Analysis of gut microbiome profiles in common marmosets (Callithrix jacchus) in health and intestinal disease.

Chronic gastrointestinal (GI) diseases are the most common diseases in captive common marmosets. To understand the role of the microbiome in GI diseases, we characterized the gut microbiome of 91 healthy marmosets (303 samples) and 59 marmosets diagnosed with inflammatory bowel disease (IBD) (200 samples). Healthy marmosets exhibited "humanized," Bacteroidetes-dominant microbiomes. After up to 2 years of standardized diet, housing and husbandry, marmoset microbiomes could be classified into four distinct marmoset sources based on Prevotella and Bacteroides levels. Using a random forest (RF) model, marmosets were classified by source with an accuracy of 93% with 100% sensitivity and 95% specificity using abundance data from 4 Prevotellaceae amplicon sequence variants (ASVs), as well as single ASVs from Coprobacter, Parabacteroides, Paraprevotella, Phascolarctobacterium, Oribacterium and Fusobacterium. A single dysbiotic IBD state was not found across all marmoset sources, but IBD was associated with lower alpha diversity and a lower Bacteroides:Prevotella copri ratio within each source. IBD was highest in a Prevotella-dominant cohort, and consistent with Prevotella-linked diseases, pro-inflammatory genes in the jejunum were upregulated. RF analysis of serum biomarkers identified serum calcium, hemoglobin and red blood cell (RBC) counts as potential biomarkers for marmoset IBD. This study characterizes the microbiome of healthy captive common marmosets and demonstrates that source-specific microbiomes can be retained despite standardized diets and husbandry practices. Marmosets with IBD had decreased alpha diversity and a shift in the ratio of Bacteroides:Prevotella copri compared to healthy marmosets.

Alterations in common marmoset gut microbiome associated with duodenal strictures.

Chronic gastrointestinal (GI) diseases are the most common diseases in captive common marmosets (Callithrix jacchus). Despite standardized housing, diet and husbandry, a recently described gastrointestinal syndrome characterized by duodenal ulcers and strictures was observed in a subset of marmosets sourced from the New England Primate Research Center. As changes in the gut microbiome have been associated with GI diseases, the gut microbiome of 52 healthy, non-stricture marmosets (153 samples) were compared to the gut microbiome of 21 captive marmosets diagnosed with a duodenal ulcer/stricture (57 samples). No significant changes were observed using alpha diversity metrics, and while the community structure was significantly different when comparing beta diversity between healthy and stricture cases, the results were inconclusive due to differences observed in the dispersion of both datasets. Differences in the abundance of individual taxa using ANCOM, as stricture-associated dysbiosis was characterized by Anaerobiospirillum loss and Clostridium perfringens increases. To identify microbial and serum biomarkers that could help classify stricture cases, we developed models using machine learning algorithms (random forest, classification and regression trees, support vector machines and k-nearest neighbors) to classify microbiome, serum chemistry or complete blood count (CBC) data. Random forest (RF) models were the most accurate models and correctly classified strictures using either 9 ASVs (amplicon sequence variants), 4 serum chemistry tests or 6 CBC tests. Based on the RF model and ANCOM results, C. perfringens was identified as a potential causative agent associated with the development of strictures. Clostridium perfringens was also isolated by microbiological culture in 4 of 9 duodenum samples from marmosets with histologically confirmed strictures. Due to the enrichment of C. perfringens in situ, we analyzed frozen duodenal tissues using both 16S microbiome profiling and RNAseq. Microbiome analysis of the duodenal tissues of 29 marmosets from the MIT colony confirmed an increased abundance of Clostridium in stricture cases. Comparison of the duodenal gene expression from stricture and non-stricture marmosets found enrichment of genes associated with intestinal absorption, and lipid metabolism, localization, and transport in stricture cases. Using machine learning, we identified increased abundance of C. perfringens, as a potential causative agent of GI disease and intestinal strictures in marmosets.

Historical criteria that distinguish syncope from seizures.

OBJECTIVES: We prospectively sought evidence-based criteria that distinguished between seizures and syncope. BACKGROUND: Loss of consciousness is usually due to either seizures or syncope. There are no evidence-based historical diagnostic criteria that distinguish them. METHODS: A total of 671 patients with loss of consciousness completed a 118-item historical questionnaire. Data sets were complete for all subjects. The data set was randomly divided into two equal groups. The contributions of symptoms to diagnoses in one group were estimated with logistic regression and point scores were developed. The accuracy of the decision rule was then assessed using split-half analysis. Analyses were performed with and without inclusion of measures of symptom burden, which were the number of losses of consciousness and the duration of the history. The scores were tested using receiver-operator characteristic analysis. RESULTS: The causes of loss of consciousness were known satisfactorily in 539 patients and included seizures (n = 102; complex partial epilepsy [50 patients] and primary generalized epilepsy [52 patients]) and syncope (n = 437; tilt-positive vasovagal syncope [267 patients], ventricular tachycardia [90 patients] and other diagnoses such as complete heart block and supraventricular tachycardias [80 patients]). The point score based on symptoms alone correctly classified 94% of patients, diagnosing seizures with 94% sensitivity and 94% specificity. Including symptom burden did not significantly improve accuracy, indicating that the symptoms surrounding the loss of consciousness accurately discriminate between seizures and syncope. CONCLUSIONS: A simple point score of historical features distinguishes syncope from seizures with very high sensitivity and specificity.

Optimizing epilepsy surgery with intraoperative MR imaging.

PURPOSE: The surgical treatment of medically intractable temporal lobe epilepsy includes the resection of temporal lobe structures. Although the reported seizure-free outcomes are highly variable, there is growing evidence that the extent of resection of the mesiotemporal lobe directly correlates with seizure control. METHODS: A moveable, high-field intraoperative magnetic resonance (MR) system was used to monitor and optimize the resection of the amygdala and hippocampus in 14 epilepsy patients. Fourteen patients with intractable seizures of temporal lobe origin underwent standard preoperative investigations including MR imaging, EEG telemetry, single-photon emission computed tomography, and neuropsychologic and sodium amytal testing. Anterior temporal lobectomy was performed on 10 patients, whereas four were treated with selective amygdalohippocampectomy. Intraoperative electrocorticography was applied as required. For all procedures, the objective was to resect the amygdala completely, and hippocampus to the posterior margin of the brainstem. RESULTS: Interdissection intraoperative MR imaging taken when optimal resection was thought to have been achieved revealed residual unresected amygdala or hippocampus in seven of 14 patients. An unexpected acute hematoma was found in one patient. At 17 months' follow-up, 13 (93%) of 14 patients are seizure free or have significantly improved seizure control. CONCLUSIONS: The mobile high-field intraoperative MR system provides high-resolution images without restriction on surgical instruments or techniques. The ability to identify and resect residual mesial temporal lobe targets before craniotomy closure is of potentially tremendous value in optimizing seizure control.