Background-Klotho is known to function as a cofactor for the phosphatonin, fibroblast growth factor (FGF)-23 at the kidney.FGF-23 levels rise in chronic kidney disease (CKD) despite progression of accelerated vascular calcification.

Context:α-Klotho has emerged as a powerful regulator of the aging process. To date, the expression profile of α-Klotho in human tissues is unknown, and its existence in some human tissue types is subject to much controversy.Objective:This is the first study to characterize systemwide tissue expression of transmembrane α-Klotho in humans. We have employed next-generation targeted proteomic analysis using parallel reaction monitoring in parallel with conventional antibody-based methods to determine the expression and spatial distribution of human α-Klotho expression in health.Results:The distribution of α-Klotho in human tissues from various organ systems, including arterial, epithelial, endocrine, reproductive, and neuronal tissues, was first identified by immunohistochemistry. Kidney tissues showed strong α-Klotho expression, whereas liver did not reveal a detectable signal. These results were next confirmed by Western blotting of both whole tissues and primary cells. To validate our antibody-based results, α-Klotho-expressing tissues were subjected to parallel reaction monitoring mass spectrometry (data deposited at ProteomeXchange, PXD002775) identifying peptides specific for the full-length, transmembrane α-Klotho isoform.Conclusions:The data presented confirm α-Klotho expression in the kidney tubule and in the artery and provide evidence of α-Klotho expression across organ systems and cell types that has not previously been described in humans.

When making perceptual decisions, humans have been shown to optimally integrate independent noisy multisensory information, matching maximum-likelihood (ML) limits. Such ML estimators provide a theoretic limit to perceptual precision (i.e., minimal thresholds). However, how the brain combines two interacting (i.e., not independent) sensory cues remains an open question. To study the precision achieved when combining interacting sensory signals, we measured perceptual roll tilt and roll rotation thresholds between 0 and 5 Hz in six normal human subjects. Primary results show that roll tilt thresholds between 0.2 and 0.5 Hz were significantly lower than predicted by a ML estimator that includes only vestibular contributions that do not interact. In this paper, we show how other cues (e.g., somatosensation) and an internal representation of sensory and body dynamics might independently contribute to the observed performance enhancement. In short, a Kalman filter was combined with an ML estimator to match human performance, whereas the potential contribution of nonvestibular cues was assessed using published bilateral loss patient data. Our results show that a Kalman filter model including previously proven canal-otolith interactions alone (without nonvestibular cues) can explain the observed performance enhancements as can a model that includes nonvestibular contributions. We found that human whole body self-motion direction-recognition thresholds measured during dynamic roll tilts were significantly lower than those predicted by a conventional maximum-likelihood weighting of the roll angular velocity and quasistatic roll tilt cues. Here, we show that two models can each match this "apparent" better-than-optimal performance: ) inclusion of a somatosensory contribution and) inclusion of a dynamic sensory interaction between canal and otolith cues via a Kalman filter model.