David Israeli

Artificial sweeteners induce glucose intolerance by altering the gut microbiota

Non-caloric artificial sweeteners (NAS) are among the most widely used food additives worldwide, regularly consumed by lean and obese individuals alike. NAS consumption is considered safe and beneficial owing to their low caloric content, yet supporting scientific data remain sparse and controversial. Here we demonstrate that consumption of commonly used NAS formulations drives the development of glucose intolerance through induction of compositional and functional alterations to the intestinal microbiota. These NAS-mediated deleterious metabolic effects are abrogated by antibiotic treatment, and are fully transferrable to germ-free mice upon faecal transplantation of microbiota configurations from NAS-consuming mice, or of microbiota anaerobically incubated in the presence of NAS. We identify NAS-altered microbial metabolic pathways that are linked to host susceptibility to metabolic disease, and demonstrate similar NAS-induced dysbiosis and glucose intolerance in healthy human subjects. Collectively, our results link NAS consumption, dysbiosis and metabolic abnormalities, thereby calling for a reassessment of massive NAS usage.

Personalized Nutrition by Prediction of Glycemic Responses.

Elevated postprandial blood glucose levels constitute a global epidemic and a major risk factor for prediabetes and type II diabetes, but existing dietary methods for controlling them have limited efficacy. Here, we continuously monitored week-long glucose levels in an 800-person cohort, measured responses to 46,898 meals, and found high variability in the response to identical meals, suggesting that universal dietary recommendations may have limited utility. We devised a machine-learning algorithm that integrates blood parameters, dietary habits, anthropometrics, physical activity, and gut microbiota measured in this cohort and showed that it accurately predicts personalized postprandial glycemic response to real-life meals. We validated these predictions in an independent 100-person cohort. Finally, a blinded randomized controlled dietary intervention based on this algorithm resulted in significantly lower postprandial responses and consistent alterations to gut microbiota configuration. Together, our results suggest that personalized diets may successfully modify elevated postprandial blood glucose and its metabolic consequences. VIDEO ABSTRACT.

Environment dominates over host genetics in shaping human gut microbiota

Human gut microbiome composition is shaped by multiple factors but the relative contribution of host genetics remains elusive. Here we examine genotype and microbiome data from 1,046 healthy individuals with several distinct ancestral origins who share a relatively common environment, and demonstrate that the gut microbiome is not significantly associated with genetic ancestry, and that host genetics have a minor role in determining microbiome composition. We show that, by contrast, there are significant similarities in the compositions of the microbiomes of genetically unrelated individuals who share a household, and that over 20% of the inter-person microbiome variability is associated with factors related to diet, drugs and anthropometric measurements. We further demonstrate that microbiome data significantly improve the prediction accuracy for many human traits, such as glucose and obesity measures, compared to models that use only host genetic and environmental data. These results suggest that microbiome alterations aimed at improving clinical outcomes may be carried out across diverse genetic backgrounds.

Transcranial optical vascular imaging (TOVI) of cortical hemodynamics in mouse brain

In vivo imaging of cerebral vasculature and blood flow provides highly valuable information for clinicians as well as researchers. Nevertheless, currently available methods are complex, time-consuming and expensive. Here, we present a novel, minimally invasive method for vascular imaging through the sufficiently transparent intact skull of young mice. Our method combines laser speckle and fluorescent imaging with dynamic color mapping and image fusion. Quickly generated wide-field images present clear visual information on blood flow and perfusion in the cerebral cortex and meninges. The ability of the method to visualize hemodynamic changes is demonstrated by induced occlusion of the middle cerebral artery. The compact and easily operated system comprises of several pieces of standard and affordable laboratory equipment. This simple, robust and inexpensive method may become an important tool for assessment of brain hemodynamics in preclinical studies.

Perceptual learning in autism: over-specificity and possible remedies

Inflexible behavior is a core characteristic of autism spectrum disorder (ASD), but its underlying cause is unknown. Using a perceptual learning protocol, we observed initially efficient learning in ASD that was followed by anomalously poor learning when the location of the target was changed (over-specificity). Reducing stimulus repetition eliminated over-specificity. Our results indicate that inflexible behavior may be evident ubiquitously in ASD, even in sensory learning, but can be circumvented by specifically designed stimulation protocols.

Circulating miRNAs are generic and versatile therapeutic monitoring biomarkers in muscular dystrophies

The development of medical approaches requires preclinical and clinical trials for assessment of therapeutic efficacy. Such evaluation entails the use of biomarkers, which provide information on the response to the therapeutic intervention. One newly-proposed class of biomarkers is the microRNA (miRNA) molecules. In muscular dystrophies (MD), the dysregulation of miRNAs was initially observed in muscle biopsy and later extended to plasma samples, suggesting that they may be of interest as biomarkers. First, we demonstrated that dystromiRs dysregulation occurs in MD with either preserved or disrupted expression of the dystrophin-associated glycoprotein complex, supporting the utilization of dystromiRs as generic biomarkers in MD. Then, we aimed at evaluation of the capacity of miRNAs as monitoring biomarkers for experimental therapeutic approach in MD. To this end, we took advantage of our previously characterized gene therapy approach in a mouse model for α-sarcoglycanopathy. We identified a dose-response correlation between the expression of miRNAs on both muscle tissue and blood serum and the therapeutic benefit as evaluated by a set of new and classically-used evaluation methods. This study supports the utility of profiling circulating miRNAs for the evaluation of therapeutic outcome in medical approaches for MD.

