Participants in the Trans-Antarctic Winter Traverse Expedition Showed Increased Bacterial Load and Diversity in Saliva but Maintained Individual Differences within Stool Microbiota and Across Metabolite Fingerprints

Understanding the impact of long-term physiological and environmental stress on the human microbiota and metabolome may be important for the success of space flight. This work is logistically difficult and has a limited number of available participants. Terrestrial analogies present important opportunities to understand changes in the microbiota and metabolome and how this may impact participant health and fitness. Here, we present work from one such analogy: the Transarctic Winter Traverse expedition, which we believe is the first assessment of the microbiota and metabolome from different bodily locations during prolonged environmental and physiological stress. Bacterial load and diversity were significantly higher during the expedition when compared with baseline levels (p < 0.001) in saliva but not stool, and only a single operational taxonomic unit assigned to the Ruminococcaceae family shows significantly altered levels in stool (p < 0.001). Metabolite fingerprints show the maintenance of individual differences across saliva, stool, and plasma samples when analysed using flow infusion electrospray mass spectrometry and Fourier transform infrared spectroscopy. Significant activity-associated changes in terms of both bacterial diversity and load are seen in saliva but not in stool, and participant differences in metabolite fingerprints persist across all three sample types.

Human Milk from Tandem Feeding Dyads Does Not Differ in Metabolite and Metataxonomic Features When Compared to Single Nursling Dyads under Six Months of Age

Given the long-term advantages of exclusive breastfeeding to infants and their mothers, there is both an individual and public health benefit to its promotion and support. Data on the composition of human milk over the course of a full period of lactation for a single nursling is sparse, but data on human milk composition during tandem feeding (feeding children of different ages from different pregnancies) is almost entirely absent. This leaves an important knowledge gap that potentially endangers the ability of parents to make a fully informed choice on infant feeding. We compared the metataxonomic and metabolite fingerprints of human milk samples from 15 tandem feeding dyads to that collected from ten exclusively breastfeeding single nursling dyads where the nursling is under six months of age. Uniquely, our cohort also included three tandem feeding nursling dyads where each child showed a preferential side for feeding-allowing a direct comparison between human milk compositions for different aged nurslings. Across our analysis of volume, total fat, estimation of total microbial load, metabolite fingerprinting, and metataxonomics, we showed no statistically significant differences between tandem feeding and single nursling dyads. This included comparisons of preferential side nurslings of different ages. Together, our findings support the practice of tandem feeding of nurslings, even when feeding an infant under six months.

Rapid ex vivo molecular fingerprinting of biofluids using laser-assisted rapid evaporative ionization mass spectrometry

Of the many metabolites involved in any clinical condition, only a narrow range of biomarkers is currently being used in the clinical setting. A key to personalized medicine would be to extend this range. Metabolic fingerprinting provides a more comprehensive insight, but many methods used for metabolomics analysis are too complex and time-consuming to be diagnostically useful. Here, a rapid evaporative ionization mass spectrometry (REIMS) system for direct ex vivo real-time analysis of biofluids with minor sample pretreatment is detailed. The REIMS can be linked to various laser wavelength systems (such as optical parametric oscillator or CO₂ laser) and with automation for high-throughput analysis. Laser-induced sample evaporation occurs within seconds through radiative heating with the plume guided to the MS instrument. The presented procedure includes (i) laser setup with automation, (ii) analysis of biofluids (blood/urine/stool/saliva/sputum/breast milk) and (iii) data analysis. We provide the optimal settings for biofluid analysis and quality control, enabling sensitive, precise and robust analysis. Using the automated setup, 96 samples can be analyzed in ~35-40 min per ionization mode, with no intervention required. Metabolic fingerprints are made up of 2,000-4,000 features, for which relative quantification can be achieved at high repeatability when total ion current normalization is applied. With saliva and feces as example matrices, >70% of features had a coefficient of variance ≤30%. However, to achieve acceptable long-term reproducibility, additional normalizations by, e.g., LOESS are recommended, especially for positive ionization.

