How does the early life environment influence the oral microbiome and determine oral health outcomes in childhood?

The first 1000 days of life, from conception to 2 years, are a critical window for the influence of environmental exposures on the assembly of the oral microbiome, which is the precursor to dental caries (decay), one of the most prevalent microbially induced disorders worldwide. While it is known that the human microbiome is susceptible to environmental exposures, there is limited understanding of the impact of prenatal and early childhood exposures on the oral microbiome trajectory and oral health. A barrier has been the lack of technology to directly measure the foetal "exposome", which includes nutritional and toxic exposures crossing the placenta. Another barrier has been the lack of statistical methods to account for the high dimensional data generated by-omic assays. Through identifying which early life exposures influence the oral microbiome and modify oral health, these findings can be translated into interventions to reduce dental decay prevalence.

An integrated metagenomics and metabolomics approach implicates the microbiota-gut-brain axis in the pathogenesis of Huntington's disease.

BACKGROUND: Huntington's disease (HD) is an autosomal dominant neurodegenerative disorder with onset and severity of symptoms influenced by various environmental factors. Recent discoveries have highlighted the importance of the gastrointestinal microbiome in mediating the gut-brain-axis bidirectional communication via circulating factors. Using shotgun sequencing, we investigated the gut microbiome composition in the R6/1 transgenic mouse model of HD from 4 to 12 weeks of age (early adolescent through to adult stages). Targeted metabolomics was also performed on the blood plasma of these mice (n = 9 per group) at 12 weeks of age to investigate potential effects of gut dysbiosis on the plasma metabolome profile. RESULTS: Modelled time profiles of each species, KEGG Orthologs and bacterial genes, revealed heightened volatility in the R6/1 mice, indicating potential early effects of the HD mutation in the gut. In addition to gut dysbiosis in R6/1 mice at 12 weeks of age, gut microbiome function was perturbed. In particular, the butanoate metabolism pathway was elevated, suggesting increased production of the protective SCFA, butyrate, in the gut. No significant alterations were found in the plasma butyrate and propionate levels in the R6/1 mice at 12 weeks of age. The statistical integration of the metagenomics and metabolomics unraveled several Bacteroides species that were negatively correlated with ATP and pipecolic acid in the plasma. CONCLUSIONS: The present study revealed the instability of the HD gut microbiome during the pre-motor symptomatic stage of the disease which may have dire consequences on the host's health. Perturbation of the HD gut microbiome function prior to significant cognitive and motor dysfunction suggest the potential role of the gut in modulating the pathogenesis of HD, potentially via specific altered plasma metabolites which mediate gut-brain signaling.

Integrative Analyses to Investigate the Link between Microbial Activity and Metabolite Degradation during Anaerobic Digestion.

Anaerobic digestion (AD) is a promising biological process that converts waste into sustainable energy. To fully exploit AD's capability, we need to deepen our knowledge of the microbiota involved in this complex bioprocess. High-throughput methodologies open new perspectives to investigate the AD process at the molecular level, supported by recent data integration methodologies to extract relevant information. In this study, we investigated the link between microbial activity and substrate degradation in a lab-scale anaerobic codigestion experiment, where digesters were fed with nine different mixtures of three cosubstrates (fish waste, sewage sludge, and grass). Samples were profiled using 16S rRNA sequencing and untargeted metabolomics. In this article, we propose a suite of multivariate tools to statistically integrate these data and identify coordinated patterns between groups of microbial and metabolic profiles specific of each cosubstrate. Five main groups of features were successfully evidenced, including cadaverine degradation found to be associated with the activity of microorganisms from the order Clostridiales and the genus Methanosarcina. This study highlights the potential of data integration toward a comprehensive understanding of AD microbiota.

Microbiome profiling reveals gut dysbiosis in a transgenic mouse model of Huntington's disease.

