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•The first component of probabilistic principal component analysis can summarise patient states in a single score by capturing variability in Alzheimer’s Disease biomarkers from cohorts such as ADNI.•Per-patient scores are similar to realignment parameters in more complex disease progression models.•We conducted exhaustive experiments to verify the robustness of the computed score to different subsets of biomarkers or measurements missing at random.•Projections of individual trajectories can be refined as follow-up data becomes available, without retraining the entire model. The recent biological redefinition of Alzheimer’s Disease (AD) has spurred the development of statistical models that relate changes in biomarkers with neurodegeneration and worsening condition linked to AD. The ability to measure such changes may facilitate earlier diagnoses for affected individuals and help in monitoring the evolution of their condition. Amongst such statistical tools, disease progression models (DPMs) are quantitative, data-driven methods that specifically attempt to describe the temporal dynamics of biomarkers relevant to AD. Due to the heterogeneous nature of this disease, with patients of similar age experiencing different AD-related changes, a challenge facing longitudinal mixed-effects-based DPMs is the estimation of patient-realigning time-shifts. These time-shifts are indispensable for meaningful biomarker modelling, but may impact fitting time or vary with missing data in jointly estimated models. In this work, we estimate an individual’s progression through Alzheimer’s disease by combining multiple biomarkers into a single value using a probabilistic formulation of principal components analysis. Our results show that this variable, which summarises AD through observable biomarkers, is remarkably similar to jointly estimated time-shifts when we compute our scores for the baseline visit, on cross-sectional data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Reproducing the expected properties of clinical datasets, we confirm that estimated scores are robust to missing data or unavailable biomarkers. In addition to cross-sectional insights, we can model the latent variable as an individual progression score by repeating estimations at follow-up examinations and refining long-term estimates as more data is gathered, which would be ideal in a clinical setting. Finally, we verify that our score can be used as a pseudo-temporal scale instead of age to ignore some patient heterogeneity in cohort data and highlight the general trend in expected biomarker evolution in affected individuals.

Ubiquitous expression of mutant Cu/Zn-superoxide dismutase (SOD1) selectively affects motor neurons in the central nervous system (CNS), causing the adult-onset degenerative disease amyotrophic lateral sclerosis (ALS). The CNS-specific impact of ubiquitous mutant SOD1 expression is recapitulated in transgenic mouse models of the disease. Here we present outcomes for the metallo-complex Cu II (atsm) tested for therapeutic efficacy in mice expressing SOD1 G93A on a mixed genetic background. Oral administration of Cu II (atsm) delayed the onset of neurological symptoms, improved locomotive capacity and extended overall survival. Although the ALS-like phenotype of SOD1 G93A mice is instigated by expression of the mutant SOD1, we show the improved phenotype of the Cu II (atsm)-treated animals involves an increase in mature mutant SOD1 protein in the disease-affected spinal cord, where concomitant increases in copper and SOD1 activity are also evident. In contrast to these effects in the spinal cord, treating with Cu II (atsm) had no effect in liver on either mutant SOD1 protein levels or its activity, indicating a CNS-selective SOD1 response to the drug. These data provide support for Cu II (atsm) as a treatment option for ALS as well as insight to the CNS-selective effects of mutant SOD1.

Gamma-aminobutyric acid (GABA) is synthesized by two isoforms of the pyridoxal 5′-phosphate–dependent enzyme glutamic acid decarboxylase (GAD65 and GAD67). GAD67 is constitutively active and is responsible for basal GABA production. In contrast, GAD65, an autoantigen in type I diabetes, is transiently activated in response to the demand for extra GABA in neurotransmission, and cycles between an active holo form and an inactive apo form. We have determined the crystal structures of N-terminal truncations of both GAD isoforms. The structure of GAD67 shows a tethered loop covering the active site, providing a catalytic environment that sustains GABA production. In contrast, the same catalytic loop is inherently mobile in GAD65. Kinetic studies suggest that mobility in the catalytic loop promotes a side reaction that results in cofactor release and GAD65 autoinactivation. These data reveal the molecular basis for regulation of GABA homeostasis.

