Advancing Personalized Medicine by Analytical Means: Selection of Three Metabolites That Allows Discrimination between Glaucoma, Diabetes, and Controls.

This paper aimed at devising an intelligence-based method to select compounds that can distinguish between open-angle glaucoma patients, type 2 diabetes patients, and healthy controls. Taking the concentration of 188 compounds measured in the aqueous humour (AH) of patients and controls, linear discriminant analysis (LDA) was used to identify the right combination of compounds that could lead to accurate diagnosis. All possibilities, using the leave-one-out approach, were considered through ad hoc programming and in silico massive data production and statistical analysis. Our proof of concept led to the selection of four molecules: acetyl-ornithine (Ac-Orn), C3 acyl-carnitine (C3), diacyl C42:6 phosphatidylcholine (PC aa C42:6), and C3-DC (C4-OH) acyl-carnitine (C3-DC (C4-OH)) that, taken in combination, would lead to a 95% discriminative success. 100% success was obtained with a non-linear combination of the concentration of three of these four compounds. By discarding younger controls to adjust by age, results were similar although one control was misclassified as a diabetes patient. Methods based on the consideration of individual clinical chemical parameters have limitations in the ability to make a reliable diagnosis, stratify patients, and assess disease progression. Leveraging human AH metabolomic data, we developed a procedure that selects a minimal number of metabolites (3-5) and designs algorithms that maximize the overall accuracy evaluating both positive predictive (PPV) and negative predictive (NPV) values. Our approach of simultaneously considering the levels of a few metabolites can be extended to any other body fluid and has potential to advance precision medicine. Artificial intelligence is expected to use algorithms that use the concentration of three to five molecules to correctly diagnose diseases, also allowing stratification of patients and evaluation of disease progression. In addition, this significant advance shifts focus from a single-molecule biomarker approach to that of an appropriate combination of metabolites.

A metabolomics study in aqueous humor discloses altered arginine metabolism in Parkinson's disease.

BACKGROUND: The lack of accessible and informative biomarkers results in a delayed diagnosis of Parkinson's disease (PD), whose symptoms appear when a significant number of dopaminergic neurons have already disappeared. The retina, a historically overlooked part of the central nervous system (CNS), has gained recent attention. It has been discovered that the composition of cerebrospinal fluid influences the aqueous humor composition through microfluidic circulation. In addition, alterations found in the brain of patients with PD have a correlate in the retina. This new paradigm highlights the potential of the aqueous humor as a sample for identifying differentially concentrated metabolites that could, eventually, become biomarkers if also found altered in blood or CSF of patients. In this research we aim at analyzing the composition of the aqueous humor from healthy controls and PD patients. METHODS: A targeted metabolomics approach with concentration determination by mass spectrometry was used. Statistical methods including principal component analysis and linear discriminants were used to select differentially concentrated metabolites that allow distinguishing patients from controls. RESULTS: In this first metabolomics study in the aqueous humor of PD patients, elevated levels of 16 compounds were found; molecules differentially concentrated grouped into biogenic amines, amino acids, and acylcarnitines. A biogenic amine, putrescine, alone could be a metabolite capable of differentiating between PD and control samples. The altered levels of the metabolites were correlated, suggesting that the elevations stem from a common mechanism involving arginine metabolism. CONCLUSIONS: A combination of three metabolites, putrescine, tyrosine, and carnitine was able to correctly classify healthy participants from PD patients. Altered metabolite levels suggest altered arginine metabolism. The pattern of metabolomic disturbances was not due to the levodopa-based dopamine replacement medication because one of the patients was not yet taking levodopa but a dopamine receptor agonist.

Biogenic Amine Levels Markedly Increase in the Aqueous Humor of Individuals with Controlled Type 2 Diabetes.

