RESUMO
Salmonella enterica is represented by >2,600 serovars that can differ in routes of transmission, host colonization, and in resistance to antimicrobials. S. enterica is the leading bacterial cause of foodborne illness in the United States, with well-established detection methodology. Current surveillance protocols rely on the characterization of a few colonies to represent an entire sample; thus, minority serovars remain undetected. Salmonella contains two CRISPR loci, CRISPR1 and CRISPR2, and the spacer contents of these can be considered serovar specific. We exploited this property to develop an amplicon-based and multiplexed sequencing approach, CRISPR-SeroSeq (serotyping by sequencing of the CRISPR loci), to identify multiple serovars present in a single sample. Using mixed genomic DNA from two Salmonella serovars, we were able to confidently detect a serovar that constituted 0.01% of the sample. Poultry is a major reservoir of Salmonella spp., including serovars that are frequently associated with human illness, as well as those that are not. Numerous studies have examined the prevalence and diversity of Salmonella spp. in poultry, though these studies were limited to culture-based approaches and therefore only identified abundant serovars. CRISPR-SeroSeq was used to investigate samples from broiler houses and a processing facility. Ninety-one percent of samples harbored multiple serovars, and there was one sample in which four different serovars were detected. In another sample, reads for the minority serovar comprised 0.003% of the total number of Salmonella spacer reads. The most abundant serovars identified were Salmonella enterica serovars Montevideo, Kentucky, Enteritidis, and Typhimurium. CRISPR-SeroSeq also differentiated between multiple strains of some serovars. This high resolution of serovar populations has the potential to be utilized as a powerful tool in the surveillance of Salmonella species.IMPORTANCESalmonella enterica is the leading bacterial cause of foodborne illness in the United States and is represented by over 2,600 distinct serovars. Some of these serovars are pathogenic in humans, while others are not. Current surveillance for this pathogen is limited by the detection of only the most abundant serovars, due to the culture-based approaches that are used. Thus, pathogenic serovars that are present in a minority remain undetected. By exploiting serovar-specific differences in the CRISPR arrays of Salmonella spp., we have developed a high-throughput sequencing tool to be able to identify multiple serovars in a single sample and tested this in multiple poultry samples. This novel approach allows differences in the dynamics of individual Salmonella serovars to be measured and can have a significant impact on understanding the ecology of this pathogen with respect to zoonotic risk and public health.
Assuntos
Repetições Palindrômicas Curtas Agrupadas e Regularmente Espaçadas , Doenças das Aves Domésticas/microbiologia , Salmonelose Animal/microbiologia , Infecções por Salmonella/microbiologia , Salmonella enterica/genética , Salmonella enterica/isolamento & purificação , Sorotipagem/métodos , Animais , Galinhas , Humanos , Salmonella enterica/classificaçãoRESUMO
Answer ALS is a biological and clinical resource of patient-derived, induced pluripotent stem (iPS) cell lines, multi-omic data derived from iPS neurons and longitudinal clinical and smartphone data from over 1,000 patients with ALS. This resource provides population-level biological and clinical data that may be employed to identify clinical-molecular-biochemical subtypes of amyotrophic lateral sclerosis (ALS). A unique smartphone-based system was employed to collect deep clinical data, including fine motor activity, speech, breathing and linguistics/cognition. The iPS spinal neurons were blood derived from each patient and these cells underwent multi-omic analytics including whole-genome sequencing, RNA transcriptomics, ATAC-sequencing and proteomics. The intent of these data is for the generation of integrated clinical and biological signatures using bioinformatics, statistics and computational biology to establish patterns that may lead to a better understanding of the underlying mechanisms of disease, including subgroup identification. A web portal for open-source sharing of all data was developed for widespread community-based data analytics.