Brain iron elevation is implicated in Alzheimer’s disease (AD) pathogenesis, but the impact of iron on disease outcomes has not been previously explored in a longitudinal study. Ferritin is the major iron storage protein of the body; by using cerebrospinal fluid (CSF) levels of ferritin as an index, we explored whether brain iron status impacts longitudinal outcomes in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. We show that baseline CSF ferritin levels were negatively associated with cognitive performance over 7 years in 91 cognitively normal, 144 mild cognitive impairment (MCI) and 67 AD subjects, and predicted MCI conversion to AD. Ferritin was strongly associated with CSF apolipoprotein E levels and was elevated by the Alzheimer’s risk allele, APOE-ɛ4 . These findings reveal that elevated brain iron adversely impacts on AD progression, and introduce brain iron elevation as a possible mechanism for APOE-ɛ4 being the major genetic risk factor for AD. Brain-iron elevation is implicated in Alzheimer’s disease (AD), but the impact of the metal on disease outcomes has not been analysed in a longitudinal study. Here, the authors examine the association between the levels of ferritin, an iron storage protein, in the cerebrospinal fluid (CSF) of AD patients and show that CSF ferritin levels predict AD outcomes.

It is increasingly recognized that Alzheimer's disease (AD) exists before dementia is present and that shifts in amyloid beta occur long before clinical symptoms can be detected. Early detection of these molecular changes is a key aspect for the success of interventions aimed at slowing down rates of cognitive decline. Recent evidence indicates that of the two established methods for measuring amyloid, a decrease in cerebrospinal fluid (CSF) amyloid β (Aβ ) may be an earlier indicator of Alzheimer's disease risk than measures of amyloid obtained from Positron Emission Tomography (PET). However, CSF collection is highly invasive and expensive. In contrast, blood collection is routinely performed, minimally invasive and cheap. In this work, we develop a blood-based signature that can provide a cheap and minimally invasive estimation of an individual's CSF amyloid status using a machine learning approach. We show that a Random Forest model derived from plasma analytes can accurately predict subjects as having abnormal (low) CSF Aβ levels indicative of AD risk (0.84 AUC, 0.78 sensitivity, and 0.73 specificity). Refinement of the modeling indicates that only APOEε4 carrier status and four plasma analytes (CGA, Aβ , Eotaxin 3, APOE) are required to achieve a high level of accuracy. Furthermore, we show across an independent validation cohort that individuals with predicted abnormal CSF Aβ levels transitioned to an AD diagnosis over 120 months significantly faster than those with predicted normal CSF Aβ levels and that the resulting model also validates reasonably across PET Aβ status (0.78 AUC). This is the first study to show that a machine learning approach, using plasma protein levels, age and APOEε4 carrier status, is able to predict CSF Aβ status, the earliest risk indicator for AD, with high accuracy.

Proteins containing membrane attack complex/perforin (MACPF) domains play important roles in vertebrate immunity, embryonic development, and neural-cell migration. In vertebrates, the ninth component of complement and perforin form oligomeric pores that lyse bacteria and kill virus-infected cells, respectively. However, the mechanism of MACPF function is unknown. We determined the crystal structure of a bacterial MACPF protein, Plu-MACPF from Photorhabdus luminescens, to 2.0 angstrom resolution. The MACPF domain reveals structural similarity with poreforming cholesterol-dependent cytolysins (CDCs) from Gram-positive bacteria. This suggests that lytic MACPF proteins may use a CDC-like mechanism to form pores and disrupt cell membranes. Sequence similarity between bacterial and vertebrate MACPF domains suggests that the fold of the CDCs, a family of proteins important for bacterial pathogenesis, is probably used by vertebrates for defense against infection.

It is increasingly recognized that Alzheimer’s disease (AD) exists before dementia is present and that shifts in amyloid beta occur long before clinical symptoms can be detected. Early detection of these molecular changes is a key aspect for the success of interventions aimed at slowing down rates of cognitive decline. Recent evidence indicates that of the two established methods for measuring amyloid, a decrease in cerebrospinal fluid (CSF) amyloid β 1−42 (A β 1−42 ) may be an earlier indicator of Alzheimer’s disease risk than measures of amyloid obtained from Positron Emission Tomography (PET). However, CSF collection is highly invasive and expensive. In contrast, blood collection is routinely performed, minimally invasive and cheap. In this work, we develop a blood-based signature that can provide a cheap and minimally invasive estimation of an individual’s CSF amyloid status using a machine learning approach. We show that a Random Forest model derived from plasma analytes can accurately predict subjects as having abnormal (low) CSF A β 1−42 levels indicative of AD risk (0.84 AUC, 0.78 sensitivity, and 0.73 specificity). Refinement of the modeling indicates that only APOEε4 carrier status and four plasma analytes (CGA, A β 1−42 , Eotaxin 3, APOE) are required to achieve a high level of accuracy. Furthermore, we show across an independent validation cohort that individuals with predicted abnormal CSF A β 1−42 levels transitioned to an AD diagnosis over 120 months significantly faster than those with predicted normal CSF A β 1−42 levels and that the resulting model also validates reasonably across PET A β 1−42 status (0.78 AUC). This is the first study to show that a machine learning approach, using plasma protein levels, age and APOEε4 carrier status, is able to predict CSF A β 1−42 status, the earliest risk indicator for AD, with high accuracy.

