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Biology subjects

Luis AJ Mur

Publications and source records attributed to Luis AJ Mur.

2 recordsLinked to original sources

Extreme metagenomics using nanopore DNA sequencing: a field report from Svalbard, 78 N

In the field of observation, chance favours only the prepared mind (Pasteur). Impressive developments in genomics have led microbiology to its third \"Golden Age\". However, conventional metagenomics strategies necessitate retrograde transfer of samples from extreme or remote environments for later analysis, rendering the powerful insights gained retrospective in nature, striking a contrast with Pasteurs dictum. Here we implement highly portable USB-based nanopore DNA sequencing platforms coupled with field-adapted environmental DNA extraction, rapid sequence library generation and off-line analyses of shotgun metagenome and 16S ribosomal RNA gene amplicon profiles to characterize microbiota dwelling within cryoconite holes upon Svalbard glaciers, the Greenland Ice Sheet and the Austrian Alps. We show in-field nanopore sequencing of metagenomes captures taxonomic composition of supraglacial microbiota, while 16S rRNA Furthermore, comparison of nanopore data with prior 16S rRNA gene V1-V3 pyrosequencing from the same samples, demonstrates strong correlations between profiles obtained from nanopore sequencing and laboratory based sequencing approaches. gene amplicon sequencing resolves bacterial community responses to habitat changes. Finally, we demonstrate the fidelity and sensitivity of in-field sequencing by analysis of mock communities using field protocols. Ultimately, in-field sequencing potentiated by nanopore devices raises the prospect of enhanced agility in exploring Earths most remote microbiomes.

Microbiology

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

BackgroundLung 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.\n\nMethodsIn 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.\n\nResultsThe neural network model showed excellent performance in clas sification 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 todiscriminate 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.\n\nConclusionsThese 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.

Cancer Biology