Sample Preparation Free Mass Spectrometry Using Laser-Assisted Rapid Evaporative Ionization Mass Spectrometry: Applications to Microbiology, Metabolic Biofluid Phenotyping, and Food Authenticity

Mass spectrometry has established itself as a powerful tool in the chemical, biological, medical, environmental, and agricultural fields. However, experimental approaches and potential application areas have been limited by a traditional reliance on sample preparation, extraction, and chromatographic separation. Ambient ionization mass spectrometry methods have addressed this challenge but are still somewhat restricted in requirements for sample manipulation to make it suitable for analysis. These limitations are particularly restrictive in view of the move toward high-throughput and automated analytical workflows. To address this, we present what we consider to be the first automated sample-preparation-free mass spectrometry platform utilizing a carbon dioxide (CO₂) laser for sample thermal desorption linked to the rapid evaporative ionization mass spectrometry (LA-REIMS) methodology. We show that the pulsatile operation of the CO₂ laser is the primary factor in achieving high signal-to-noise ratios. We further show that the LA-REIMS automated platform is suited to the analysis of three diverse biological materials within different application areas. First, clinical microbiology isolates were classified to species level with an accuracy of 97.2%, the highest accuracy reported in current literature. Second, fecal samples from a type 2 diabetes mellitus cohort were analyzed with LA-REIMS, which allowed tentative identification of biomarkers which are potentially associated with disease pathogenesis and a disease classification accuracy of 94%. Finally, we showed the ability of the LA-REIMS system to detect instances of adulteration of cooking oil and determine the geographical area of production of three protected olive oil products with 100% classification accuracy.

Off-Colony Screening of Biosynthetic Libraries by Rapid Laser-Enabled Mass Spectrometry

By leveraging advances in DNA synthesis and molecular cloning techniques, synthetic biology increasingly makes use of large construct libraries to explore large design spaces. For biosynthetic pathway engineering, the ability to screen these libraries for a variety of metabolites of interest is essential. If the metabolite of interest or the metabolic phenotype is not easily measurable, screening soon becomes a major bottleneck involving time-consuming culturing, sample preparation, and extraction. To address this, we demonstrate the use of automated laser-assisted rapid evaporative ionization mass spectrometry (LA-REIMS)-a form of ambient laser desorption ionization mass spectrometry-to perform rapid mass spectrometry analysis direct from agar plate yeast colonies without sample preparation or extraction. We use LA-REIMS to assess production levels of violacein and betulinic acid directly from yeast colonies at a rate of 6 colonies per minute. We then demonstrate the throughput enabled by LA-REIMS by screening over 450 yeast colonies within <4 h, while simultaneously generating recoverable glycerol stocks of each colony in real time. This showcases LA-REIMS as a prescreening tool to complement downstream quantification methods such as liquid chromatography-mass spectroscopy (LCMS). By prescreening several hundred colonies with LA-REIMS, we successfully isolate and verify a strain with a 2.5-fold improvement in betulinic acid production. Finally, we show that LA-REIMS can detect 20 out of a panel of 27 diverse biological molecules, demonstrating the broad applicability of LA-REIMS to metabolite detection. The rapid and automated nature of LA-REIMS makes this a valuable new technology to complement existing screening technologies currently employed in academic and industrial workflows.

Metabolic Phenotyping and Strain Characterisation of Pseudomonas aeruginosa Isolates from Cystic Fibrosis Patients Using Rapid Evaporative Ionisation Mass Spectrometry

Rapid evaporative ionisation mass spectrometry (REIMS) is a novel technique for the real-time analysis of biological material. It works by conducting an electrical current through a sample, causing it to rapidly heat and evaporate, with the analyte containing vapour channelled to a mass spectrometer. It was used to characterise the metabolome of 45 Pseudomonas aeruginosa (P. aeruginosa) isolates from cystic fibrosis (CF) patients and compared to 80 non-CF P. aeruginosa. Phospholipids gave the highest signal intensity; 17 rhamnolipids and 18 quorum sensing molecules were detected, demonstrating that REIMS has potential for the study of virulence-related metabolites. P. aeruginosa isolates obtained from respiratory samples showed a higher diversity, which was attributed to the chronic nature of most respiratory infections. The analytical sensitivity of REIMS allowed the detection of a metabolome that could be used to classify individual P. aeruginosa isolates after repeated culturing with 81% accuracy, and an average 83% concordance with multilocus sequence typing. This study underpins the capacities of REIMS as a tool with clinical applications, such as metabolic phenotyping of the important CF pathogen P. aeruginosa, and highlights the potential of metabolic fingerprinting for fine scale characterisation at a sub-species level.

