Biology subjects

Rodrigues, C.

Publications and source records attributed to Rodrigues, C..

3 recordsLinked to original sources

bioRxiv2018

Identification of Klebsiella pneumoniae complex members using MALDI-TOF mass spectrometry

Klebsiella pneumoniae (phylogroup Kp1), one of the most problematic pathogens associated with antibiotic resistance worldwide, is phylogenetically closely related to K. quasipneumoniae [subsp. quasipneumoniae (Kp2) and subsp. similipneumoniae (Kp4)], K. variicola (Kp3) and two unnamed phylogroups (Kp5 and Kp6). Together, Kp1 to Kp6 make-up the K. pneumoniae complex. Currently, the phylogroups can be reliably identified only by gene sequencing. Misidentification using standard methods is common and the clinical significance of K. pneumoniae complex members is therefore imprecisely defined. Here, we evaluated the potential of MALDI-TOF mass spectrometry to discriminate K. pneumoniae complex members. We report for the first time the existence of mass spectrometry biomarkers associated with the phylogroups, with a sensitivity and specificity ranging between 80-100% and 97-100%, respectively. Strains within phylogroups Kp1, Kp2, Kp4 and Kp5 each shared two specific peaks not observed in other phylogroups. Kp3 strains shared a peak that was only observed otherwise in Kp5. Finally, Kp6 had a diagnostic peak shared only with Kp1. Kp3 and Kp6 could therefore be identified by exclusion criteria (lacking Kp5 and Kp1-specific peaks, respectively). Further, ranked Pearson correlation clustering of spectra grouped strains according to their phylogroup. These results call for incorporation of spectra of all K. pneumoniae complex members into reference MALDI-TOF spectra databases, in which they are currently lacking. This advance may allow for simple and precise identification of K. pneumoniae and closely related species, opening the way to a better understanding of their epidemiology, ecology and pathogenesis.

microbiology↗

bioRxiv2017

A Suv39H1-low chromatin state drives migratory cell populations in cervical cancer

The emergence of migratory cell populations within tumours represents a critical early stage during cancer metastasis. We have previously reported one such population, marked by CD66, in cervical cancers. It is unclear what broad mechanisms regulate such migratory populations. Here, we describe the role of a Suv39H1-low heterochromatin state as a driver of cervical cancer migratory populations. Cervical cancer cells sorted based on migratory ability in vitro show low Suv39H1, and Suv39H1 knockdown enhances cell migration. Histopathology shows the emergence of migratory Suv39H1low populations in advanced carcinoma progression. Meta-analysis of data from The Cancer Genome Atlas (TCGA) reveals that Suv39H1-low tumours show migration and CD66 expression signatures, and correlate with lower patient survival. Lastly, genome-wide profiling of migrated populations using RNA-Seq and H3K9me3 ChIP-Seq reveals Suv39H1-linked transcriptome alterations and a broad loss of H3K9me3, suggesting an increase in chromatin plasticity in migrated populations. The understanding of such chromatin based regulation in migratory populations may prove valuable in efforts to develop anti-metastatic strategies.

cancer biology↗

bioRxiv2017

Optimal homotopy analysis of a chaotic HIV-1 model incorporating AIDS-related cancer cells

The studies of nonlinear models in epidemiology have generated a deep interest in gaining insight into the mechanisms that underlie AIDS-related cancers, providing us with a better understanding of cancer immunity and viral oncogenesis. In this article, we analyse an HIV-1 model incorporating the relations between three dynamical variables: cancer cells, healthy CD4+ T lymphocytes and infected CD4+ T lymphocytes. Recent theoretical investigations indicate that these cells interactions lead to different dynamical outcomes, for instance to periodic or chaotic behavior. Firstly, we analytically prove the boundedness of the trajectories in the systems attractor. The complexity of the coupling between the dynamical variables is quantified using observability indices. Our calculations reveal that the highest observable variable is the population of cancer cells, thus indicating that these cells could be monitored in future experiments in order to obtain time series for attractors reconstruction. We identify different dynamical behaviors of the system varying two biologically meaningful parameters: r1, representing the uncontrolled proliferation rate of cancer cells, and k1, denoting the immune systems killing rate of cancer cells. The maximum Lyapunov exponent is computed to identify the chaotic regimes. Considering very recent developments in the literature related to the homotopy analysis method (HAM), we construct the explicit series solution of the cancer model and focus our analysis on the dynamical variable with the highest observability index. An optimal homotopy analysis approach is used to improve the computational efficiency of HAM by means of appropriate values for the convergence control parameter, which greatly accelerate the convergence of the series solution.

systems biology↗