Biology subjects

Jimenez, J.

Publications and source records attributed to Jimenez, J..

22 records · Page 2Linked to original sources

bioRxiv2018

Mutational analysis of N-ethyl-N-nitrosourea (ENU) in the fission yeast Schizosaccharomyces pombe.

Forward genetics has boosted our knowledge on genic function in a multitude of biological models and it has significantly contributed to the understanding of genetic bases of development, ageing and human diseases. With the advent of the next generation sequencing and use of powerful bioinformatic tools, this traditional genetic strategy has acquired a new impulse. At present, whole genome sequencing assisted by in silico analysis allows the rapid and efficient identification of gene variants that are responsible for a particular phenotype. In this experimental pipeline, it is crucial to start by provoking a large number of random changes in the genome of the model organisms to be screened. A range of chemical mutagens are used to this end because most of them display particular reactivity properties and act differently over DNA. Here we use N-ethyl-N-nitrosourea (ENU) as a mutagen for the first time to our knowledge in the fission yeast Schizosaccharomyces pombe. By comparison to the extensively used Ethyl methanesulfonate (EMS) in a phenotype-based study, we conclude that ENU is a very potent and easy-to-use mutagen. Judging from DNA sequence analysis of the identified mutants, ENU induces base changes rather than indels and the mutational spectrum in the fission yeast seems similar to that found in mice but different to that described in other single-celled organisms such as budding yeast and E. coli. Using ENU in S. pombe, we have gathered a collection of 49 auxotrophic mutants including two deleterious alleles of ATIC human ortholog. Defective alleles of this gene are causative of AICA-Ribosiduria, a severe genetic disease. We have also identified 5 aminoglycoside-resistance inactivating mutations in APH genes. All these mutations reported here may be of interest in the metabolism and antibiotic resistance research fields.

genetics↗

bioRxiv2018

Progression of recent Mycobacterium tuberculosis exposure to active tuberculosis is a highly heritable complex trait driven by 3q23 in Peruvians

Among 1.8 billion people worldwide infected with Mycobacterium tuberculosis, 5-15% are expected to develop active tuberculosis (TB). Approximately half of these will progress to active TB within the first 18 months after infection, presumably because they fail to mount the initial immune response that contains the local bacterial spread. The other half will reactivate their latent infection later in life, likely triggered by a loss of immune competence due to factors such as HIV-associated immunosuppression or ageing. This natural history suggests that undiscovered host genetic factors may control early progression to active TB. Here, we report results from a large genome-wide genetic study of early TB progression. We genotyped a total of 4,002 active TB cases and their household contacts in Peru and quantified genetic heritability [Formula] of early TB progression to be 21.2% under the liability scale. Compared to the reported [Formula] of genome-wide TB susceptibility (15.5%), this result indicates early TB progression has a stronger genetic basis than population-wide TB susceptibility. We identified a novel association between early TB progression and variants located in an enhancer region on chromosome 3q23 (rs73226617, OR=1.19; P < 5x10-8). We used in silico and in vitro analyses to identify likely functional variants and target genes, highlighting new candidate mechanisms of host response in early TB progression.

genetics↗

bioRxiv2017

Sampling Stability And Processing Parameter-Dependent Characteristics Of The 3D Fractal Dimension As A Marker Of Structural Brain Complexity In Magnetic Resonance Images

Fractal analysis represents a promising new approach to structural neuroimaging data, yet systematic evaluation of the fractal dimension (FD) as a marker of structural brain complexity is scarce. Here we present in-depth methodological assessment of FD estimation in structural brain MRI. On the computational side, we show that spatial scale optimization can significantly improve FD estimation accuracy, as suggested by simulation studies with known FD values. For empirical evaluation, we analyzed two recent open-access neuroimaging data sets (MASSIVE and Midnight Scan Club), stratified by fundamental image characteristics including registration, sequence weighting, spatial resolution, segmentation procedures, tissue type, and image complexity. Deviation analyses showed high repeated-acquisition stability of the FD estimates across both data sets, with differential deviation susceptibility according to image characteristics. While less frequently studied in the literature, FD estimation in T2-weighted images yielded robust outcomes. Importantly, we observed a significant impact of image registration on absolute FD estimates. Applying different registration schemes, we found that unbalanced registration induced i) repeated-measurement deviation clusters around the registration target, ii) strong bidirectional correlations among image analysis groups, and iii) spurious associations between the FD and an index of structural similarity, and these effects were strongly attenuated by reregistration in both data sets. Indeed, differences in FD between scans did not simply track differences in structure per se, suggesting that structural complexity and structural similarity represent distinct aspects of structural brain MRI. In conclusion, scale optimization can improve FD estimation accuracy, and empirical FD estimates are reliable yet sensitive to image characteristics.

neuroscience↗

bioRxiv2016

Resource Competition Shapes the Response of Genetic Circuits

A common approach to design genetic circuits is to compose gene expression cassettes together. While appealing, this modular approach is challenged by the fact that expression of each gene depends on the availability of transcriptional/translational resources, which is in turn determined by the presence of other genes in the circuit. This raises the question of how competition for resources by different genes affects a circuits behavior. Here, we create a library of genetic activation cascades in bacteria E. coli, where we explicitly tune the resource demand by each gene. We develop a general Hill-function-based model that incorporates resource competition effects through resource demand coefficients. These coefficients lead to non-regulatory interactions among genes that reshape circuits behavior. For the activation cascade, such interactions result in surprising biphasic or monotonically decreasing responses. Finally, we use resource demand coefficients to guide the choice of ribosome binding site (RBS) and DNA copy number to restore the cascades intended monotonically increasing response. Our results demonstrate how unintended circuits behavior arises from resource competition and provide a model-guided methodology to minimize the resulting effects.\n\nO_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

synthetic biology↗