Technological readiness and implementation of genomic-driven precision medicine for complex diseases.

The fields of human genetics and genomics have generated considerable knowledge about the mechanistic basis of many diseases. Genomic approaches to diagnosis, prognostication, prevention and treatment - genomic-driven precision medicine (GDPM) - may help optimize medical practice. Here, we provide a comprehensive review of GDPM of complex diseases across major medical specialties. We focus on technological readiness: how rapidly a test can be implemented into health care. Although these areas of medicine are diverse, key similarities exist across almost all areas. Many medical areas have, within their standards of care, at least one GDPM test for a genetic variant of strong effect that aids the identification/diagnosis of a more homogeneous subset within a larger disease group or identifies a subset with different therapeutic requirements. However, for almost all complex diseases, the majority of patients do not carry established single-gene mutations with large effects. Thus, research is underway that seeks to determine the polygenic basis of many complex diseases. Nevertheless, most complex diseases are caused by the interplay of genetic, behavioural and environmental risk factors, which will likely necessitate models for prediction and diagnosis that incorporate genetic and non-genetic data.

Serum proteomic profiling at diagnosis predicts clinical course, and need for intensification of treatment in inflammatory bowel disease.

BACKGROUND: Success in personalized medicine in complex disease is critically dependent on biomarker discovery. We profiled serum proteins using a novel proximity extension assay [PEA] to identify diagnostic and prognostic biomarkers in inflammatory bowel disease [IBD]. METHODS: We conducted a prospective case-control study in an inception cohort of 552 patients [328 IBD, 224 non-IBD], profiling proteins recruited across six centres. Treatment escalation was characterized by the need for biological agents or surgery after initial disease remission. Nested leave-one-out cross-validation was used to examine the performance of diagnostic and prognostic proteins. RESULTS: A total of 66 serum proteins differentiated IBD from symptomatic non-IBD controls, including matrix metallopeptidase-12 [MMP-12; Holm-adjusted p = 4.1 × 10-23] and oncostatin-M [OSM; p = 3.7 × 10-16]. Nine of these proteins are associated with cis-germline variation [59 independent single nucleotide polymorphisms]. Fifteen proteins, all members of tumour necrosis factor-independent pathways including interleukin-1 (IL-1) and OSM, predicted escalation, over a median follow-up of 518 [interquartile range 224-756] days. Nested cross-validation of the entire data set allowed characterization of five-protein models [96% comprising five core proteins ITGAV, EpCAM, IL18, SLAMF7 and IL8], which define a high-risk subgroup in IBD [hazard ratio 3.90, confidence interval: 2.43-6.26], or allowed distinct two- and three-protein models for ulcerative colitis and Crohn's disease respectively. CONCLUSION: We have characterized a simple oligo-protein panel that has the potential to identify IBD from symptomatic controls and to predict future disease course. Further prospective work is required to validate our findings.

Correlations between microbiota and metabolites after faecal microbiota transfer in irritable bowel syndrome.

Faecal microbiota transfer (FMT) consists of the infusion of donor faecal material into the intestine of a patient with the aim to restore a disturbed gut microbiota. In this study, it was investigated whether FMT has an effect on faecal microbial composition, its functional capacity, faecal metabolite profiles and their interactions in 16 irritable bowel syndrome (IBS) patients. Faecal samples from eight different time points before and until six months after allogenic FMT (faecal material from a healthy donor) as well as autologous FMT (own faecal material) were analysed by 16S RNA gene amplicon sequencing and gas chromatography coupled to mass spectrometry (GS-MS). The results showed that the allogenic FMT resulted in alterations in the microbial composition that were detectable up to six months, whereas after autologous FMT this was not the case. Similar results were found for the functional profiles, which were predicted from the phylogenetic sequencing data. While both allogenic FMT as well as autologous FMT did not have an effect on the faecal metabolites measured in this study, correlations between the microbial composition and the metabolites showed that the microbe-metabolite interactions seemed to be disrupted after allogenic FMT compared to autologous FMT. This shows that FMT can lead to altered interactions between the gut microbiota and its metabolites in IBS patients. Further research should investigate if and how this affects efficacy of FMT treatments.