Huntington's disease (HD) is a progressive neurodegenerative disorder caused by a trinucleotide repeat expansion in the huntingtin (HTT) gene, which is expressed ubiquitously throughout the brain and peripheral tissues. Whilst the focus of much research has been on the cognitive, psychiatric and motor symptoms of HD, the extent of peripheral pathology and its potential impact on central symptoms has been less intensely explored. Disruption of the gastrointestinal microbiome (gut dysbiosis) has been recently reported in a number of neurological and psychiatric disorders, and therefore we hypothesized that it might also occur in HD. We have used 16S rRNA amplicon sequencing to characterize the gut microbiome in the R6/1 transgenic mouse model of HD, relative to littermate wild-type controls. We report that there is a significant difference in microbiota composition in HD mice at 12 weeks of age. Specifically, we observed an increase in Bacteriodetes and a proportional decrease in Firmicutes in the HD gut microbiome. In addition, we observed an increase in microbial diversity in male HD mice, compared to wild-type controls, but no differences in diversity were observed in female HD mice. The gut dysbiosis observed coincided with impairment in body weight gain despite higher food intake as well as motor deficits at 12 weeks of age. Gut dysbiosis was also associated with a change in the gut microenvironment, as we observed higher fecal water content in HD mice at 12 weeks of age. This study provides the first evidence of gut dysbiosis in HD.

Evaluation of Serum Glycoprotein Biomarker Candidates for Detection of Esophageal Adenocarcinoma and Surveillance of Barrett's Esophagus.

Esophageal adenocarcinoma (EAC) is thought to develop from asymptomatic Barrett's esophagus (BE) with a low annual rate of conversion. Current endoscopy surveillance of BE patients is probably not cost-effective. Previously, we discovered serum glycoprotein biomarker candidates which could discriminate BE patients from EAC. Here, we aimed to validate candidate serum glycoprotein biomarkers in independent cohorts, and to develop a biomarker candidate panel for BE surveillance. Serum glycoprotein biomarker candidates were measured in 301 serum samples collected from Australia (4 states) and the United States (1 clinic) using previously established lectin magnetic bead array (LeMBA) coupled multiple reaction monitoring mass spectrometry (MRM-MS) tier 3 assay. The area under receiver operating characteristic curve (AUROC) was calculated as a measure of discrimination, and multivariate recursive partitioning was used to formulate a multi-marker panel for BE surveillance. Complement C9 (C9), gelsolin (GSN), serum paraoxonase/arylesterase 1 (PON1) and serum paraoxonase/lactonase 3 (PON3) were validated as diagnostic glycoprotein biomarkers in lectin pull-down samples for EAC across both cohorts. A panel of 10 serum glycoprotein biomarker candidates discriminated BE patients not requiring intervention (BE± low grade dysplasia) from those requiring intervention (BE with high grade dysplasia (BE-HGD) or EAC) with an AUROC value of 0.93. Tissue expression of C9 was found to be induced in BE, dysplastic BE and EAC. In longitudinal samples from subjects that have progressed toward EAC, levels of serum C9 were significantly (p < 0.05) increased with disease progression in EPHA (erythroagglutinin from Phaseolus vulgaris) and NPL (Narcissus pseudonarcissus lectin) pull-down samples. The results confirm alteration of complement pathway glycoproteins during BE-EAC pathogenesis. Further prospective clinical validation of the confirmed biomarker candidates in a large cohort is warranted, prior to development of a first-line BE surveillance blood test.

Exploring the association between BMI and mortality in Australian women and men with and without diabetes: the AusDiab study.

AIMS/HYPOTHESIS: There is conflicting evidence about the obesity paradox-the counterintuitive survival advantage of obesity among certain subpopulations of individuals with chronic conditions. It is believed that results supporting the obesity paradox are due to methodological flaws, such as collider bias. The aim of this study was to examine the association between obesity and mortality in Australian men and women. In addition, we explored whether obesity would appear to be protective if the analysis was restricted to a subpopulation with disease, and to discuss the potential role of collider bias in producing such a result. METHODS: The examined cohort included 10,575 Australian adults (4844 men and 5731 women) aged 25-91 years who were recruited for the AusDiab baseline survey in 1999 and followed-up through 2014. The main predictor variable was BMI categorised as normal weight (18.5 to <25 kg/m2), overweight (25 to <30 kg/m2) and obese (≥30 kg/m2), and the outcome of interest was all-cause mortality. Hazard ratios were estimated from Cox proportional hazards regression models in the entire cohort and then in subpopulations with and without diabetes. RESULTS: A total of 1477 deaths occurred during 145,384 person-years (median 14.6 years) of follow-up. Mortality was higher in obese than in normal-weight individuals for the full population (HR 1.18; 95% CI 1.05, 1.32). When an interaction between diabetes status and BMI category was added to the model, there was no evidence of an interaction between BMI and diabetes status (p = 0.92). When participants with and without diabetes were analysed separately, there was no evidence of an association between obesity and mortality in those with diabetes (HR 0.91; 95% CI 0.62, 1.33). CONCLUSIONS/INTERPRETATION: In the entire AusDiab cohort, we found a significantly higher mortality among obese participants as compared with their normal-weight counterparts. We found no difference in the obesity-mortality association between individuals with and without diabetes.

