Biology subjects

Ramirez, M.

Publications and source records attributed to Ramirez, M..

3 recordsLinked to original sources

bioRxiv2018

Altered protein quality control contributes to noise-induced hearing loss

Exposure to damaging levels of noise is the most common cause of hearing loss and impairs high frequency hearing in more than 15 % of adult Americans. Using mice exposed to increasing levels of noise in combination with quantitative proteomics, we tested how noise insults remodel the cochlear proteome both acutely and after a two-week recovery period. We used ABR & DPOAE recordings to define the intensity of noise exposure necessary to produce temporary or permanent threshold shifts (TTS, PTS) in young adult mice and found noise at 94 and 105 dB SPL levels for 30 minutes elicits TTS and PTS, respectively. We quantified thousands of proteins and found that noise insults cause a rapid increase rather than a decrease in the levels of many proteins involved with protein homeostasis, myelin, cytoskeletal structures, and cell junctions such as the synapse. The vast majority of proteins with increased levels immediately after noise exposure showed normal levels after two weeks of recovery. However, several proteins involved in oxidative stress and neuroprotection had significantly increased levels only after the recovery period suggesting they play in important role in regeneration. Interestingly, a small panel of mitochondrial proteins were significantly altered only in PTS conditions suggesting potential discrete protein mechanisms. Our discovery-based proteomic analysis extends the recent description of noise-induced cochlear synaptopathy and shows that noise insults drive a robust proteostasis response. These data provide a new understanding of noise sensitive proteins and may inform the development of effective preventiative strategies or therapies for NIHL.

neuroscience↗

bioRxiv2018

Contribution of common and rare variants to bipolar disorder susceptibility in extended pedigrees from population isolates

Current evidence from case/control studies indicates that genetic risk for psychiatric disorders derives primarily from numerous common variants, each with a small phenotypic impact. The literature describing apparent segregation of bipolar disorder (BP) in numerous multigenerational pedigrees suggests that, in such families, large-effect inherited variants might play a greater role. To evaluate this hypothesis, we conducted genetic analyses in 26 Colombian (CO) and Costa Rican (CR) pedigrees ascertained for BP1, the most severe and heritable form of BP. In these pedigrees, we performed microarray SNP genotyping of 856 individuals and high-coverage whole-genome sequencing of 454 individuals. Compared to their unaffected relatives, BP1 individuals had higher polygenic risk scores estimated from SNPs associated with BP discovered in independent genome-wide association studies, and also displayed a higher burden of rare deleterious single nucleotide variants (SNVs) and rare copy number variants (CNVs) in genes likely to be relevant to BP1. Parametric and non-parametric linkage analyses identified 15 BP1 linkage peaks, encompassing about 100 genes, although we observed no significant segregation pattern for any particular rare SNVs and CNVs. These results suggest that even in extended pedigrees, genetic risk for BP appears to derive mainly from small to moderate effect rare and common variants.

genetics↗

bioRxiv2017

chewBBACA: A complete suite for gene-by-gene schema creation and strain identification

Gene-by-gene approaches are becoming increasingly popular in bacterial genomic epidemiology and outbreak detection. However, there is a lack of open-source scalable software for schema definition and allele calling for these methodologies. The chewBBACA suite was designed to assist users in the creation and evaluation of novel whole-genome or core-genome gene-by-gene typing schemas and subsequent allele calling in bacterial strains of interest. The software can run in a laptop or in high performance clusters making it useful for both small laboratories and large reference centers. ChewBBACA is available at https://github.com/B-UMMI/chewBBACA or as a docker image at https://hub.docker.com/r/ummidock/chewbbaca/.\n\nDATA SUMMARYO_LIAssembled genomes used for the tutorial were downloaded from NCBI in August 2016 by selecting those submitted as Streptococcus agalactiae taxon or sub-taxa. All the assemblies have been deposited as a zip file in FigShare (https://figshare.com/s/9cbe1d422805db54cd52), where a file with the original ftp link for each NCBI directory is also available.\nC_LIO_LICode for the chewBBACA suite is available at https://github.com/B-UMMI/chewBBACA while the tutorial example is found at https://github.com/B-UMMI/chewBBACA_tutorial.\nC_LI\n\nI/We confirm all supporting data, code and protocols have been provided within the article or through supplementary data files. {boxtimes}\n\nIMPACT STATEMENTThe chewBBACA software offers a computational solution for the creation, evaluation and use of whole genome (wg) and core genome (cg) multilocus sequence typing (MLST) schemas. It allows researchers to develop wg/cgMLST schemes for any bacterial species from a set of genomes of interest. The alleles identified by chewBBACA correspond to potential coding sequences, possibly offering insights into the correspondence between the genetic variability identified and phenotypic variability. The software performs allele calling in a matter of seconds to minutes per strain in a laptop but is easily scalable for the analysis of large datasets of hundreds of thousands of strains using multiprocessing options. The chewBBACA software thus provides an efficient and freely available open source solution for gene-by-gene methods. Moreover, the ability to perform these tasks locally is desirable when the submission of raw data to a central repository or web services is hindered by data protection policies or ethical or legal concerns.

bioinformatics↗