| 1. |
- Abdellah, Tebani, et al.
(författare)
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Integration of molecular profiles in a longitudinal wellness profiling cohort.
- 2020
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Ingår i: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 11:1
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Tidskriftsartikel (refereegranskat)abstract
- An important aspect of precision medicine is to probe the stability in molecular profiles among healthy individuals over time. Here, we sample a longitudinal wellness cohort with 100 healthy individuals and analyze blood molecular profiles including proteomics, transcriptomics, lipidomics, metabolomics, autoantibodies andimmune cell profiling, complementedwith gut microbiota composition and routine clinical chemistry. Overall, our results show high variation between individuals across different molecular readouts, while the intra-individual baseline variation is low. The analyses show that each individual has a unique and stable plasma protein profile throughout the study period and that many individuals also show distinct profiles with regards to the other omics datasets, with strong underlying connections between the blood proteome and the clinical chemistry parameters. In conclusion, the results support an individual-based definition of health and show that comprehensive omics profiling in a longitudinal manner is a path forward for precision medicine.
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| 2. |
- Alm, Erik, 1980-, et al.
(författare)
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A solution to the 1D NMR alignment problem using an extended generalized fuzzy Hough transform and mode support
- 2009
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Ingår i: Analytical and Bioanalytical Chemistry. - : Springer Science and Business Media LLC. - 1618-2642 .- 1618-2650. ; 395:1, s. 213-223
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Tidskriftsartikel (refereegranskat)abstract
- This paper approaches the problem of intersample peak correspondence in the context of later applying statistical data analysis techniques to 1D 1H-nuclear magnetic resonance (NMR) data. Any data analysis methodology will fail to produce meaningful results if the analyzed data table is not synchronized, i.e., each analyzed variable frequency (Hz) does not originate from the same chemical source throughout the entire dataset. This is typically the case when dealing with NMR data from biological samples. In this paper, we present a new state of the art for solving this problem using the generalized fuzzy Hough transform (GFHT). This paper describes significant improvements since the method was introduced for NMR datasets of plasma in Csenki et al. (Anal Bioanal Chem 389:875-885, 15) and is now capable of synchronizing peaks from more complex datasets such as urine as well as plasma data. We present a novel way of globally modeling peak shifts using principal component analysis, a new algorithm for calculating the transform and an effective peak detection algorithm. The algorithm is applied to two real metabonomic 1H-NMR datasets and the properties of the method are compared to bucketing. We implicitly prove that GFHT establishes the objectively true correspondence. Desirable features of the GFHT are: (1) intersample peak correspondence even if peaks change order on the frequency axis and (2) the method is symmetric with respect to the samples.
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| 3. |
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| 4. |
- Alm, Erik, 1980-, et al.
(författare)
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Time-resolved biomarker discovery inH-NMR data using generalized fuzzy Hough transform alignment and parallel factor analysis
- 2010
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Ingår i: Analytical and Bioanalytical Chemistry. - : Springer Science and Business Media LLC. - 1618-2642 .- 1618-2650. ; 396:5, s. 1681-1689
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Tidskriftsartikel (refereegranskat)abstract
- This work addresses the subject of time-series analysis of comprehensive 1H-NMR data of biological origin. One of the problems with toxicological and efficacy studies is the confounding of correlation between the administered drug, its metabolites and the systemic changes in molecular dynamics, i.e., the flux of drug-related molecules correlates with the molecules of system regulation. This correlation poses a problem for biomarker mining since this confounding must be untangled in order to separate true biomarker molecules from dose-related molecules. One way of achieving this goal is to perform pharmacokinetic analysis. The difference in pharmacokinetic time profiles of different molecules can aid in the elucidation of the origin of the dynamics, this can even be achieved regardless of whether the identity of the molecule is known or not. This mode of analysis is the basis for metabonomic studies of toxicology and efficacy. One major problem concerning the analysis of 1H-NMR data generated from metabonomic studies is that of the peak positional variation and of peak overlap. These phenomena induce variance in the data, obscuring the true information content and are hence unwanted but hard to avoid. Here, we show that by using the generalized fuzzy Hough transform spectral alignment, variable selection, and parallel factor analysis, we can solve both the alignment and the confounding problem stated above. Using the outlined method, several different temporal concentration profiles can be resolved and the majority of the studied molecules and their respective fluxes can be attributed to these resolved kinetic profiles. The resolved time profiles hereby simplifies finding true biomarkers and bio-patterns for early detection of biological conditions as well as providing more detailed information about the studied biological system. The presented method represents a significant step forward in time-series analysis of biological 1H-NMR data as it provides almost full automation of the whole data analysis process and is able to analyze over 800 unique features per sample. The method is demonstrated using a 1H-NMR rat urine dataset from a toxicology study and is compared with a classical approach: COW alignment followed by bucketing.
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| 5. |
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| 6. |
- Bostanci, Nagihan, et al.