Quantitative gene profiling of long noncoding RNAs with targeted RNA sequencing.

We compared quantitative RT-PCR (qRT-PCR), RNA-seq and capture sequencing (CaptureSeq) in terms of their ability to assemble and quantify long noncoding RNAs and novel coding exons across 20 human tissues. CaptureSeq was superior for the detection and quantification of genes with low expression, showed little technical variation and accurately measured differential expression. This approach expands and refines previous annotations and simultaneously generates an expression atlas.

Prediction of kinase-specific phosphorylation sites through an integrative model of protein context and sequence.

Identifying kinase substrates and the specific phosphorylation sites they regulate is an important factor in understanding protein function regulation and signalling pathways. Computational prediction of kinase targets - assigning kinases to putative substrates, and selecting from protein sequence the sites that kinases can phosphorylate - requires the consideration of both the cellular context that kinases operate in, as well as their binding affinity. This consideration enables investigation of how phosphorylation influences a range of biological processes. We report here a novel probabilistic model for classifying kinase-specific phosphorylation sites from sequence across three model organisms: human, mouse and yeast. The model incorporates position-specific amino acid frequencies, and counts of co-occurring amino acids from kinase binding sites. We show how this model can be seamlessly integrated with protein interactions and cell-cycle abundance profiles. When evaluating the prediction accuracy of our method, PhosphoPICK, on an independent hold-out set of kinase-specific phosphorylation sites, it achieved an average specificity of 97%, with 32% sensitivity. We compared PhosphoPICK's ability, through cross-validation, to predict kinase-specific phosphorylation sites with alternative methods, and show that at high levels of specificity PhosphoPICK obtains greater sensitivity for most comparisons made. We investigated the relationship between kinase-specific phosphorylation sites and nuclear localisation signals. We show that kinases PKA, Akt1 and AurB have an over-representation of predicted binding sites at particular positions downstream from predicted nuclear localisation signals, demonstrating an important role for these kinases in regulating the nuclear import of proteins. PhosphoPICK is freely available as a web-service at http://bioinf.scmb.uq.edu.au/phosphopick.

Serum Glycoprotein Biomarker Discovery and Qualification Pipeline Reveals Novel Diagnostic Biomarker Candidates for Esophageal Adenocarcinoma.

We report an integrated pipeline for efficient serum glycoprotein biomarker candidate discovery and qualification that may be used to facilitate cancer diagnosis and management. The discovery phase used semi-automated lectin magnetic bead array (LeMBA)-coupled tandem mass spectrometry with a dedicated data-housing and analysis pipeline; GlycoSelector (http://glycoselector.di.uq.edu.au). The qualification phase used lectin magnetic bead array-multiple reaction monitoring-mass spectrometry incorporating an interactive web-interface, Shiny mixOmics (http://mixomics-projects.di.uq.edu.au/Shiny), for univariate and multivariate statistical analysis. Relative quantitation was performed by referencing to a spiked-in glycoprotein, chicken ovalbumin. We applied this workflow to identify diagnostic biomarkers for esophageal adenocarcinoma (EAC), a life threatening malignancy with poor prognosis in the advanced setting. EAC develops from metaplastic condition Barrett's esophagus (BE). Currently diagnosis and monitoring of at-risk patients is through endoscopy and biopsy, which is expensive and requires hospital admission. Hence there is a clinical need for a noninvasive diagnostic biomarker of EAC. In total 89 patient samples from healthy controls, and patients with BE or EAC were screened in discovery and qualification stages. Of the 246 glycoforms measured in the qualification stage, 40 glycoforms (as measured by lectin affinity) qualified as candidate serum markers. The top candidate for distinguishing healthy from BE patients' group was Narcissus pseudonarcissus lectin (NPL)-reactive Apolipoprotein B-100 (p value = 0.0231; AUROC = 0.71); BE versus EAC, Aleuria aurantia lectin (AAL)-reactive complement component C9 (p value = 0.0001; AUROC = 0.85); healthy versus EAC, Erythroagglutinin Phaseolus vulgaris (EPHA)-reactive gelsolin (p value = 0.0014; AUROC = 0.80). A panel of 8 glycoforms showed an improved AUROC of 0.94 to discriminate EAC from BE. Two biomarker candidates were independently verified by lectin magnetic bead array-immunoblotting, confirming the validity of the relative quantitation approach. Thus, we have identified candidate biomarkers, which, following large-scale clinical evaluation, can be developed into diagnostic blood tests. A key feature of the pipeline is the potential for rapid translation of the candidate biomarkers to lectin-immunoassays.