Ubiquitous expression of mutant Cu/Zn-superoxide dismutase (SOD1) selectively affects motor neurons in the central nervous system (CNS), causing the adult-onset degenerative disease amyotrophic lateral sclerosis (ALS). The CNS-specific impact of ubiquitous mutant SOD1 expression is recapitulated in transgenic mouse models of the disease. Here we present outcomes for the metallo-complex Cu (atsm) tested for therapeutic efficacy in mice expressing SOD1 on a mixed genetic background. Oral administration of Cu (atsm) delayed the onset of neurological symptoms, improved locomotive capacity and extended overall survival. Although the ALS-like phenotype of SOD1 mice is instigated by expression of the mutant SOD1, we show the improved phenotype of the Cu (atsm)-treated animals involves an increase in mature mutant SOD1 protein in the disease-affected spinal cord, where concomitant increases in copper and SOD1 activity are also evident. In contrast to these effects in the spinal cord, treating with Cu (atsm) had no effect in liver on either mutant SOD1 protein levels or its activity, indicating a CNS-selective SOD1 response to the drug. These data provide support for Cu (atsm) as a treatment option for ALS as well as insight to the CNS-selective effects of mutant SOD1.

It is increasingly recognized that Alzheimer’s disease (AD) exists before dementia is present and that shifts in amyloid beta occur long before clinical symptoms can be detected. Early detection of these molecular changes is a key aspect for the success of interventions aimed at slowing down rates of cognitive decline. Recent evidence indicates that of the two established methods for measuring amyloid, a decrease in cerebrospinal fluid (CSF) amyloid β1–42 (Aβ1–42) may be an earlier indicator of Alzheimer’s disease risk than measures of amyloid obtained from Positron Emission Tomography (PET). However, CSF collection is highly invasive and expensive. In contrast, blood collection is routinely performed, minimally invasive and cheap. In this work, we develop a blood-based signature that can provide a cheap and minimally invasive estimation of an individual’s CSF amyloid status using a machine learning approach. We show that a Random Forest model derived from plasma analytes can accurately predict subjects as having abnormal (low) CSF Aβ1–42 levels indicative of AD risk (0.84 AUC, 0.78 sensitivity, and 0.73 specificity). Refinement of the modeling indicates that only APOEε4 carrier status and four plasma analytes (CGA, Aβ1–42, Eotaxin 3, APOE) are required to achieve a high level of accuracy. Furthermore, we show across an independent validation cohort that individuals with predicted abnormal CSF Aβ1–42 levels transitioned to an AD diagnosis over 120 months significantly faster than those with predicted normal CSF Aβ1–42levels and that the resulting model also validates reasonably across PET Aβ1-42 status (0.78 AUC). This is the first study to show that a machine learning approach, using plasma protein levels, age and APOEε4 carrier status, is able to predict CSF Aβ1–42 status, the earliest risk indicator for AD, with high accuracy.

It is increasingly recognized that Alzheimer's disease (AD) exists before dementia is present and that shifts in amyloid beta occur long before clinical symptoms can be detected. Early detection of these molecular changes is a key aspect for the success of interventions aimed at slowing down rates of cognitive decline. Recent evidence indicates that of the two established methods for measuring amyloid, a decrease in cerebral spinal fluid (CSF) amyloid 1-42 (A 1-42) may be an earlier indicator of Alzheimer's disease risk than measures of amyloid obtained from Positron Emission Topography (PET). However, CSF collection is highly invasive and expensive. In contrast, blood collection is routinely performed, minimally invasive and cheap. In this work, we develop a blood-based signature that can provide a cheap and minimally invasive estimation of an individual's CSF amyloid status using a machine learning approach. We show that a Random Forest model derived from plasma analytes can accurately predict subjects as having abnormal (low) CSF A 1-42 levels indicative of AD risk (0.84 AUC, 0.78 sensitivity, and 0.73 specificity). Refinement of the modeling indicates that only APOE 4 carrier status and four analytes are required to achieve a high level of accuracy. Furthermore, we show across an independent validation cohort that individuals with predicted abnormal CSF A 1-42 levels transitioned to an AD diagnosis over 120 months significantly faster than those predicted with normal CSF A 1-42 levels and that the resulting model also performs reasonably across PET A 1-42 status. This is the first study to show that a machine learning approach, using plasma protein levels, age and APOE 4 carrier status, is able to predict CSF A 1-42 status, the earliest risk indicator for AD, with high accuracy.