The relationship between faecal-associated and mucosal-associated microbiota in irritable bowel syndrome patients and healthy subjects.

BACKGROUND: The faecal-associated microbiota is commonly seen as a surrogate of the mucosal-associated microbiota. However, previous studies indicate that they are different. Furthermore, analyses of the mucosal microbiota are commonly done after standard bowel cleansing, affecting the microbial composition. AIM: To compare the mucosal-associated microbiota, obtained from unprepared colon, with faecal-associated microbiota in healthy subjects and irritable bowel syndrome (IBS) patients. METHODS: Faecal and mucosal biopsies were obtained from 33 IBS patients and 16 healthy controls. Of IBS patients, 49% belonged to the diarrhoea-predominant subgroup and 80% suffered from IBS symptoms during at least 5 years. Biopsies were collected from unprepared sigmoid colon and faecal samples a day before colonoscopy. Microbiota analyses were performed with a phylogenetic microarray and redundancy discriminant analysis. RESULTS: The composition of the mucosal- and the faecal-associated microbiota in unprepared sigmoid colon differs significantly (P = 0.002). Clinical characteristics of IBS did not correlate with this difference. Bacteroidetes dominate the mucosal-associated microbiota. Firmicutes, Actinobacteria and Proteobacteria dominate the faecal-associated microbiota. Healthy subjects had a significantly higher (P < 0.005) abundance (1.9%) of the bacterial group uncultured Clostridiales I in the mucosal-associated microbiota than IBS patients (0.3%). Bacterial diversity was higher in faecal- compared with mucosal-associated microbiota in IBS patients (P < 0.005). No differences were found in healthy subjects. CONCLUSIONS: Differences in the mucosal-associated microbiota between healthy individuals and IBS patients are minimal (one bacterial group) compared to differences in the faecal microbiota of both groups (53 bacterial groups). Microbial aberrations characterising IBS are more pronounced in the faeces than in the mucosa.

Milk metabolites and their genetic variability.

The composition of milk is crucial to evaluate milk performance and quality measures. Milk components partly contribute to breeding scores, and they can be assessed to judge metabolic and energy status of the cow as well as to serve as predictive markers for diseases. In addition to the milk composition measures (e.g., fat, protein, lactose) traditionally recorded during milk performance test via infrared spectroscopy, novel techniques, such as gas chromatography-mass spectrometry, allow for a further analysis of milk into its metabolic components. Gas chromatography-mass spectrometry is suitable for measuring several hundred metabolites with high throughput, and thus it is applicable to study sources of genetic and nongenetic variation of milk metabolites in dairy cows. Heritability and mode of inheritance of metabolite measurements were studied in a linear mixed model approach including expected (pedigree) and realized (genomic) relationship between animals. The genetic variability of 190 milk metabolite intensities was analyzed from 1,295 cows held on 18 farms in Mecklenburg-Western Pomerania, Germany. Besides extensive pedigree information, genotypic data comprising 37,180 single nucleotide polymorphism markers were available. Goodness of fit and significance of genetic variance components based on likelihood ratio tests were investigated with a full model, including marker- and pedigree-based genetic effects. Broad-sense heritability varied from zero to 0.699, with a median of 0.125. Significant additive genetic variance was observed for highly heritable metabolites, but dominance variance was not significantly present. As some metabolites are particularly favorable for human nutrition, for instance, future research should address the identification of locus-specific genetic effects and investigate metabolites as the molecular basis of traditional milk performance test traits.

Investigating associations between milk metabolite profiles and milk traits of Holstein cows.