Implementation and evaluation of amyloidosis subtyping by laser-capture microdissection and tandem mass spectrometry.

BACKGROUND: Correct identification of the amyloidosis-causing protein is crucial for clinical management. Recently the Mayo Clinic reported laser-capture microdissection (LCM) with liquid chromatography-coupled tandem mass spectrometry (MS/MS) as a new diagnostic tool for amyloid diagnosis. Here, we report an independent implementation of this proteomic diagnostics method at the Princess Alexandra Hospital Amyloidosis Centre in Brisbane, Australia. RESULTS: From 2010 to 2014, 138 biopsies received from 35 different organ sites were analysed by LCM-MS/MS using Congo Red staining to visualise amyloid deposits. There was insufficient tissue in the block for LCM for 7 cases. An amyloid forming protein was ultimately identified in 121 out of 131 attempted cases (94 %). Of the 121 successful cases, the Mayo Clinic amyloid proteomic signature (at least two of Serum Amyloid P, ApoE and ApoA4) was detected in 92 (76 %). Low levels of additional amyloid forming proteins were frequently identified with the main amyloid forming protein, which may reflect co-deposition of fibrils. Furthermore, vitronectin and clusterin were frequently identified in our samples. Adding vitronectin to the amyloid signature increases the number of positive cases, suggesting a potential 4th protein for the signature. In terms of clinical impact, amyloid typing by immunohistochemistry was attempted in 88 cases, reported as diagnostic in 39, however, 5 were subsequently revealed by proteomic analysis to be incorrect. Overall, the referring clinician's diagnosis of amyloid subtype was altered by proteomic analysis in 24 % of cases. While LCM-MS/MS was highly robust in protein identification, clinical information was still required for subtyping, particularly for systemic versus localized amyloidosis. CONCLUSIONS: This study reports the independent implementation and evaluation of a proteomics-based diagnostic for amyloidosis subtyping. Our results support LCM-MS/MS as a powerful new diagnostic technique for amyloidosis, but also identified some challenges and further development opportunities.

A fine-scale dissection of the DNA double-strand break repair machinery and its implications for breast cancer therapy.

DNA-damage response machinery is crucial to maintain the genomic integrity of cells, by enabling effective repair of even highly lethal lesions such as DNA double-strand breaks (DSBs). Defects in specific genes acquired through mutations, copy-number alterations or epigenetic changes can alter the balance of these pathways, triggering cancerous potential in cells. Selective killing of cancer cells by sensitizing them to further DNA damage, especially by induction of DSBs, therefore requires careful modulation of DSB-repair pathways. Here, we review the latest knowledge on the two DSB-repair pathways, homologous recombination and non-homologous end joining in human, describing in detail the functions of their components and the key mechanisms contributing to the repair. Such an in-depth characterization of these pathways enables a more mechanistic understanding of how cells respond to therapies, and suggests molecules and processes that can be explored as potential therapeutic targets. One such avenue that has shown immense promise is via the exploitation of synthetic lethal relationships, for which the BRCA1-PARP1 relationship is particularly notable. Here, we describe how this relationship functions and the manner in which cancer cells acquire therapy resistance by restoring their DSB repair potential.

Model-based joint visualization of multiple compositional omics datasets.

