ICT-based system to predict and prevent falls (iStoppFalls): results from an international multicenter randomized controlled trial

Background: Falls and fall-related injuries are a serious public health issue. Exercise programs can effectively reduce fall risk in older people. The iStoppFalls project developed an Information and Communication Technology-based system to deliver an unsupervised exercise program in older people’s homes. The primary aims of the iStoppFalls randomized controlled trial were to assess the feasibility (exercise adherence, acceptability and safety) of the intervention program and its effectiveness on common fall risk factors. Methods: A total of 153 community-dwelling people aged 65+ years took part in this international, multicentre, randomized controlled trial. Intervention group participants conducted the exercise program for 16 weeks, with a recommended duration of 120 min/week for balance exergames and 60 min/week for strength exercises. All intervention and control participants received educational material including advice on a healthy lifestyle and fall prevention. Assessments included physical and cognitive tests, and questionnaires for health, fear of falling, number of falls, quality of life and psychosocial outcomes. Results: The median total exercise duration was 11.7 h (IQR = 22.0) over the 16-week intervention period. There were no adverse events. Physiological fall risk (Physiological Profile Assessment, PPA) reduced significantly more in the intervention group compared to the control group (F1,127 = 4.54, p = 0.035). There was a significant three-way interaction for fall risk assessed by the PPA between the high-adherence (>90 min/week; n = 18, 25.4 %), low-adherence (n = 53, 74.6 %) and control group (F2,125 = 3.12, n = 75, p = 0.044). Post hoc analysis revealed a significantly larger effect in favour of the high-adherence group compared to the control group for fall risk (p = 0.031), postural sway (p = 0.046), stepping reaction time (p = 0.041), executive functioning (p = 0.044), and quality of life (p for trend = 0.052). Conclusions: The iStoppFalls exercise program reduced physiological fall risk in the study sample. Additional subgroup analyses revealed that intervention participants with better adherence also improved in postural sway, stepping reaction, and executive function

Monoubiquitination of Ancient Ubiquitous Protein 1 Promotes Lipid Droplet Clustering

<div><p>Lipid droplets, the intracellular storage organelles for neutral lipids, exist in a wide range of sizes and of morphologically distinct organization, from loosely dispersed lipid droplets to tightly packed lipid droplet clusters. We show that the lipid droplet protein AUP1 induces cluster formation. A fraction of AUP1 is monoubiquitinated at various lysine residues. This process depends on its internal CUE domain, which is a known ubiquitin-binding domain. AUP1 with a deleted or point mutagenized CUE domain, as well as a lysine-free mutant, are not ubiquitinated and do not induce lipid droplet clustering. When such ubiquitination deficient mutants are fused to ubiquitin, clustering is restored. AUP1 mutants with defective droplet targeting fail to induce clustering. Also, another lipid droplet protein, NSDHL, with a fused ubiquitin does not induce clustering. The data indicate that monoubiquitinated AUP1 on the lipid droplet surface specifically induces clustering, and suggest a homophilic interaction with a second AUP1 molecule or a heterophilic interaction with another ubiquitin-binding protein.</p></div

Knockdown of AUP1 causes declustering of LDs.

<p>A) A431 cells were either mock transfected or transfected with one of three different siRNAs against AUP1. Cells were lysed and proteins separated by SDS-PAGE and immunoblotted with anti-AUP1 antibody. GAPDH served as loading control. B) Fluorescence micrographs of mock transfected (control) or siRNA treated (siRNA3) A431 cells, both grown in medium supplemented with 50 µM oleate. Cells were immunostained with anti-AUP1 antibody (AUP1, left), and LD540 (LDs, middle panels). Merged images (right) show nuclei stained by DAPI in blue, AUP1 in red and LDs in green. Bars, 10 µm. C) Quantification of LD clustering in mock- (control) or siRNA- (as indicated) treated A431 cells. Results are displayed as average ± standard deviation of three independent experiments. For each individual experiment at least 25 cells were analyzed.</p

AUP1 overexpression causes LD clustering.

<p>A–C) COS7 cells were transfected with empty control vector (control) or different HA-tagged AUP1 constructs as indicated and grown in medium supplemented with 50 µM oleate. Cells were immunostained with anti-HA antibody (left), and LD540 (LDs, middle panels). Merged images (right) show nuclei stained by DAPI in blue, AUP1 in red and LDs in green. Bars, 10 µm. COS7 cells not expressing AUP1-HA do not show LD clustering (marked by asterisk (*)).</p

Possible mechanisms for AUP1 induced LD clustering.