A pilot study using metagenomic sequencing of the sputum microbiome suggests potential bacterial biomarkers for lung cancer

Lung cancer (LC) is the most prevalent cancer worldwide, and responsible for over 1.3 million deaths each year. Currently, LC has a low five year survival rates relative to other cancers, and thus, novel methods to screen for and diagnose malignancies are necessary to improve patient outcomes. Here, we report on a pilot-sized study to evaluate the potential of the sputum microbiome as a source of non-invasive bacterial biomarkers for lung cancer status and stage. Spontaneous sputum samples were collected from ten patients referred with possible LC, of which four were eventually diagnosed with LC (LC⁺), and six had no LC after one year (LC^-). Of the seven bacterial species found in all samples, Streptococcus viridans was significantly higher in LC⁺ samples. Seven further bacterial species were found only in LC^-, and 16 were found only in samples from LC⁺. Additional taxonomic differences were identified in regards to significant fold changes between LC⁺ and LC^-cases, with five species having significantly higher abundances in LC⁺, with Granulicatella adiacens showing the highest level of abundance change. Functional differences, evident through significant fold changes, included polyamine metabolism and iron siderophore receptors. G. adiacens abundance was correlated with six other bacterial species, namely Enterococcus sp. 130, Streptococcus intermedius, Escherichia coli, S. viridans, Acinetobacter junii, and Streptococcus sp. 6, in LC⁺ samples only, which could also be related to LC stage. Spontaneous sputum appears to be a viable source of bacterial biomarkers which may have utility as biomarkers for LC status and stage.

Metabolomic-based biomarker discovery for non-invasive lung cancer screening: A case study

BACKGROUND

Lung cancer (LC) is one of the leading lethal cancers worldwide, with an estimated 18.4% of all cancer deaths being attributed to the disease. Despite developments in cancer diagnosis and treatment over the previous thirty years, LC has seen little to no improvement in the overall five year survival rate after initial diagnosis.

METHODS

In this paper, we extended a recent study which profiled the metabolites in sputum from patients with lung cancer and age-matched volunteers smoking controls using flow infusion electrospray ion mass spectrometry. We selected key metabolites for distinguishing between different classes of lung cancer, and employed artificial neural networks and leave-one-out cross-validation to evaluate the predictive power of the identified biomarkers.

RESULTS

The neural network model showed excellent performance in classification between lung cancer and control groups with the area under the receiver operating characteristic curve of 0.99. The sensitivity and specificity of for detecting cancer from controls were 96% and 94% respectively. Furthermore, we have identified six putative metabolites that were able to discriminate between sputum samples derived from patients suffering small cell lung cancer (SCLC) and non-small cell lung cancer. These metabolites achieved excellent cross validation performance with a sensitivity of 80% and specificity of 100% for predicting SCLC.

CONCLUSIONS

These results indicate that sputum metabolic profiling may have potential for screening of lung cancer and lung cancer recurrence, and may greatly improve effectiveness of clinical intervention. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.

Metagenomic Sequencing of the Chronic Obstructive Pulmonary Disease Upper Bronchial Tract Microbiome Reveals Functional Changes Associated with Disease Severity

Chronic Obstructive Pulmonary Disease (COPD) is a major source of mortality and morbidity worldwide. The microbiome associated with this disease may be an important component of the disease, though studies to date have been based on sequencing of the 16S rRNA gene, and have revealed unequivocal results. Here, we employed metagenomic sequencing of the upper bronchial tract (UBT) microbiome to allow for greater elucidation of its taxonomic composition, and revealing functional changes associated with the disease. The bacterial metagenomes within sputum samples from eight COPD patients and ten 'healthy' smokers (Controls) were sequenced, and suggested significant changes in the abundance of bacterial species, particularly within the Streptococcus genus. The functional capacity of the COPD UBT microbiome indicated an increased capacity for bacterial growth, which could be an important feature in bacterial-associated acute exacerbations. Regression analyses correlated COPD severity (FEV1% of predicted) with differences in the abundance of Streptococcus pneumoniae and functional classifications related to a reduced capacity for bacterial sialic acid metabolism. This study suggests that the COPD UBT microbiome could be used in patient risk stratification and in identifying novel monitoring and treatment methods, but study of a longitudinal cohort will be required to unequivocally relate these features of the microbiome with COPD severity.

The metabolomic detection of lung cancer biomarkers in sputum

OBJECTIVES

Developing screening and diagnosis methodologies based on novel biomarkers should allow for the detection of the lung cancer (LC) and possibly at an earlier stage and thereby increase the effectiveness of clinical interventions. Here, our primary objective was to evaluate the potential of spontaneous sputum as a source of non-invasive metabolomic biomarkers for LC status.