The integration of multiple omics datasets measured on the same samples is a challenging task: data come from heterogeneous sources and vary in signal quality. In addition, some omics data are inherently compositional, e.g. sequence count data. Most integrative methods are limited in their ability to handle covariates, missing values, compositional structure and heteroscedasticity. In this article we introduce a flexible model-based approach to data integration to address these current limitations: COMBI. We combine concepts, such as compositional biplots and log-ratio link functions with latent variable models, and propose an attractive visualization through multiplots to improve interpretation. Using real data examples and simulations, we illustrate and compare our method with other data integration techniques. Our algorithm is available in the R-package combi.

Biomarker development for axial spondyloarthritis.

The term axial spondyloarthritis (axSpA) encompasses a heterogeneous group of diseases that have variable presentations, extra-articular manifestations and clinical outcomes, and that will respond differently to treatments. The prototypical type of axSpA, ankylosing spondylitis, is thought to be caused by interaction between the genetically primed host immune system and gut microbiota. Currently used biomarkers such as HLA-B27 status, C-reactive protein and erythrocyte sedimentation rate have, at best, moderate diagnostic and predictive value. Improved biomarkers are needed for axSpA to assist with early diagnosis and to better predict treatment responses and long-term outcomes. Advances in a range of 'omics' technologies and statistical approaches, including genomics approaches (such as polygenic risk scores), microbiome profiling and, potentially, transcriptomic, proteomic and metabolomic profiling, are making it possible for more informative biomarker sets to be developed for use in such clinical applications. Future developments in this field will probably involve combinations of biomarkers that require novel statistical approaches to analyse and to produce easy to interpret metrics for clinical application. Large publicly available datasets from well-characterized case-cohort studies that use extensive biological sampling, particularly focusing on early disease and responses to medications, are required to establish successful biomarker discovery and validation programmes.

Dietary intake influences gut microbiota development of healthy Australian children from the age of one to two years.

Early life nutrition is a vital determinant of an individual's life-long health and also directly influences the ecological and functional development of the gut microbiota. However, there are limited longitudinal studies examining the effect of diet on the gut microbiota development in early childhood. Here, up to seven stool samples were collected from each of 48 healthy children during their second year of life, and microbiota dynamics were assessed using 16S rRNA gene amplicon sequencing. Children's dietary information was also collected during the same period using a validated food frequency questionnaire designed for this age group, over five time points. We observed significant changes in gut microbiota community, concordant with changes in the children's dietary pattern over the 12-month period. In particular, we found differential effects on specific Firmicutes-affiliated lineages in response to frequent intake of either processed or unprocessed foods. Additionally, the consumption of fortified milk supplemented with a Bifidobacterium probiotic and prebiotics (synbiotics) further increased the presence of Bifidobacterium spp., highlighting the potential use of synbiotics to prolong and sustain changes in these lineages and shaping the gut microbiota community in young children.

Dynamic molecular changes during the first week of human life follow a robust developmental trajectory.

Systems biology can unravel complex biology but has not been extensively applied to human newborns, a group highly vulnerable to a wide range of diseases. We optimized methods to extract transcriptomic, proteomic, metabolomic, cytokine/chemokine, and single cell immune phenotyping data from <1 ml of blood, a volume readily obtained from newborns. Indexing to baseline and applying innovative integrative computational methods reveals dramatic changes along a remarkably stable developmental trajectory over the first week of life. This is most evident in changes of interferon and complement pathways, as well as neutrophil-associated signaling. Validated across two independent cohorts of newborns from West Africa and Australasia, a robust and common trajectory emerges, suggesting a purposeful rather than random developmental path. Systems biology and innovative data integration can provide fresh insights into the molecular ontogeny of the first week of life, a dynamic developmental phase that is key for health and disease.

Effect of Perioperative Opioids on Cancer-Relevant Circulating Parameters: Mu Opioid Receptor and Toll-Like Receptor 4 Activation Potential, and Proteolytic Profile.