<p>From left to right: Type 1: monoubiquitinated AUP1 dimerizes <i>in trans</i> with another (1A) ubiquitinated or (1B) non-ubiquitinated AUP1 by binding between the ubiquitin moieties and the CUE domains. Type 2: Monoubiquitinated AUP1 interacts <i>in trans</i> with another LD protein containing a ubiquitin-binding domain (UBD). Type 3: Interaction of type 1 or 2, but mediated by a soluble adaptor protein.</p

AUP1 is ubiquitinated.

<p>A) COS7 cells were transfected with His-ubiquitin and HA-tagged AUP1 constructs and controls (as indicated). His-ubiquitin and His-ubiquitin modified proteins were isolated from cell lysates using Ni-NTA agarose. Proteins from lysates (10% input, left panel) and His-purifications (right panel) were separated by SDS-PAGE and immunoblotted with anti-HA antibody. B, C) COS7 cells were transfected with His-ubiquitin and HA-tagged AUP1 constructs (as indicated). Samples were processed as under A). D) Quantification of LD clustering in COS7 cells overexpressing HA-tagged Lys to Arg mutation AUP1 constructs (as indicated). Results are displayed as average ± standard deviation of three independent experiments. For each individual experiment at least 25 cells were analyzed. E) COS7 cells were transfected with a construct expressing AUP1-10KR-HA and grown in medium supplemented with 50 µM oleate. Cells were immunostained with anti-HA antibody (left), and LD540 (LDs, middle panel). The merged image (right) shows nuclei stained by DAPI in blue, AUP1 in red and LDs in green. Bars, 10 µm.</p

AUP1 monoubiquitination is sufficient to promote LD clustering.

<p>A) COS7 cells were transfected with HA-tagged AUP1 mutation constructs (as indicated), fused to monoubiquitin and grown in medium supplemented with 50 µM oleate. Cells were immunostained with anti-HA antibody (left), and LD540 (LDs, middle panels). Merged images (right) show nuclei stained by DAPI in blue, AUP1 in red and LDs in green. Bars, 10 µm. B) COS7 cells were transfected with HA-tagged NSDHL with or without a fused monoubiquitin as indicated and grown in medium supplemented with 50 µM oleate. Cells were immunostained with anti-HA antibody (left), and LD540 (LDs, middle panels). Merged images (right) show nuclei stained by DAPI in blue, AUP1 in red and LDs in green. Bars, 10 µm. C) Quantification of LD clustering in COS7 cells overexpressing HA-tagged AUP1 fusion constructs (as indicated). Results are displayed as average ± standard deviation of three independent experiments. For each individual experiment at least 25 cells were analyzed.</p

The AUP1 CUE domain is important for LD clustering.

<p>A–C) COS7 cells were transfected with different HA-tagged AUP1 domain deletion or mutation constructs as indicated and grown in medium supplemented with 50 µM oleate. Cells were immunostained with anti-HA antibody (left), and LD540 (LDs, middle panels). Merged images (right) show nuclei stained by DAPI in blue, AUP1 in red and LDs in green. Bars, 10 µm. D) Quantification of LD clustering in COS7 cells overexpressing HA-tagged AUP1 constructs as indicated. Empty vector was used as control. Results are displayed as average ± standard deviation of three independent experiments. For each individual experiment at least 25 cells were analyzed. F) Expression levels of HA-tagged AUP1 constructs. Proteins from COS7 cells overexpressing HA-tagged AUP1 constructs as indicated were separated by SDS-PAGE and immunoblotted with anti-HA antibody. GAPDH served as loading control. Note: AUP1-ΔCUE-HA and AUP1-mutCUE3-HA migrated at an apparent molecular weight around five kDa higher than expected. E) Amino acid sequence of the AUP1 CUE domain and predicted relative position of the three α-helices after Prag <i>et al. </i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0072453#pone.0072453-Prag1" target="_blank">[34]</a>. Mutated amino acid residues of the three AUP1-mutCUE constructs used in this work are highlighted in grey.</p

Tracing Fatty Acid Metabolism by Click Chemistry

Fatty acids are abundant constituents of all biological systems, and their metabolism is important for normal function at all levels of an organism. Aberrations in fatty acid metabolism are associated with pathological states and have become a focus of current research, particularly due to the interest in metabolic overload diseases. Here we present a click-chemistry-based method that allows tracing of fatty acid metabolism in virtually any biological system. It combines high sensitivity with excellent linearity and fast sample turnover. Since it is free of radioactivity, it can be combined with any other modern analysis technology and can be used in high-throughput applications. Using the new method, we provide for the first time an analysis of cellular fatty metabolism with high time resolution and a comprehensive comparison of utilization of a broad spectrum of fatty acids in hepatoma and adipose cell lines