MATERIALS AND METHODS

Spontaneous sputum was collected and processed from 34 patients with suspected LC, alongside 33 healthy controls. Of the 34 patients, 23 were subsequently diagnosed with LC (LC(+), 16 NSCLC, six SCLC, and one radiological diagnosis), at various stages of disease progression. The 67 samples were analysed using flow infusion electrospray ion mass spectrometry (FIE-MS) and gas-chromatography mass spectrometry (GC-MS).

RESULTS

Principal component analysis identified negative mode FIE-MS as having the main separating power between samples from healthy and LC. Discriminatory metabolites were identified using ANOVA and Random Forest. Indications of potential diagnostic accuracy involved the use of receiver operating characteristic/area under the curve (ROC/AUC) analyses. This approach identified metabolites changes that were only observed with LC. Metabolites with AUC values of greater than 0.8 which distinguished between LC(+)/LC(-) binary classifications where identified and included Ganglioside GM1 which has previously been linked to LC.

CONCLUSION

This study indicates that metabolomics based on sputum can yield metabolites that can be used as a diagnostic and/or discriminator tool. These could aid clinical intervention and targeted diagnosis of LC within an 'at risk' LC(-) population group. The use of sputum as a non-invasive source of metabolite biomarkers may aid in the development of an at-risk population screening programme for lung cancer or enhanced clinical diagnostic pathways.

Characterization of the rumen lipidome and microbiome of steers fed a diet supplemented with flax and echium oil

Developing novel strategies for improving the fatty acid composition of ruminant products relies upon increasing our understanding of rumen bacterial lipid metabolism. This study investigated whether flax or echium oil supplementation of steer diets could alter the rumen fatty acids and change the microbiome. Six Hereford × Friesian steers were offered grass silage/sugar beet pulp only (GS), or GS supplemented either with flax oil (GSF) or echium oil (GSE) at 3% kg⁻¹ silage dry matter in a 3 × 3 replicated Latin square design with 21-day periods with rumen samples taken on day 21 for the analyses of the fatty acids and microbiome. Flax oil supplementation of steer diets increased the intake of polyunsaturated fatty acids, but a substantial degree of rumen biohydrogenation was seen. Likewise, echium oil supplementation of steer diets resulted in increased intake of 18:4n-3, but this was substantially biohydrogenated within the rumen. Microbiome pyrosequences showed that 50% of the bacterial genera were core to all diets (found at least once under each dietary intervention), with 19.10%, 5.460% and 12.02% being unique to the rumen microbiota of steers fed GS, GSF and GSE respectively. Higher 16S rDNA sequence abundance of the genera Butyrivibrio, Howardella, Oribacterium, Pseudobutyrivibrio and Roseburia was seen post flax feeding. Higher 16S rDNA abundance of the genus Succinovibrio and Roseburia was seen post echium feeding. The role of these bacteria in biohydrogenation now requires further study.

Effects of bovine colostrum supplementation on upper respiratory illness in active males

Bovine colostrum (COL) has been advocated as a nutritional countermeasure to exercise-induced immune dysfunction and increased risk of upper respiratory illness (URI) in athletic populations, however, the mechanisms remain unclear. During winter months, under double-blind procedures, 53 males (mean training load±SD, 50.5±28.9 MET-hweek(-1)) were randomized to daily supplementation of 20g of COL (N=25) or an isoenergetic/isomacronutrient placebo (PLA) (N=28) for 12weeks. Venous blood was collected at baseline and at 12weeks and unstimulated saliva samples at 4 weeks intervals. There was a significantly lower proportion of URI days and number of URI episodes with COL compared to PLA over the 12weeks (p<0.05). There was no effect of COL on in vitro neutrophil oxidative burst, salivary secretory IgA or salivary antimicrobial peptides (p>0.05), which does not support previously suggested mechanisms. In a subset of participants (COL=14, PLA=17), real-time quantitative PCR, targeting the 16S rRNA gene showed there was an increase in salivary bacterial load over the 12 weeks period with PLA (p<0.05) which was not as evident with COL. Discriminant function analysis of outputs received from serum metabolomics showed changes across time but not between groups. This is the first study to demonstrate that COL limits the increased salivary bacterial load in physically active males during the winter months which may provide a novel mechanism of immune-modulation with COL and a relevant marker of in vivo (innate) immunity and risk of URI.