(författare)
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Dysbiosis of the Human Oral Microbiome During the Menstrual Cycle and Vulnerability to the External Exposures of Smoking and Dietary Sugar.
- 2021
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Ingår i: Frontiers in Cellular and Infection Microbiology. - : Frontiers Media SA. - 2235-2988. ; 11
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Tidskriftsartikel (refereegranskat)abstract
- Physiological hormonal fluctuations exert endogenous pressures on the structure and function of the human microbiome. As such, the menstrual cycle may selectively disrupt the homeostasis of the resident oral microbiome, thus compromising oral health. Hence, the aim of the present study was to structurally and functionally profile the salivary microbiome of 103 women in reproductive age with regular menstrual cycle, while evaluating the modifying influences of hormonal contraceptives, sex hormones, diet, and smoking. Whole saliva was sampled during the menstrual, follicular, and luteal phases (n = 309) of the cycle, and the participants reported questionnaire-based data concerning their life habits and oral or systemic health. No significant differences in alpha-diversity or phase-specific clustering of the overall microbiome were observed. Nevertheless, the salivary abundances of genera Campylobacter, Haemophilus, Prevotella, and Oribacterium varied throughout the cycle, and a higher species-richness was observed during the luteal phase. While the overall community structure maintained relatively intact, its functional properties were drastically affected. In particular, 11 functional modules were differentially abundant throughout the menstrual cycle, including pentose phosphate metabolism, and biosynthesis of cobalamin and neurotransmitter gamma-aminobutyric acid. The menstrual cycle phase, but not oral contraceptive usage, was accountable for greater variations in the metabolic pathways of the salivary microbiome. Further co-risk factor analysis demonstrated that Prevotella and Veillonella were increased in current smokers, whereas high dietary sugar consumption modified the richness and diversity of the microbiome during the cycle. This is the first large study to systematically address dysbiotic variations of the oral microbiome during the course of menstrual cycle, and document the additive effect of smoking and sugar consumption as environmental risk factors. It reveals the structural resilience and functional adaptability of the oral microbiome to the endogenous hormonal pressures of the menstrual cycle, while revealing its vulnerability to the exogenous exposures of diet and smoking.
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| 7. |
- Cheng, Liqin, et al.
(författare)
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A MicroRNA Gene Panel Predicts the Vaginal Microbiota Composition
- 2021
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Ingår i: mSystems. - : American Society for Microbiology. - 2379-5077. ; 6:3
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Tidskriftsartikel (refereegranskat)abstract
- The vaginal microbiota plays an essential role in vaginal health. The vaginas of many reproductive-age women are dominated by one of the Lactobacillus species. However, the vaginas of a large number of women are characterized by the colonization of several other anaerobes. Notably, some women with the non-Lactobacillus-dominated vaginal microbiota develop bacterial vaginosis, which has been correlated with sexually transmitted infections and other adverse outcomes. However, interactions and mechanisms linking the vaginal microbiota to host response are still under investigation. There are studies suggesting a link between human microRNAs and gut microbiota, but limited analysis has been carried out on the interplay of microRNAs and vaginal microbiota. In this study, we performed a microRNA expression array profiling on 67 vaginal samples from young Swedish women. MicroRNAs were clustered into distinct groups according to vaginal microbiota composition. Interestingly, 182 microRNAs were significantly elevated in their expression in the non-Lactobacillus-dominated community, suggesting an antagonistic relationship between Lactobacillus and microRNAs. Of the elevated microRNAs, 10 microRNAs displayed excellent diagnostic potential, visualized by receiver operating characteristics analysis. We further validated our findings in 34 independent samples where expression of top microRNA candidates strongly separated the Lactobacillus-dominated community from the non-Lactobacillus-dominated community in the vaginal microbiota. Notably, the Lactobacillus crispatus-dominated community showed the most profound differential microRNA expression compared with the non-Lactobacillus-dominated community. In conclusion, we demonstrate a strong relationship between the vaginal microbiota and numerous genital microRNAs, which may facilitate a deeper mechanistic interplay in this biological niche. IMPORTANCE Vaginal microbiota is correlated with women's health, where a non-Lactobacillus-dominated community predisposes women to a higher risk of disease, including human papillomavirus (HPV). However, the molecular relationship between the vaginal microbiota and host is largely unexplored. In this study, we investigated a link between the vaginal microbiota and host microRNAs in a group of young women. We uncovered an inverse correlation of the expression of microRNAs with the abundance of Lactobacillus species in the vaginal microbiota. Particularly, the expression of microRNA miR-23a-3p and miR-130a-3p, displaying significantly elevated levels in non-Lactobacillus-dominated communities, predicted the bacterial composition of the vaginal microbiota in an independent validation group. Since targeting of microRNAs is explored in the clinical setting, our results warrant investigation of whether microRNA modulation could be used for treating vaginosis recurrence and vaginosis-related diseases. Conversely, commensal bacteria could be used for treating diseases with microRNA aberrations.