Purpose: The purpose of this study is to investigate the potential interplay between opioid analgesia and tumor metastasis through modulation of µ-opioid receptor (MOR), Toll-like receptor 4 (TLR4) activation, and matrix degradation potential.Experimental Design: Plasma samples were collected from 60 patients undergoing elective lower limb joint replacement preoperatively and at 3, 6, and 24 hours after surgery; pain scores were documented at the same time points. Opioid administration was recorded and converted into morphine IV equivalents. Plasma samples were also collected from 10 healthy volunteers. Alphascreen cyclic AMP assay and MOR-overexpressing cells were employed to quantify MOR activation. HEK-Blue hTLR4 were utilized to measure TLR4 activation. Circulating matrix metalloprotease and tissue inhibitor of matrix protease activities were assessed by gelatin zymography and reverse zymography, respectively.Results: Postoperative plasma samples displayed the ability to activate MOR and to inhibit lipopolysaccharide (LPS)-induced TLR4 activation. Linear mixed model analysis revealed that MOR activation had a significant effect on inhibition of LPS-induced TLR4 activation. Furthermore, TLR4 had a significant effect to explain pain scores. Postoperative samples also displayed altered circulating matrix-degrading enzymes activity potential, but this was correlated neither to opioid administration nor to MOR activation potential.Conclusions: Our results show for the first time that (i) opioids administered to surgery patients result in modulation of ligand-induced TLR4 activation and (ii) postoperative pain is associated with increased circulating TLR4 activation potential. Our study further promotes the use of MOR activation potential rather than opioid intake in clinical studies measuring opioid exposure at a given time point. Clin Cancer Res; 24(10); 2319-27. ©2018 AACR.

Intestinal Metaproteomics Reveals Host-Microbiota Interactions in Subjects at Risk for Type 1 Diabetes.

OBJECTIVE: Dysbiosis of the gut microbiota has been linked to disease pathogenesis in type 1 diabetes, yet the functional consequences to the host of this dysbiosis are unknown. We investigated the functional interactions between the microbiota and the host associated with type 1 diabetes disease risk. RESEARCH DESIGN AND METHODS: We performed a cross-sectional analysis of stool samples from subjects with recent-onset type 1 diabetes (n = 33), islet autoantibody-positive subjects (n = 17), low-risk autoantibody-negative subjects (n = 29), and healthy subjects (n = 22). Metaproteomic analysis was used to identify gut- and pancreas-derived host and microbial proteins, and these data were integrated with sequencing-based microbiota profiling. RESULTS: Both human (host-derived) proteins and microbial-derived proteins could be used to differentiate new-onset and islet autoantibody-positive subjects from low-risk subjects. Significant alterations were identified in the prevalence of host proteins associated with exocrine pancreas output, inflammation, and mucosal function. Integrative analysis showed that microbial taxa associated with host proteins involved in maintaining function of the mucous barrier, microvilli adhesion, and exocrine pancreas were depleted in patients with new-onset type 1 diabetes. CONCLUSIONS: These data support that patients with type 1 diabetes have increased intestinal inflammation and decreased barrier function. They also confirmed that pancreatic exocrine dysfunction occurs in new-onset type 1 diabetes and show for the first time that this dysfunction is present in high-risk individuals before disease onset. The data identify a unique type 1 diabetes-associated signature in stool that may be useful as a means to monitor disease progression or response to therapies aimed at restoring a healthy microbiota.

DynOmics to identify delays and co-expression patterns across time course experiments.

Dynamic changes in biological systems can be captured by measuring molecular expression from different levels (e.g., genes and proteins) across time. Integration of such data aims to identify molecules that show similar expression changes over time; such molecules may be co-regulated and thus involved in similar biological processes. Combining data sources presents a systematic approach to study molecular behaviour. It can compensate for missing data in one source, and can reduce false positives when multiple sources highlight the same pathways. However, integrative approaches must accommodate the challenges inherent in 'omics' data, including high-dimensionality, noise, and timing differences in expression. As current methods for identification of co-expression cannot cope with this level of complexity, we developed a novel algorithm called DynOmics. DynOmics is based on the fast Fourier transform, from which the difference in expression initiation between trajectories can be estimated. This delay can then be used to realign the trajectories and identify those which show a high degree of correlation. Through extensive simulations, we demonstrate that DynOmics is efficient and accurate compared to existing approaches. We consider two case studies highlighting its application, identifying regulatory relationships across 'omics' data within an organism and for comparative gene expression analysis across organisms.