elucidation of the above interaction. Also, with aid of MCR-BANDS method, the absence of rotational ambiguity was verified in the obtained results and we confirmed that the obtained results were unambiguous and reliable. The binding of VB7 to BSA was also modeled by molecular docking methods. Excellent agreement was found between the experimental and computational results. The differences of DPV responses of VB7 in the absence and presence of BSA (ΔI) were found to be linearly related to BSA concentration between 0.5×10(-9) mol L(-1) and 35.0×10(-9) mol L(-1), and a limit of detection (LOD, 3Sb/b) of 0.22×10(-9) mol L(-1) was calculated. Finally, the DPV method was further applied to the determination of serum albumin (SA) in serum samples obtained from Holstein cows and the results were in good agreement with those obtained by a medical diagnostic laboratory whose method was based on traditional cellulose acetate electrophoresis. The MWCNTs/GCE showed enhanced electron transfer kinetics, large electroactive surface area, and was highly sensitive, selective, and stable towards SA determination. The satisfactory analytical performance of the proposed method would make it potentially advantageous for a broad range of biosensing and clinical applications.

Combination of electrochemistry with chemometrics to introduce an efficient analytical method for simultaneous quantification of five opium alkaloids in complex matrices

For the first time, an analytical methodology based on differential pulse voltammetry (DPV) at a glassy carbon electrode (GCE) and integration of three efficient strategies including variable selection based on ant colony optimization (ACO), mathematical pre-processing selection by genetic algorithm (GA), and sample selection (SS) through a distance-based procedure to improve partial least squares-1 (PLS-1, ACO-GA-SS-PLS-1) multivariate calibration (MVC) for the simultaneous determination of five opium alkaloids including morphine (MOP), noscapine (NOP), thebaine (TEB), codeine (COD), and papaverine (PAP) was used and validated. The baselines of the DPV signals were modeled as a smooth curve, using P-splines, a combination of B-splines and a discrete roughness penalty. After subtraction of the baseline we got a signal with a two-component probability density. One component was for the peaks and it was approximated by a uniform distribution on the potential axis. The other component was for the observed noise around the baseline. Some sources of bi-linearity deviation for electrochemical data were discussed and analyzed. The lack of bi-linearity was tackled by potential shift correction using correlation optimized warping (COW) algorithm. The MVC model was developed as a quinternary calibration model in a blank human serum sample (drug-free) provided by a healthy volunteer to regard the presence of a strong matrix effect which may be caused by the possible interferents present in the serum, and it was validated and tested with two independent sets of analytes mixtures in the blank and actual human serum samples, respectively. Fortunately, the proposed methodology was successful in simultaneous determination of MOP, NOP, TEB, COD, and PAP in both blank and actual human serum samples and its results were satisfactory comparable to those obtained by applying the reference method based on high performance liquid chromatography-ultraviolet detection (HPLC-UV).