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| 8. |
- Cheng, Liqin, et al.
(författare)
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Vaginal microbiota and human papillomavirus infection among young Swedish women
- 2020
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Ingår i: npj Biofilms and Microbiomes. - : Springer Science and Business Media LLC. - 2055-5008. ; 6:1
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Tidskriftsartikel (refereegranskat)abstract
- Human papillomavirus (HPV) infection is one of the most common sexually transmitted diseases. To define the HPV-associated microbial community among a high vaccination coverage population, we carried out a cross-sectional study with 345 young Swedish women. The microbial composition and its association with HPV infection, including 27 HPV types, were analyzed. Microbial alpha-diversity was found significantly higher in the HPV-infected group (especially with oncogenic HPV types and multiple HPV types), compared with the HPV negative group. The vaginal microbiota among HPV-infected women was characterized by a larger number of bacterial vaginosis-associated bacteria (BVAB), Sneathia, Prevotella, and Megasphaera. In addition, the correlation analysis demonstrated that twice as many women with non-Lactobacillus-dominant vaginal microbiota were infected with oncogenic HPV types, compared with L. crispatus-dominated vaginal microbiota. The data suggest that HPV infection, especially oncogenic HPV types, is strongly associated with a non-Lactobacillus-dominant vaginal microbiota, regardless of age and vaccination status.
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| 9. |
- Chorell, Elin, et al.
(författare)
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Statistical multivariate metabolite profiling for aiding biomarker pattern detection and mechanistic interpretations in GC/MS based metabolomics
- 2006
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Ingår i: Metabolomics. - : Springer Science and Business Media LLC. - 1573-3882 .- 1573-3890. ; 2:4, s. 257-68
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Tidskriftsartikel (refereegranskat)abstract
- A strategy for robust and reliable mechanistic statistical modelling of metabolic responses in relation to drug induced toxicity is presented. The suggested approach addresses two cases commonly occurring within metabonomic toxicology studies, namely; 1) A pre-defined hypothesis about the biological mechanism exists and 2) No such hypothesis exists. GC/MS data from a liver toxicity study consisting of rat urine from control rats and rats exposed to a proprietary AstraZeneca compound were resolved by means of hierarchical multivariate curve resolution (H-MCR) generating 287 resolved chromatographic profiles with corresponding mass spectra. Filtering according to significance in relation to drug exposure rendered in 210 compound profiles, which were subjected to further statistical analysis following correction to account for the control variation over time. These dose related metabolite traces were then used as new observations in the subsequent analyses. For case 1, a multivariate approach, named Target Batch Analysis, based on OPLS regression was applied to correlate all metabolite traces to one or more key metabolites involved in the pre-defined hypothesis. For case 2, principal component analysis (PCA) was combined with hierarchical cluster analysis (HCA) to create a robust and interpretable framework for unbiased mechanistic screening. Both the Target Batch Analysis and the unbiased approach were cross-verified using the other method to ensure that the results did match in terms of detected metabolite traces. This was also the case, implying that this is a working concept for clustering of metabolites in relation to their toxicity induced dynamic profiles regardless if there is a pre-existing hypothesis or not. For each of the methods the detected metabolites were subjected to identification by means of data base comparison as well as verification in the raw data. The proposed strategy should be seen as a general approach for facilitating mechanistic modelling and interpretations in metabolomic studies.
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| 10. |
- Csenki, Leonard, et al.
(författare)
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Proof of principle of a generalized fuzzy Hough transform approach to peak alignment of one-dimensional 1H NMR data
- 2007
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Ingår i: Analytical and Bioanalytical Chemistry. - : Springer Science and Business Media LLC. - 1618-2642 .- 1618-2650. ; 389:3, s. 875-885
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Tidskriftsartikel (refereegranskat)abstract
- In metabolic profiling, multivariate data analysis techniques are used to interpret one-dimensional (1D) 1H NMR data. Multivariate data analysis techniques require that peaks are characterised by the same variables in every spectrum. This location constraint is essential for correct comparison of the intensities of several NMR spectra. However, variations in physicochemical factors can cause the locations of the peaks to shift. The location prerequisite may thus not be met, and so, to solve this problem, alignment methods have been developed. However, current state-of-the-art algorithms for data alignment cannot resolve the inherent problems encountered when analysing NMR data of biological origin, because they are unable to align peaks when the spatial order of the peaks changes—a commonly occurring phenomenon. In this paper a new algorithm is proposed, based on the Hough transform operating on an image representation of the NMR dataset that is capable of correctly aligning peaks when existing methods fail. The proposed algorithm was compared with current state-of-the-art algorithms operating on a selected plasma dataset to demonstrate its potential. A urine dataset was also processed using the algorithm as a further demonstration. The method is capable of successfully aligning the plasma data but further development is needed to address more challenging applications, for example urine data.
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