Presymptomatic cognitive and neuroanatomical changes in genetic frontotemporal dementia in the Genetic Frontotemporal dementia Initiative (GENFI) study: A cross-sectional analysis

Background: Frontotemporal dementia is a highly heritable neurodegenerative disorder. In about a third of patients, the disease is caused by autosomal dominant genetic mutations usually in one of three genes: progranulin (. GRN), microtubule-associated protein tau (. MAPT), or chromosome 9 open reading frame 72 (. C9orf72). Findings from studies of other genetic dementias have shown neuroimaging and cognitive changes before symptoms onset, and we aimed to identify whether such changes could be shown in frontotemporal dementia. Methods: We recruited participants to this multicentre study who either were known carriers of a pathogenic mutation in GRN, MAPT, or C9orf72, or were at risk of carrying a mutation because a first-degree relative was a known symptomatic carrier. We calculated time to expected onset as the difference between age at assessment and mean age at onset within the family. Participants underwent a standardised clinical assessment and neuropsychological battery. We did MRI and generated cortical and subcortical volumes using a parcellation of the volumetric T1-weighted scan. We used linear mixed-effects models to examine whether the association of neuropsychology and imaging measures with time to expected onset of symptoms differed between mutation carriers and non-carriers. Findings: Between Jan 30, 2012, and Sept 15, 2013, we recruited participants from 11 research sites in the UK, Italy, the Netherlands, Sweden, and Canada. We analysed data from 220 participants: 118 mutation carriers (40 symptomatic and 78 asymptomatic) and 102 non-carriers. For neuropsychology measures, we noted the earliest significant differences between mutation carriers and non-carriers 5 years before expected onset, when differences were significant for all measures except for tests of immediate recall and verbal fluency. We noted the largest Z score differences between carriers and non-carriers 5 years before expected onset in tests of naming (Boston Naming Test -0·7; SE 0·3) and executive function (Trail Making Test Part B, Digit Span backwards, and Digit Symbol Task, all -0·5, SE 0·2). For imaging measures, we noted differences earliest for the insula (at 10 years before expected symptom onset, mean volume as a percentage of total intracranial volume was 0·80% in mutation carriers and 0·84% in non-carriers; difference -0·04, SE 0·02) followed by the temporal lobe (at 10 years before expected symptom onset, mean volume as a percentage of total intracranial volume 8·1% in mutation carriers and 8·3% in non-carriers; difference -0·2, SE 0·1). Interpretation: Structural imaging and cognitive changes can be identified 5-10 years before expected onset of symptoms in asymptomatic adults at risk of genetic frontotemporal dementia. These findings could help to define biomarkers that can stage presymptomatic disease and track disease progression, which will be important for future therapeutic trials. Funding: Centres of Excellence in Neurodegenerati

Spatiotemporal analysis for detection of pre-symptomatic shape changes in neurodegenerative diseases : initial application to the GENFI cohort

Brain atrophy as measured from structural MR images, is one of the primary imaging biomarkers used to track neurodegenerative disease progression. In diseases such as frontotemporal dementia or Alzheimer's disease, atrophy can be observed in key brain structures years before any clinical symptoms are present. Atrophy is most commonly captured as volume change of key structures and the shape changes of these structures are typically not analysed despite being potentially more sensitive than summary volume statistics over the entire structure. In this paper we propose a spatiotemporal analysis pipeline based on Large Diffeomorphic Deformation Metric Mapping (LDDMM) to detect shape changes from volumetric MRI scans. We applied our framework to a cohort of individuals with genetic variants of frontotemporal dementia and healthy controls from the Genetic FTD Initiative (GENFI) study. Our method, take full advantage of the LDDMM framework, and relies on the creation of a population specific average spatiotemporal trajectory of a relevant brain structure of interest, the thalamus in our case. The residuals from each patient data to the average spatiotemporal trajectory are then clustered and studied to assess when presymptomatic mutation carriers differ from healthy control subjects. We found statistical differences in shape in the anterior region of the thalamus at least five years before the mutation carrier subjects develop any clinical symptoms. This region of the thalamus has been shown to be predominantly connected to the frontal lobe, consistent with the pattern of cortical atrophy seen in the disease

