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.
is represented by >2,600 serovars that can differ in routes of transmission, host colonization, and in resistance to antimicrobials. is the leading bacterial cause of foodborne illness in the United States with well-established detection methodology. Current surveillance protocols rely on characterization of a few colonies to represent an entire sample, thus minority serovars remain undetected. contains two CRISPR loci, CRISPR1 and CRISPR2, and the spacer content of these can be considered serovar specific. We exploited this property to develop an amplicon-based and multiplexed sequencing approach, CRISPR-SeroSeq, to identify multiple serovars present in a single sample. Using mixed genomic DNA from two serovars, we were able to confidently detect a serovar that constituted 0.01% of the sample.Poultry is a major reservoir for, 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 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. 91% of samples harbored multiple serovars, including one where four different serovars were detected.In another sample, reads for the minority serovar comprised 0.003% of the total number of spacer reads. The most abundant serovars identified were 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 surveillance of is the leading bacterial cause of foodborne illness in the United States and is represented by over 2600 distinct serovars. Some of these serovars are pathogenic in humans, while others are not. Current surveillance for this pathogen is limited by 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 , we have developed a highthroughput 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 dynamics of individual 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.
The clinical presentation of amyotrophic lateral sclerosis (ALS), a fatal neurodegenerative disease, varies widely across patients, making it challenging to determine if potential therapeutics slow progression. We sought to determine whether there were common patterns of disease progression that could aid in the design and analysis of clinical trials. We developed an approach based on a mixture of Gaussian processes to identify clusters of patients sharing similar disease progression patterns, modeling their average trajectories and the variability in each cluster. We show that ALS progression is frequently nonlinear, with periods of stable disease preceded or followed by rapid decline. We also show that our approach can be extended to Alzheimer’s and Parkinson’s diseases. Our results advance the characterization of disease progression of ALS and provide a flexible modeling approach that can be applied to other progressive diseases.
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