In the field of dairy cattle research, it is of great interest to improve the detection and prevention of diseases (e.g., mastitis and ketosis) and monitor specific traits related to the state of health and management. During the standard milk performance test, traditional milk traits are monitored, and quality and quantity are screened. In addition to the standard test, it is also now possible to analyze milk metabolites in a high-throughput manner and to consider them in connection with milk traits to identify functionally important metabolites that can also serve as biomarker candidates. We present a study in which 190 milk metabolites and 14 milk traits of 1,305 Holstein cows on 18 commercial farms were investigated to characterize interrelations of milk metabolites between each other, to milk traits from the milk standard performance test, and to influencing factors such as farm and sire effect (half-sib structure). The effect of influencing factors (e.g., farm) varied among metabolites and traditional milk traits. The investigations of associations between metabolites and milk traits revealed groups of metabolites that show, for example, positive correlations to protein and casein, and negative correlations to lactose and pH. On the other hand, groups of metabolites jointly associated with the investigated milk traits can be identified and functionally discussed. To enable a multivariate investigation, 2 machine learning methods were applied to detect important metabolites that are highly correlated with the investigated traditional milk traits. For somatic cell score, uracil, lactic acid, and 9 other important metabolites were detected. Lactic acid has already been proposed as a biomarker candidate for mastitis in the recent literature. In conclusion, we found sets of metabolites eligible to predict milk traits, enabling the analysis of milk traits from a metabolic perspective and discussion of the possible functional background for some of the detected associations.

Short communication: evaluation of bovine milk residues from routine milk testing programs as DNA source for genotyping.

Genome-wide association studies and genomic evaluation using a dense set of genetic markers both require a large number of genotyped individuals. Collection of the respective samples contributes substantially to the cost of the approach. In dairy cattle research, the use of residues from routine milk recording would be a cost-saving alternative to obtain samples for an appropriate number of individuals with specific phenotypes in a very short time. To assess the suitability of milk recording residues, we concurrently investigated milk residues obtained after standardized milk recording procedures and blood samples from 115 cows originating from 3 farms with different milking systems by genotyping 15 microsatellite markers. We found that 4% of the milk samples were possibly assigned to the wrong animal (i.e., conflicts) and that at least 27% of the milk residues were contaminated, as indicated by an extra allele not present in the blood sample. These additional alleles primarily originated from a sample with a higher somatic cell score that went through the milk sample analyzer in the milk laboratory before the target sample. Furthermore, additional allele carryover was observed across more than one sample, when the difference in somatic cell count between samples exceeded 100,000 cells/mL. Finally, in several samples, the extra allele could not be traced back to previous samples passing through the milk sample analyzer. One source of those contaminations might be sample collection on-farm due to milk traces from the previously milked cow in the hose. No correlation was found between the farm management and conflicts or contaminations. We conclude that residues from routine milk recording are not suitable for genomic evaluation or genome-wide association studies because of the high prevalence of contamination generated at several steps during the collection and processing of milk residual samples.

Suppressor of cytokine signaling-3 is affected in T-cells from tuberculosisTB patients.

T-cells and T-cell-derived cytokines are crucial mediators of protection against Mycobacterium tuberculosis infection, but these factors are insufficient as biomarkers for disease susceptibility. In order to define T-cell molecules involved in tuberculosis (TB), we compared gene expression profiles of T-cells from patients with active TB, healthy donors with latent M. tuberculosis infection (LTBIs) and non-infected healthy donors (NIDs) by microarray analysis. Pathway-focused analyses identified a prevalent subset of candidate genes involved in the Janus kinase (JAK)-signal transducer and activator of transcription signalling pathway, including those encoding suppressor of cytokine signalling (SOCS) molecules, in the subset of protection-associated genes. Differential expression was verified by quantitative PCR analysis for the cytokine-inducible SH2-containing protein (CISH), SOCS3, JAK3, interleukin-2 receptor α-chain (IL2RA), and the proto-oncogene serine/threonine protein kinase (PIM1). Classification analyses revealed that this set of molecules was able to discriminate efficiently between T-cells from TB patients and those from LTBIs, and, notably, to achieve optimal discrimination between LTBIs and NIDs. Further characterization by quantitative PCR revealed highly variable candidate gene expression in CD4(+) and CD8(+) T-cells from TB patients and only minor differences between CD4(+) and CD8(+) T-cell subpopulations. These results point to a role of cytokine receptor signalling regulation in T-cells in susceptibility to TB.

Human gene expression profiles of susceptibility and resistance in tuberculosis.

