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Gonzalez-Jimenez, M.

Publications and source records attributed to Gonzalez-Jimenez, M..

2 recordsLinked to original sources

Low-frequency vibrational modes in G-quadruplexes reveal the mechanical properties of nucleic acids

Low-frequency vibrations play an essential role in biomolecular processes involving DNA such as gene expression, charge transfer, drug intercalation, and DNA-protein recognition. However, understanding of the vibrational basis of these mechanisms relies on theoretical models due to the lack of experimental evidence. Here we present the low-frequency vibrational spectra of G-quadruplexes (structures formed by four strands of DNA) and B-DNA characterized using femtosecond optical Kerr-effect spectroscopy. Contrary to expectation, we found that G-quadruplexes show several strongly underdamped delocalized phonon-like modes that have the potential to contribute to the biology of the DNA at the atomic level. In addition, G-quadruplexes present modes at a higher frequency than B-DNA demonstrating that changes in the stiffness of the molecule alter its gigahertz to terahertz vibrational profile. These results demonstrate that current theoretical models fail to predict basic properties of the vibrational modes of DNA. Statement of significanceA number of recent studies have identified thermally excited low-frequency vibrational modes as a key deciding factor in the biological function of DNA. However, the nature of these vibrational modes has never been established. Here, vibrational spectroscopy with unrivalled signal-to-noise in the gigahertz to terahertz range is used to determine the low-frequency Raman spectra of nucleotides and oligomeric DNAs carefully chosen to form G-quadruplexes, structures formed by four strands of DNA common in the genome. These G-quadruplexes exhibit an unusual group of highly-underdamped delocalized vibrational modes--not reproduced by any of the theoretical models in use--which are expected to be the thermally excited. This provides a new perspective on the role of low-frequency vibrational modes in protein interactions and allostery.

biophysics↗

Prediction of malaria mosquito species and population age structure using mid-infrared spectroscopy and supervised machine learning

Despite the global efforts made in the fight against malaria, the disease is resurging. One of the main causes is the resistance that Anopheles mosquitoes, vectors of the disease, have developed to insecticides. Anopheles must survive for at least 12 days to possibly transmit malaria. Therefore, to evaluate and improve malaria vector control interventions, it is imperative to monitor and accurately estimate the age distribution of mosquito populations as well as total population sizes. However, estimating mosquito age is currently a slow, imprecise, and labour-intensive process that can only distinguish under-from over-four-day-old female mosquitoes. Here, we demonstrate a machine-learning based approach that utilizes mid-infrared spectra of mosquitoes to characterize simultaneously, and with unprecedented accuracy, both age and species identity of females of the malaria vectors Anopheles gambiae and An. arabiensis mosquitoes within their respective populations. The prediction of the age structures was statistically indistinguishable from true modelled distributions. The method has a negligible cost per mosquito, does not require highly trained personnel, is substantially faster than current techniques, and so can be easily applied in both laboratory and field settings. Our results show that, with larger mid-infrared spectroscopy data sets, this technique can be further improved and expanded to vectors of other diseases such as Zika and Dengue.

bioinformatics↗