Edward Zhao

BACKGROUND: Nonmotor symptoms are common among patients with Parkinson's disease (PD) and some may precede disease diagnosis. METHODS: We conducted a meta-analysis on the prevalence of selected nonmotor symptoms before and after PD diagnosis, using random-effect models. We searched PubMed (1965 through October/November 2012) for the following symptoms: hyposmia, constipation, rapid eye movement sleep behavior disorder, excessive daytime sleepiness, depression, and anxiety. Eligible studies were publications in English with original data on one or more of these symptoms. RESULTS: The search generated 2,373 non-duplicated publications and 332 met the inclusion criteria, mostly (n = 320) on symptoms after PD diagnosis. For all symptoms, the prevalence was substantially higher in PD cases than in controls, each affecting over a third of the patients. Hyposmia was the most prevalent (75.5% in cases vs. 19.1% in controls), followed by constipation (50% vs. 17.7%), anxiety (39.9% vs. 19.1%), rapid eye movement sleep behavior disorder (37.0% vs. 7.0%), depression (36.6% vs. 14.9%), and excessive daytime sleepiness (33.9% vs. 10.5%). We observed substantial heterogeneities across studies and meta-regression analyses suggested that several factors might have contributed to this. However, the prevalence estimates were fairly robust in several sensitivity analyses. Only 20 studies had data on any symptoms prior to PD diagnosis, but still the analyses revealed higher prevalence in future PD cases than in controls. CONCLUSION: These symptoms are common among PD patients both before and after diagnosis. Further studies are needed to understand the natural history of nonmotor symptoms in PD and their etiological and clinical implications.

Abstract Accurate cell typing is fundamental to analysis of spatial single-cell transcriptomics, but legacy scRNA-seq algorithms can underperform in this new type of data. We have developed a cell typing algorithm, Insitutype, designed for statistical and computational efficiency in spatial transcriptomics data. Insitutype is based on a likelihood model that weighs the evidence from every expression value, extracting all the information available in each cell’s expression profile. This likelihood model underlies a Bayes classifier for supervised cell typing, and an Expectation-Maximization algorithm for unsupervised and semi-supervised clustering. Insitutype also leverages alternative data types collected in spatial studies, such as cell images and spatial context, by using them to inform prior probabilities of cell type calls. We demonstrate rapid clustering of millions of cells and accurate fine-grained cell typing of kidney and non-small cell lung cancer samples.

Recent studies have determined that inflammasome signaling plays an important role in driving intestinal epithelial cell (IEC) responses to bacterial infections, such as Salmonella enterica serovar Typhimurium. There are two primary inflammasome pathways, canonical (involving caspase-1) and noncanonical (involving caspase-4 and -5 in humans and caspase-11 in mice). Prior studies identified the canonical inflammasome as the major pathway leading to interleukin-18 (IL-18) release and restriction of S .

Background Inherited peripheral neuropathies (IPNs) represent a broad group of genetically and clinically heterogeneous disorders, including axonal Charcot-Marie-Tooth type 2 (CMT2) and hereditary motor neuropathy (HMN). Approximately 60%–70% of cases with HMN/CMT2 still remain without a genetic diagnosis. Interestingly, mutations in HMN/CMT2 genes may also be responsible for motor neuron disorders or other neuromuscular diseases, suggesting a broad phenotypic spectrum of clinically and genetically related conditions. Thus, it is of paramount importance to identify novel causative variants in HMN/CMT2 patients to better predict clinical outcome and progression. Methods We designed a collaborative study for the identification of variants responsible for HMN/CMT2. We collected 15 HMN/CMT2 families with evidence for autosomal recessive inheritance, who had tested negative for mutations in 94 known IPN genes, who underwent whole-exome sequencing (WES) analyses. Candidate genes identified by WES were sequenced in an additional cohort of 167 familial or sporadic HMN/CMT2 patients using next-generation sequencing (NGS) panel analysis. Results Bioinformatic analyses led to the identification of novel or very rare variants in genes, which have not been previously associated with HMN/CMT2 ( ARHGEF28 , KBTBD13 , AGRN and GNE ); in genes previously associated with HMN/CMT2 but in combination with different clinical phenotypes ( VRK1 and PNKP ), and in the SIGMAR1 gene, which has been linked to HMN/CMT2 in only a few cases. These findings were further validated by Sanger sequencing, segregation analyses and functional studies. Conclusions These results demonstrate the broad spectrum of clinical phenotypes that can be associated with a specific disease gene, as well as the complexity of the pathogenesis of neuromuscular disorders.