Environmental factors dominate over host genetics in shaping human gut microbiota composition

Human gut microbiome composition is shaped by multiple host intrinsic and extrinsic factors, but the relative contribution of host genetic compared to environmental factors remains elusive. Here, we genotyped a cohort of 696 healthy individuals from several distinct ancestral origins and a relatively common environment, and demonstrate that there is no statistically significant association between microbiome composition and ethnicity, single nucleotide polymorphisms (SNPs), or overall genetic similarity, and that only 5 of 211 (2.4%) previously reported microbiome-SNP associations replicate in our cohort. In contrast, we find similarities in the microbiome composition of genetically unrelated individuals who share a household. We define the term biome-explainability as the variance of a host phenotype explained by the microbiome after accounting for the contribution of human genetics. Consistent with our finding that microbiome and host genetics are largely independent, we find significant biome-explainability levels of 16-33% for body mass index (BMI), fasting glucose, high-density lipoprotein (HDL) cholesterol, waist circumference, waist-hip ratio (WHR), and lactose consumption. We further show that several human phenotypes can be predicted substantially more accurately when adding microbiome data to host genetics data, and that the contribution of both data sources to prediction accuracy is largely additive. Overall, our results suggest that human microbiome composition is dominated by environmental factors rather than by host genetics.

A simple approach for non-invasive transcranial optical vascular imaging (nTOVI).

In vivo imaging of cerebral vasculature is highly vital for clinicians and medical researchers alike. For a number of years non-invasive optical-based imaging of brain vascular network by using standard fluorescence probes has been considered as impossible. In the current paper controverting this paradigm, we present a robust non-invasive optical-based imaging approach that allows visualize major cerebral vessels at the high temporal and spatial resolution. The developed technique is simple to use, utilizes standard fluorescent dyes, inexpensive micro-imaging and computation procedures. The ability to clearly visualize middle cerebral artery and other major vessels of brain vascular network, as well as the measurements of dynamics of blood flow are presented. The developed imaging approach has a great potential in neuroimaging and can significantly expand the capabilities of preclinical functional studies of brain and notably contribute for analysis of cerebral blood circulation in disorder models. An example of 1 × 1.5 cm color-coded image of brain blood vessels of mouse obtained in vivo by transcranial optical vascular imaging (TOVI) approach through the intact cranium.

The time course and characteristics of procedural learning in schizophrenia patients and healthy individuals

Patients with schizophrenia have deficits in some types of procedural learning. Several mechanisms contribute to this learning in healthy individuals, including statistical and sequence-learning. To find preserved and impaired learning mechanisms in schizophrenia, we studied the time course and characteristics of implicitly introduced sequence-learning (SRT task) in 15 schizophrenia patients (seven mild and eight severe) and nine healthy controls, in short sessions over multiple days (5–22). The data show speed gains of similar magnitude for all groups, but the groups differed in overall speed and in the characteristics of the learning. By analyzing the data according to its spatial-position and temporal-order components, we provide evidence for two types of learning that could differentiate the groups: while the learning of the slower, severe group was dominated by statistical learning, the control group moved from a fast learning phase of statistical-related performance to subsequence learning (chunking). Our findings oppose the naïve assumption that a similar gain of speed reflects a similar learning process; they indicate that the slower performance reflects the activation of a different motor plan than does the faster performance; and demonstrate that statistical learning and subsequence learning are two successive stages in implicit sequence learning, with chunks inferred from prior statistical computations. Our results indicate that statistical learning is intact in patients with schizophrenia, but is slower to develop in the severe patients. We suggest that this slow learning rate and the associated slow performance contribute to their deficit in developing sequence-specific learning by setting a temporal constraint on developing higher order associations.

Response: Commentary: Perceptual learning in autism: over-specificity and possible remedies

In a recent study, we tested perceptual learning in adults with autism spectrum disorder (ASD) (Harris et al., 2015), employing the standard and well-established texture-learning paradigm [TDT; (Karni and Sagi, 1991; Sagi, 1995; Harris et al., 2012)]. In this paradigm, observers learn to discriminate an oriented texture target embedded at a fixed location in a background of elements having a different orientation. Performance is measured as a function of the time-interval between the onset of the target and a mask (stimulus onset asynchrony, SOA), with threshold defined as the minimal time (SOA) to reach a predefined criterion level of performance. Typical observers improve their performance (show reduced thresholds) with training across 3–4 days, but need to relearn the task when the target is moved to a different location in the visual field, showing specificity. We (Harris et al., 2015) reported similar results with observers with ASD, but unlike the typical observers who showed faster learning at the second location (Sagi, 2011), ASD observers showed difficulty in relearning the task at the second location, suggesting that the training with the target at the first location might have interfered with the training at the new, second location. We termed this anomalous poor learning “over-specificity” (OS) to reflect the narrowness of the learning and the failure to generalize, and quantified OS as the average threshold difference between the second and the first learning curves (for generalization OS 0). A modified learning paradigm, where standard target trials were interleaved with no-target trials (“dummy” trials) during training, showed generalization of learning (OS 1) has never been observed before.