Fingerprinting microbiomes towards screening for microbial antibiotic resistance

There is an increasing need to investigate microbiomes in their entirety in a variety of contexts ranging from environmental to human health scenarios. This requirement is becoming increasingly important with emergence of antibiotic resistance. In general, more conventional approaches are too expensive and/or time-consuming and often predicated on prior knowledge of the microorganisms one wishes to study. Herein, we propose the use of biospectroscopy tools as relatively high-throughput, non-destructive approaches to profile microbiomes under study. Fourier-transform infrared (FTIR) or Raman spectroscopy both generate fingerprint spectra of biological material and such spectra can readily be subsequently classed according to biochemical changes in the microbiota, such as emergence of antibiotic resistance. FTIR spectroscopy techniques generally can only be applied to desiccated material whereas Raman approaches can be applied to more hydrated samples. The ability to readily fingerprint microbiomes could lend itself to new approaches in determining microbial behaviours and emergence of antibiotic resistance

Molecular networks of human muscle adaptation to exercise and age

Physical activity and molecular ageing presumably interact to precipitate musculoskeletal decline in humans with age. Herein, we have delineated molecular networks for these two major components of sarcopenic risk using multiple independent clinical cohorts. We generated genome-wide transcript profiles from individuals (n = 44) who then undertook 20 weeks of supervised resistance-exercise training (RET). Expectedly, our subjects exhibited a marked range of hypertrophic responses (3% to +28%), and when applying Ingenuity Pathway Analysis (IPA) up-stream analysis to ~580 genes that co-varied with gain in lean mass, we identified rapamycin (mTOR) signaling associating with growth (P = 1.4×10−30). Paradoxically, those displaying most hypertrophy exhibited an inhibited mTOR activation signature, including the striking down-regulation of 70 rRNAs. Differential analysis found networks mimicking developmental processes (activated all-trans-retinoic acid (ATRA, Z-score = 4.5; P = 6×10−13) and inhibited aryl-hydrocarbon receptor signaling (AhR, Z-score = −2.3; P = 3×10−7)) with RET. Intriguingly, as ATRA and AhR gene-sets were also a feature of endurance exercise training (EET), they appear to represent “generic” physical activity responsive gene-networks. For age, we found that differential gene-expression methods do not produce consistent molecular differences between young versus old individuals. Instead, utilizing two independent cohorts (n = 45 and n = 52), with a continuum of subject ages (18–78 y), the first reproducible set of age-related transcripts in human muscle was identified. This analysis identified ~500 genes highly enriched in post-transcriptional processes (P = 1×10−6) and with negligible links to the aforementioned generic exercise regulated gene-sets and some overlap with ribosomal genes. The RNA signatures from multiple compounds all targeting serotonin, DNA topoisomerase antagonism, and RXR activation were significantly related to the muscle age-related genes. Finally, a number of specific chromosomal loci, including 1q12 and 13q21, contributed by more than chance to the age-related gene list (P = 0.01–0.005), implying possible epigenetic events. We conclude that human muscle age-related molecular processes appear distinct from the processes regulated by those of physical activity

Tutorial: Multivariate Classification for Vibrational Spectroscopy in Biological Samples

Vibrational spectroscopy techniques, such as Fourier-transform infrared (FTIR) and Raman spectroscopy, have been successful methods for studying the interaction of light with biological materials and facilitating novel cell biology analysis. Spectrochemical analysis is very attractive in disease screening and diagnosis, microbiological studies and forensic and environmental investigations because of its low cost, minimal sample preparation, non-destructive nature and substantially accurate results. However, there is now an urgent need for multivariate classification protocols allowing one to analyze biologically derived spectrochemical data to obtain accurate and reliable results. Multivariate classification comprises discriminant analysis and class-modeling techniques where multiple spectral variables are analyzed in conjunction to distinguish and assign unknown samples to pre-defined groups. The requirement for such protocols is demonstrated by the fact that applications of deep-learning algorithms of complex datasets are being increasingly recognized as critical for extracting important information and visualizing it in a readily interpretable form. Hereby, we have provided a tutorial for multivariate classification analysis of vibrational spectroscopy data (FTIR, Raman and near-IR) highlighting a series of critical steps, such as preprocessing, data selection, feature extraction, classification and model validation. This is an essential aspect toward the construction of a practical spectrochemical analysis model for biological analysis in real-world applications, where fast, accurate and reliable classification models are fundamental

New results from pionic atoms

In the last few years the resolution of Ge- and Si-detectors has been improved remarkably, particularly for low energetic X-rays. It therefore became possible to measure absorption broadenings and transition energies of pionic 2p-1s transitions in very light elements, which could be measured before with only limited accuracy. In a recently performed measurement of the pionic 2p-1s transitions in /sup 6/Li, /sup 7/Li and /sup 9/Be a small Ge- and a small Si- detector were used with resolutions of better than 550 eV below 50 keV and better than 200 eV below 10 keV, respectively. The broadening Gamma /sub 1S/ of the pionic 1s level due to strong interaction between the orbiting pion and nucleus was measured with an accuracy of 10-20 eV, corresponding to an error of less than 5%. The shift of the binding energy of the pion in the 1s level Delta E/sub 1s/, due to strong interaction, was determined to an accuracy of 15 eV, corresponding to an error of less than 5% for Li and around 0.5% for Be. For the calibration of the system, muonic lines, adjacent to the pionic lines were used. The energies of these monic lines are easily calculated. The resolution of the spectrometer was calibrated using the same muonic lines. (5 refs)