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Highlights • Mass spectrometry provides an ideal tool for metabolic profiling of microbial communities. • Wide range of approaches suited for different metabolites of interest. • Field of ambient ionisation mass spectrometry highly promising for rapid analysis. • Experimental approach needed to separate correlation and causality. Abstract The interaction between microbial communities and their environment, such as the human gastrointestinal tract, has been an area of microbiology rapidly advanced, by developments in sequencing technology. However, these techniques are largely limited to the detection of the taxonomic composition of a microbial community and/or its genetic functional capacity. Here, we discuss a range of mass spectrometry-based approaches which researchers can employ to explore the host-microbiome interactions at the metabolic level. Traditional approaches to mass spectrometry are detailed, alongside new developments in the field, namely ambient ionisation mass spectrometry and imaging mass spectrometry, which we believe will prove to be important to future work in this field. We further discuss considerations for experimental workflows, data analysis options and propose a methodology for the establishment of causal relationships between functional host-microbiome interactions with regards to health and disease in the human gastrointestinal tract. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Highlight: [•] This is the first study on salivary microbiome in athletes, showing increased bacterial load during winter months which is limited with bovine colostrum supplementation. [ABSTRACT FROM AUTHOR]

Cryoconite holes are known as foci of microbial diversity and activity on polar glacier surfaces, but are virtually unexplored microbial habitats in alpine regions. In addition, whether cryoconite community structure reflects ecosystem functionality is poorly understood. Terminal restriction fragment length polymorphism and Fourier transform infrared metabolite fingerprinting of cryoconite from glaciers in Austria, Greenland and Svalbard demonstrated cryoconite bacterial communities are closely correlated with cognate metabolite fingerprints. The influence of bacterial-associated fatty acids and polysaccharides was inferred, underlining the importance of bacterial community structure in the properties of cryoconite. Thus, combined application of T-RFLP and FT- IR metabolite fingerprinting promises high throughput, and hence, rapid assessment of community structure-function relationships. Pyrosequencing revealed Proteobacteria were particularly abundant, with Cyanobacteria likely acting as ecosystem engineers in both alpine and Arctic cryoconite communities. However, despite these generalities, significant differences in bacterial community structures, compositions and metabolomes are found between alpine and Arctic cryoconite habitats, reflecting the impact of local and regional conditions on the challenges of thriving in glacial ecosystems. [ABSTRACT FROM AUTHOR]