Disease progression modelling of Alzheimer's disease using probabilistic principal components analysis.

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.

Process mining-driven analysis of COVID-19's impact on vaccination patterns.

Process mining is a discipline sitting between data mining and process science, whose goal is to provide theoretical methods and software tools to analyse process execution data, known as event logs. Although process mining was originally conceived to facilitate business process management activities, research studies have shown the benefit of leveraging process mining in healthcare contexts. However, applying process mining tools to analyse healthcare process execution data is not straightforward. In this paper, we show a methodology to: i) prepare general practice healthcare process data for conducting a process mining analysis; ii) select and apply suitable process mining solutions for successfully executing the analysis; and iii) extract valuable insights from the obtained results, alongside leads for traditional data mining analysis. By doing so, we identified two major challenges when using process mining solutions for analysing healthcare process data, and highlighted benefits and limitations of the state-of-the-art process mining techniques when dealing with highly variable processes and large data-sets. While we provide solutions to the identified challenges, the overarching goal of this study was to detect differences between the patients' health services utilization pattern observed in 2020-during the COVID-19 pandemic and mandatory lock-downs -and the one observed in the prior four years, 2016 to 2019. By using a combination of process mining techniques and traditional data mining, we were able to demonstrate that vaccinations in Victoria did not drop drastically-as other interactions did. On the contrary, we observed a surge of influenza and pneumococcus vaccinations in 2020, as opposed to other research findings of similar studies conducted in different geographical areas.

Identification of Leukocyte Surface P2X7 as a Biomarker Associated with Alzheimer's Disease.

Alzheimer's disease (AD) has shown altered immune responses in the periphery. We studied P2X7 (a proinflammatory receptor and a scavenger receptor) and two integrins, CD11b and CD11c, on the surface of circulating leukocytes and analysed their associations with Aß-PET, brain atrophy, neuropsychological assessments, and cerebrospinal fluid (CSF) biomarkers. Total 287 age-matched, sex-balanced participants were recruited in a discovery cohort and two validation cohorts through the AIBL study and studied using tri-colour flow cytometry. Our results demonstrated reduced expressions of P2X7, CD11b, and CD11c on leukocytes, particularly monocytes, in Aß +ve cases compared with Aß -ve controls. P2X7 and integrin downregulation was observed at pre-clinical stage of AD and stayed low throughout disease course. We further constructed a polygenic risk score (PRS) model based on 12 P2RX7 risk alleles to assess the genetic impact on P2X7 function in AIBL and ADNI cohorts. No significant association was identified between the P2RX7 gene and AD, indicating that P2X7 downregulation in AD is likely caused by environmental changes rather than genetic factors. In conclusion, the downregulation of P2X7 and integrins at pre-clinical stage of AD indicates altered pro-inflammatory responses, phagocytic functions, and migrating capabilities of circulating monocytes in early AD pathogenesis. Our study not only improves our understanding of peripheral immune involvement in early stage of AD but also provides more insights into novel biomarker development, diagnosis, and prognosis of AD.

Changes in plasma amyloid beta in a longitudinal study of aging and Alzheimer's disease.

BACKGROUND: A practical biomarker is required to facilitate the preclinical diagnosis of Alzheimer's disease (AD). METHODS: Plasma amyloid beta (Aß)1-40, Aß1-42, Aßn-40, and Aßn-42 peptides were measured at baseline and after 18 months in 771 participants from the Australian Imaging Biomarkers and Lifestyle (AIBL) study of aging. Aß peptide levels were compared with clinical pathology, neuroimaging and neuropsychological measurements. RESULTS: Although inflammatory and renal function covariates influenced plasma Aß levels significantly, a decrease in Aß1-42/Aß1-40 was observed in patients with AD, and was also inversely correlated with neocortical amyloid burden. During the 18 months, plasma Aß1-42 decreased in subjects with mild cognitive impairment (MCI) and in those transitioning from healthy to MCI. CONCLUSION: Our findings are consistent with a number of published plasma Aß studies and, although the prognostic value of individual measures in any given subject is limited, the diagnostic contribution of plasma Aß may demonstrate utility when combined with a panel of peripheral biomarkers.

Stemformatics data portal enables transcriptional benchmarking of lab-derived myeloid cells.