Spatiotemporal analysis for detection of pre-symptomatic shape changes in neurodegenerative diseases: Initial application to the GENFI cohort

Brain atrophy as measured from structural MR images, is one of the primary imaging biomarkers used to track neurodegenerative disease progression. In diseases such as frontotemporal dementia or Alzheimer's disease, atrophy can be observed in key brain structures years before any clinical symptoms are present. Atrophy is most commonly captured as volume change of key structures and the shape changes of these structures are typically not analysed despite being potentially more sensitive than summary volume statistics over the entire structure. In this paper we propose a spatiotemporal analysis pipeline based on Large Diffeomorphic Deformation Metric Mapping (LDDMM) to detect shape changes from volumetric MRI scans. We applied our framework to a cohort of individuals with genetic variants of frontotemporal dementia and healthy controls from the Genetic FTD Initiative (GENFI) study. Our method, take full advantage of the LDDMM framework, and relies on the creation of a population specific average spatiotemporal trajectory of a relevant brain structure of interest, the thalamus in our case. The residuals from each patient data to the average spatiotemporal trajectory are then clustered and studied to assess when presymptomatic mutation carriers differ from healthy control subjects. We found statistical differences in shape in the anterior region of the thalamus at least five years before the mutation carrier subjects develop any clinical symptoms. This region of the thalamus has been shown to be predominantly connected to the frontal lobe, consistent with the pattern of cortical atrophy seen in the disease

Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference

The heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include multiple phenotypes on distinct disease trajectories. Here we introduce a machine-learning technique\u2014Subtype and Stage Inference (SuStaIn)\u2014able to uncover data-driven disease phenotypes with distinct temporal progression patterns, from widely available cross-sectional patient studies. Results from imaging studies in two neurodegenerative diseases reveal subgroups and their distinct trajectories of regional neurodegeneration. In genetic frontotemporal dementia, SuStaIn identifies genotypes from imaging alone, validating its ability to identify subtypes; further the technique reveals within-genotype heterogeneity. In Alzheimer\u2019s disease, SuStaIn uncovers three subtypes, uniquely characterising their temporal complexity. SuStaIn provides fine-grained patient stratification, which substantially enhances the ability to predict conversion between diagnostic categories over standard models that ignore subtype (p = 7.18 7 10 124 ) or temporal stage (p = 3.96 7 10 125 ). SuStaIn offers new promise for enabling disease subtype discovery and precision medicine

Kirkiin: A New Toxic Type 2 Ribosome-Inactivating Protein from the Caudex of Adenia kirkii

Ribosome-inactivating proteins (RIPs) are plant toxins that irreversibly damage ribosomes and other substrates, thus causing cell death. RIPs are classified in type 1 RIPs, single-chain enzymatic proteins, and type 2 RIPs, consisting of active A chains, similar to type 1 RIPs, linked to lectin B chains, which enable the rapid internalization of the toxin into the cell. For this reason, many type 2 RIPs are very cytotoxic, ricin, volkensin and stenodactylin being the most toxic ones. From the caudex of Adenia kirkii (Mast.) Engl., a new type 2 RIP, named kirkiin, was purified by affinity chromatography on acid-treated Sepharose CL-6B and gel filtration. The lectin, with molecular weight of about 58 kDa, agglutinated erythrocytes and inhibited protein synthesis in a cell-free system at very low concentrations. Moreover, kirkiin was able to depurinate mammalian and yeast ribosomes, but it showed little or no activity on other nucleotide substrates. In neuroblastoma cells, kirkiin inhibited protein synthesis and induced apoptosis at doses in the pM range. The biological characteristics of kirkiin make this protein a potential candidate for several experimental pharmacological applications both alone for local treatments and as component of immunoconjugates for systemic targeting in neurodegenerative studies and cancer therapy