The composition of the aqueous humor of patients with type 2 diabetes is relevant to understanding the underlying causes of eye-related comorbidities. Information on the composition of aqueous humor in healthy subjects is limited due to the lack of adequate controls. To carry out a metabolomics study, 31 samples of aqueous humor from healthy subjects without ocular pathology, submitted to refractive surgery and seven samples from patients with type 2 diabetes without signs of ocular pathology related to diabetes were used. The level of 25 molecules was significantly (p < 0.001) altered in the aqueous humor of the patient group. The concentration of a single molecule, N-acetylornithine, makes it possible to discriminate between control and diabetes (sensitivity and specificity equal to 1). In addition, receptor operating characteristic curve and principal component analysis for the above-mentioned six molecules yielded significantly (p < 0.001) altered in the aqueous humor of the patient group. In addition, receptor operating characteristic curve and principal component analysis for six compounds yielded cut-off values and remarkable sensitivity, specificity, and segregation ability. The altered level of N-acetylornithine may be due to an increased amount of acetate in diabetes. It is of interest to further investigate whether this alteration is related to the pathogenesis of the disease. The increase in the amino form of pyruvate, alanine, in diabetes is also relevant because it could be a means of reducing the formation of lactate from pyruvate.

M2 cortex-dorsolateral striatum stimulation reverses motor symptoms and synaptic deficits in Huntington's disease.

Huntington's disease (HD) is a neurological disorder characterized by motor disturbances. HD pathology is most prominent in the striatum, the central hub of the basal ganglia. The cerebral cortex is the main striatal afferent, and progressive cortico-striatal disconnection characterizes HD. We mapped striatal network dysfunction in HD mice to ultimately modulate the activity of a specific cortico-striatal circuit to ameliorate motor symptoms and recover synaptic plasticity. Multimodal MRI in vivo indicates cortico-striatal and thalamo-striatal functional network deficits and reduced glutamate/glutamine ratio in the striatum of HD mice. Moreover, optogenetically-induced glutamate release from M2 cortex terminals in the dorsolateral striatum (DLS) was undetectable in HD mice and striatal neurons show blunted electrophysiological responses. Remarkably, repeated M2-DLS optogenetic stimulation normalized motor behavior in HD mice and evoked a sustained increase of synaptic plasticity. Overall, these results reveal that selective stimulation of the M2-DLS pathway can become an effective therapeutic strategy in HD.

Value of monitoring circulating donor-reactive memory B cells to characterize antibody-mediated rejection after kidney transplantation.

Antibody-mediated rejection (ABMR) is defined by specific histopathological lesions and evidence of circulating donor-specific antibodies (DSA). Although DSA are not always detectable, monitoring donor-reactive memory B cells (mBC) could identify patients at risk of developing ABMR. Peripheral donor-reactive mBC using a novel HLA B cell ELISpot assay, serum DSA, and numbers of different B cell subsets were assessed in 175 consecutive kidney transplants undergoing either for-cause or 6- and 24-month surveillance biopsies for their association with main histological lesions of ABMR and impact on allograft outcome. In 85 incident for-cause biopsies, high frequencies of donor-reactive mBC were detected in all 16 (100%) acute ABMR/DSA+ and most chronic ABMR, with or without DSA (24/30[80%] and 21/29[72.4%], respectively). In a longitudinal cohort of 90 nonsensitized patients, a progressively higher expansion of donor-reactive mBC than de novo DSA was observed at 6 and 24 months (8.8% vs 7.7% and 15.5% vs 11.1%, respectively) and accurately identified patients with ongoing subclinical ABMR (area under the curve = 0.917 and area under the curve = 0.809, respectively). An unsupervised hierarchical cluster analysis revealed a strong association between donor-reactive mBC with main fundamental allograft lesions associated with ABMR and conferred a significant deleterious impact on graft outcome. Monitoring donor-reactive mBC may be useful to further characterize humoral rejection after kidney transplantation.

Studying Brain Circuit Function with Dynamic Causal Modeling for Optogenetic fMRI.

Defining the large-scale behavior of brain circuits with cell type specificity is a major goal of neuroscience. However, neuronal circuit diagrams typically draw upon anatomical and electrophysiological measurements acquired in isolation. Consequently, a dynamic and cell-type-specific connectivity map has never been constructed from simultaneous measurements across the brain. Here, we introduce dynamic causal modeling (DCM) for optogenetic fMRI experiments-which uniquely allow cell-type-specific, brain-wide functional measurements-to parameterize the causal relationships among regions of a distributed brain network with cell type specificity. Strikingly, when applied to the brain-wide basal ganglia-thalamocortical network, DCM accurately reproduced the empirically observed time series, and the strongest connections were key connections of optogenetically stimulated pathways. We predict that quantitative and cell-type-specific descriptions of dynamic connectivity, as illustrated here, will empower novel systems-level understanding of neuronal circuit dynamics and facilitate the design of more effective neuromodulation therapies.