Tuberculosis (TB) still poses a profound burden on global health, owing to significant morbidity and mortality worldwide. Although a fully functional immune system is essential for the control of Mycobacterium tuberculosis infection, the underlying mechanisms and reasons for failure in part of the infected population remain enigmatic. Here, whole-blood microarray gene expression analyses were performed in TB patients and in latently as well as uninfected healthy controls to define biomarkers predictive of susceptibility and resistance. Fc gamma receptor 1B (FCGRIB)was identified as the most differentially expressed gene, and, in combination with four other markers, produced a high degree of accuracy in discriminating TB patients and latently infected donors. We determined differentially expressed genes unique for active disease and identified profiles that correlated with susceptibility and resistance to TB. Elevated expression of innate immune-related genes in active TB and higher expression of particular gene clusters involved in apoptosis and natural killer cell activity in latently infected donors are likely to be the major distinctive factors determining failure or success in controlling M. tuberculosis infection. The gene expression profiles defined in this study provide valuable clues for better understanding of progression from latent infection to active disease and pave the way for defining predictive correlates of protection in TB.

Deconfounding microarray analysis - independent measurements of cell type proportions used in a regression model to resolve tissue heterogeneity bias.

OBJECTIVES: Microarray analysis requires standardized specimens and evaluation procedures to achieve acceptable results. A major limitation of this method is caused by heterogeneity in the cellular composition of tissue specimens, which frequently confounds data analysis. We introduce a linear model to deconfound gene expression data from tissue heterogeneity for genes exclusively expressed by a single cell type. METHODS: Gene expression data are deconfounded from tissue heterogeneity effects by analyzing them using an appropriate linear regression model. In our illustrating data set tissue heterogeneity is being measured using flow cytometry. Gene expression data are determined in parallel by real time quantitative polymerase chain reaction (qPCR) and microarray analyses. Verification of deconfounding is enabled using protein quantification for the respective marker genes. RESULTS: For our illustrating dataset, quantification of cell type proportions for peripheral blood mononuclear cells (PBMC) from tuberculosis patients and controls revealed differences in B cell and monocyte proportions between both study groups, and thus heterogeneity for the tissue under investigation. Gene expression analyses reflected these differences in celltype distribution. Fitting an appropriate linear model allowed us to deconfound measured transcriptome levels from tissue heterogeneity effects. In the case of monocytes, additional differential expression on the single cell level could be proposed. Protein quantification verified these deconfounded results. CONCLUSIONS: Deconfounding of transcriptome analyses for cellular heterogeneity greatly improves interpretability, and hence the validity of transcriptome profiling results.

Linkage analyses of chromosomal region 18p11-q12 in dyslexia.

Dyslexia is characterized as a significant impairment in reading and spelling ability that cannot be explained by low intelligence, low school attendance or deficits in sensory acuity. It is known to be a hereditary disorder that affects about 5% of school aged children, making it the most common of childhood learning disorders. Several susceptibility loci have been reported on chromosomes 1, 2, 3, 6, 15, and 18. The locus on chromosome 18 has been described as having the strongest influence on single word reading, phoneme awareness, and orthographic coding in the largest genome wide linkage study published to date (Fisher et al., 2002). Here we present data from 82 German families in order to investigate linkage of various dyslexia-related traits to the previously described region on chromosome 18p11-q12. Using two- and multipoint analyses, we did not find support for linkage of spelling, single word reading, phoneme awareness, orthographic coding and rapid naming to any of the 14 genotyped STR markers. Possible explanations for our non-replication include differences in study design, limited power of our study and overestimation of the effect of the chromosome 18 locus in the original study.

Tutorial on microarray gene expression experiments. An introduction.

OBJECTIVES: With the collection of articles presented in this special issue, we aim at educating interested statisticians and biometricians on the one hand as well as biologists and medical researchers on the other with respect to basic necessities in planning, conducting and analyzing microarray gene expression experiments. The reader should get comprehensive directions to understand both the overall structure of this approach as well as the decisive details, which enable--or thwart--a meaningful data analysis. METHODS: For a one-day workshop with tutorial character we brought together experts in design, conduct and analysis of microarray gene expression experiments who prepared a series of comprehensive lessons. These contributions were then reworked into a series of introductory articles and bundled in form and content as a Special Topic. RESULTS: It was possible to present a tutorial overview of the field. The interested reader was able to learn the basic necessities and was referred to further references for details on the possible alternatives. A recipe style all-embracing plan, covering all eventualities and possibilities was not only beyond the scope of an introductory tutorial-like presentation, but was also not yet agreed upon by the scientific society. CONCLUSIONS: It proved feasible to find a framework for integrating the interdisciplinary approaches to the challenging field of gene expression analysis with microarrays, hopefully contributing to a rapid and comprehensive introduction for novices.