Primary Sequence and 3D Structure Prediction of the Plant Toxin Stenodactylin

Stenodactylin is one of the most potent type 2 ribosome-inactivating proteins (RIPs); its high toxicity has been demonstrated in several models both in vitro and in vivo. Due to its peculiarities, stenodactylin could have several medical and biotechnological applications in neuroscience and cancer treatment. In this work, we report the complete amino acid sequence of stenodactylin and 3D structure prediction. The comparison between the primary sequence of stenodactylin and other RIPs allowed us to identify homologies/differences and the amino acids involved in RIP toxic activity. Stenodactylin RNA was isolated from plant caudex, reverse transcribed through PCR and the cDNA was amplificated and cloned into a plasmid vector and further analyzed by sequencing. Nucleotide sequence analysis showed that stenodactylin A and B chains contain 251 and 258 amino acids, respectively. The key amino acids of the active site described for ricin and most other RIPs are also conserved in the stenodactylin A chain. Stenodactylin amino acid sequence shows a high identity degree with volkensin (81.7% for A chain, 90.3% for B chain), whilst when compared with other type 2 RIPs the identity degree ranges from 27.7 to 33.0% for the A chain and from 42.1 to 47.7% for the B chain

Ribosome-inactivating proteins in edible plants and purification and characterization of a new ribosome-inactivating protein from Cucurbita moschata

The basic protein fraction of tissue extracts from 40 edible plants inhibited cell-free protein synthesis and released adenine from herring sperm DNA, thus having adenine glycosylase activity. This suggested the presence of ribosome-inactivating proteins (RIPs) in the plant extracts. This indication was further strengthened by the presence of the two activities after a partial chromatographic purification of three extracts, including that from Lycopersicon esculentum (tomato), which had very low activity. From the extract of Cucurbita moschata (pumpkin), the most active one, a glycoprotein of 30,665 Da was purified which had the properties of a RIP, in that (i) it inhibited protein synthesis by a rabbit reticulocyte lysate with IC50 (concentration giving 50% inhibition) 0.035 nM (1.08 ng ml-1) and by HeLa, HT29 and JM cells with IC50 in the 100 nM range, (ii) deadenylated hsDNA and other polynucleotidic substrates, and (iii) depurinated yeast rRNA at a concentration of 0.1 ng ml-1, all values being comparable to those of other RIPs. The C. moschata RIP gave a weak cross-reaction only with an antiserum against dianthin 32, but not with antisera against other RIPs, and had superoxide dismutase, antifungal and antibacterial activities. © 2006 Elsevier B.V. All rights reserved

Predictors of taste acuity in healthy older Europeans

This study aimed to identify factors associated with taste acuity in healthy older European adults aged 55-87 years, employing a factorial independent design to recruit older adults from centres in France, Italy and United Kingdom. Adults aged 70-87 years (N=387) were recruited in Rome (Italy) (n=108) and Grenoble (France) (n=91) and aged 55-70 years in Northern Ireland (United Kingdom) (n=93) and Clermont-Ferrand (C-F) (France) (n=95). A signal detection theory (SDT) approach was used for detection threshold assessment of the four basic tastes (salt; sweet; bitter; and, sour). Trial data were converted to R-indices. Diet was assessed by means of four day food diaries. Dietary data were converted using WISP and then reduced, using a principal components analysis, to four components: Component 1 'high fat and salt'; Component 2 'high vitamins and fibre'; Component 3 'high fat and carbohydrate'; and, Component 4 'high trace elements'. Socio-demographic information was collected by self report survey. Four separate regression analyses were carried out, one for each of the four basic taste qualities (sweet; sour; bitter; salt). Mean ROC scores for each taste quality were the response variables and age, sex, country, social class and dietary components were predictor variables. The main predictors of taste acuity were age, sex, social class and country, which had differential effects for each taste quality. These data suggest that socio-demographic and cultural factors should be taken into account when considering taste acuity in older people