Comparison of fMRI analysis methods for heterogeneous BOLD responses in block design studies.

A large number of fMRI studies have shown that the temporal dynamics of evoked BOLD responses can be highly heterogeneous. Failing to model heterogeneous responses in statistical analysis can lead to significant errors in signal detection and characterization and alter the neurobiological interpretation. However, to date it is not clear that, out of a large number of options, which methods are robust against variability in the temporal dynamics of BOLD responses in block-design studies. Here, we used rodent optogenetic fMRI data with heterogeneous BOLD responses and simulations guided by experimental data as a means to investigate different analysis methods' performance against heterogeneous BOLD responses. Evaluations are carried out within the general linear model (GLM) framework and consist of standard basis sets as well as independent component analysis (ICA). Analyses show that, in the presence of heterogeneous BOLD responses, conventionally used GLM with a canonical basis set leads to considerable errors in the detection and characterization of BOLD responses. Our results suggest that the 3rd and 4th order gamma basis sets, the 7th to 9th order finite impulse response (FIR) basis sets, the 5th to 9th order B-spline basis sets, and the 2nd to 5th order Fourier basis sets are optimal for good balance between detection and characterization, while the 1st order Fourier basis set (coherence analysis) used in our earlier studies show good detection capability. ICA has mostly good detection and characterization capabilities, but detects a large volume of spurious activation with the control fMRI data.

Activation of Direct and Indirect Pathway Medium Spiny Neurons Drives Distinct Brain-wide Responses.

A central theory of basal ganglia function is that striatal neurons expressing the D1 and D2 dopamine receptors exert opposing brain-wide influences. However, the causal influence of each population has never been measured at the whole-brain scale. Here, we selectively stimulated D1 or D2 receptor-expressing neurons while visualizing whole-brain activity with fMRI. Excitation of either inhibitory population evoked robust positive BOLD signals within striatum, while downstream regions exhibited significantly different and generally opposing responses consistent with-though not easily predicted from-contemporary models of basal ganglia function. Importantly, positive and negative signals within the striatum, thalamus, GPi, and STN were all associated with increases and decreases in single-unit activity, respectively. These findings provide direct evidence for the opposing influence of D1 and D2 receptor-expressing striatal neurons on brain-wide circuitry and extend the interpretability of fMRI studies by defining cell-type-specific contributions to the BOLD signal.

Analyzing task-dependent brain network changes by whole-brain psychophysiological interactions: a comparison to conventional analysis.

While fMRI activation studies contrasting task conditions regularly assess the whole brain, this is usually not true for studies analyzing task-dependent brain connectivity changes by psychophysiological interactions (PPI). Here we combine standard PPI (sPPI) and generalized PPI (gPPI) with a priori brain parcellation by spatially constrained normalized cut spectral clustering (NCUT) to analyze task-dependent connectivity changes in a whole brain manner, and compare the results to multiseed conventional PPI analyses over all activation peaks in an episodic memory recall task. We show that, depending on the chosen parcellation frame, the whole-brain PPI approach is able to detect a large amount of the information that is detected by the conventional approach. Over and above, whole-brain PPI allows identification of several additional task-modulated connections, particularly from seed regions without significant activation differences between conditions.

Simultaneous EEG and fMRI reveals a causally connected subcortical-cortical network during reward anticipation.

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) have been used to study the neural correlates of reward anticipation, but the interrelation of EEG and fMRI measures remains unknown. The goal of the present study was to investigate this relationship in response to a well established reward anticipation paradigm using simultaneous EEG-fMRI recording in healthy human subjects. Analysis of causal interactions between the thalamus (THAL), ventral-striatum (VS), and supplementary motor area (SMA), using both mediator analysis and dynamic causal modeling, revealed that (1) THAL fMRI blood oxygenation level-dependent (BOLD) activity is mediating intermodal correlations between the EEG contingent negative variation (CNV) signal and the fMRI BOLD signal in SMA and VS, (2) the underlying causal connectivity network consists of top-down regulation from SMA to VS and SMA to THAL along with an excitatory information flow through a THAL→VS→SMA route during reward anticipation, and (3) the EEG CNV signal is best predicted by a combination of THAL fMRI BOLD response and strength of top-down regulation from SMA to VS and SMA to THAL. Collectively, these findings represent a likely neurobiological mechanism mapping a primarily subcortical process, i.e., reward anticipation, onto a cortical signature.