Two-color microarray experiments. Technology and sources of variance.

OBJECTIVES: Microarray gene expression experiments have a complex technical background. Knowledge about certain technical details is inevitable to judge alternatives for both experimental design and analysis. Here, we introduce the necessary details for the so-called two-color microarray experiments and review major sources of technical variance. METHODS: We follow the sequence of experimental steps during a typical two-color microarray gene expression experiment, stressing decisive points in the choice of technique, experimental handling and biophysical basics. We point out where technical variation is to be expected. RESULTS: Tissue storage, RNA extraction techniques, as well as the microarray hybridization represent major components of technical variance to be considered. Depending on the possibilities for access to the biomedical material under investigation, choice of amplification and labeling techniques can also be decisive to avoid additional technical variance. The two-color microarray experimental approach seeks to avoid a group of probe-level technical biases making use of the advantages of an incomplete block-design. CONCLUSIONS: It is worth to know the major sources of technical variance during the typical experimental sequence, both for choice of experimental design and techniques of molecular biology, as well as for the understanding of quality control and normalization approaches. Here, early investments pay at the level of reduced technical variance, allowing for enhanced detection levels for the effects under investigation.

Sample selection for microarray gene expression studies.

OBJECTIVES: The choice of biomedical samples for microarray gene expression studies is decisive for both validity and interpretability of results. We present a consistent, comprehensive framework to deal with the typical selection problems in microarray studies. METHODS: Microarray studies are designed either as case-control studies or as comparisons of parallel groups from cohort studies, since high levels of random variation in the experimental approach thwart absolute measurements of gene expression levels. Validity and results of gene expression studies heavily rely on the appropriate choice of these study groups. Therefore, the so-called principles of comparability, which are well known from both clinical and epidemiological studies, need to be applied to microarray experiments. RESULTS: The principles of comparability are the study-base principle, the principle of deconfounding and the principle of comparable accuracy in measurements. We explain each of these principles, show how they apply to microarray experiments, and illustrate them with examples. The examples are chosen as to represent typical stumbling blocks of microarray experimental design, and to exemplify the benefits of implementing the principles of comparability in the setting of microarray experiments. CONCLUSIONS: Microarray studies are closely related to classical study designs and therefore have to obey the same principles of comparability as these. Their validity should not be compromised by selection, confounding or information bias. The so-called study-base principle, calling for comparability and thorough definition of the compared cell populations, is the key principle for the choice of biomedical samples and controls in microarray studies.

Formation of metastable RNA structures by sequential folding during transcription: time-resolved structural analysis of potato spindle tuber viroid (-)-stranded RNA by temperature-gradient gel electrophoresis.

A model of functional elements critical for replication and infectivity of the potato spindle tuber viroid (PSTVd) was proposed earlier: a thermodynamically metastable structure containing a specific hairpin (HP II) in the (-)-strand replication intermediate is essential for template activity during (+)-strand synthesis. We present here a detailed kinetic analysis on how PSTVd (-)-strands fold during synthesis by sequential folding into a variety of metastable structures that rearrange only slowly into the structure distribution of the thermodynamic equilibrium. Synthesis of PSTVd (-)-strands was performed by T7-RNA-polymerase; the rate of synthesis was varied by altering the concentration of nucleoside triphosphates to mimic the in vivo synthesis rate of DNA-dependent RNA polymerase II. With dependence on rate and duration of the synthesis, the structure distributions were analyzed by temperature-gradient gel electrophoresis (TGGE). Metastable structures are generated preferentially at low transcription rates--similar to in vivo rates--or at short transcription times at higher rates. Higher transcription rates or longer transcription times lead to metastable structures in low or undetectable amounts. Instead different structures do gradually appear having a more rod-like shape and higher thermodynamic stability, and the thermodynamically optimal rod-like structure dominates finally. It is concluded that viroids are able to use metastable as well as stable structures for their biological functions.