Stemformatics.org has been serving the stem cell research community for over a decade, by making it easy for users to find and view transcriptional profiles of pluripotent and adult stem cells and their progeny, comparing data derived from multiple tissues and derivation methods. In recent years, Stemformatics has shifted its focus from curation to collation and integration of public data with shared phenotypes. It now hosts several integrated expression atlases based on human myeloid cells, which allow for easy cross-dataset comparisons and discovery of emerging cell subsets and activation properties. The atlases are designed for external users to benchmark their own data against a common reference. Here, we use case studies to illustrate how to find and explore previously published datasets of relevance and how in-vitro-derived cells can be transcriptionally matched to cells in the integrated atlas to highlight phenotypes of interest.

An evaluation of existing text de-identification tools for use with patient progress notes from Australian general practice.

INTRODUCTION: Digitized patient progress notes from general practice represent a significant resource for clinical and public health research but cannot feasibly and ethically be used for these purposes without automated de-identification. Internationally, several open-source natural language processing tools have been developed, however, given wide variations in clinical documentation practices, these cannot be utilized without appropriate review. We evaluated the performance of four de-identification tools and assessed their suitability for customization to Australian general practice progress notes. METHODS: Four tools were selected: three rule-based (HMS Scrubber, MIT De-id, Philter) and one machine learning (MIST). 300 patient progress notes from three general practice clinics were manually annotated with personally identifying information. We conducted a pairwise comparison between the manual annotations and patient identifiers automatically detected by each tool, measuring recall (sensitivity), precision (positive predictive value), f1-score (harmonic mean of precision and recall), and f2-score (weighs recall 2x higher than precision). Error analysis was also conducted to better understand each tool's structure and performance. RESULTS: Manual annotation detected 701 identifiers in seven categories. The rule-based tools detected identifiers in six categories and MIST in three. Philter achieved the highest aggregate recall (67%) and the highest recall for NAME (87%). HMS Scrubber achieved the highest recall for DATE (94%) and all tools performed poorly on LOCATION. MIST achieved the highest precision for NAME and DATE while also achieving similar recall to the rule-based tools for DATE and highest recall for LOCATION. Philter had the lowest aggregate precision (37%), however preliminary adjustments of its rules and dictionaries showed a substantial reduction in false positives. CONCLUSION: Existing off-the-shelf solutions for automated de-identification of clinical text are not immediately suitable for our context without modification. Philter is the most promising candidate due to its high recall and flexibility however will require extensive revising of its pattern matching rules and dictionaries.

GABA production by glutamic acid decarboxylase is regulated by a dynamic catalytic loop.

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.

Single amino acid and trinucleotide repeats: function and evolution.

The most well known effect of single amino acid repeat expansion, beyond a certain threshold, is the development of a specific disease, depending on the protein in which the expansion has occurred. For example, the expansion of the glutamine repeat in huntingtin leads to the debilitating neurodegenerative disease, Huntington's disease. Similarly, there are a range of other disorders caused by trinucleotide repeat expansions encoding polyglutamine or polyalanine tracts. The age of onset of the polyglutamine-induced neurodegenerative diseases is usually negatively correlated with the length of expanded CAG/glutamine repeat. However, recent studies have given evidence that single amino acid repeats may also play critical roles in normal protein function and that changes in the length of single amino acid repeats is likely to play a beneficial role in evolution. This chapter will look at the prevalence, function and possible role single amino acid repeats have in evolution and other biological processes.

"Escalibur"-A practical pipeline for the de novo analysis of nucleotide variation in nonmodel eukaryotes.

The revolution in genomics has enabled large-scale population genetic investigations of a wide range of organisms, but there has been a relatively limited focus on improving analytical pipelines. To efficiently analyse large data sets, highly integrated and automated software pipelines, which are easy to use, efficient, reliable, reproducible and run in multiple computational environments, are required. A number of software workflows have been developed to handle and process such data sets for population genetic analyses, but effective, specialized pipelines for genetic and statistical analyses of nonmodel organisms are lacking. For most species, resources for variomes (sets of genetic variations found in populations of species) are not available, and/or genome assemblies are often incomplete and fragmented, complicating the selection of the most suitable reference genome when multiple assemblies are available. Additionally, the biological samples used often contain extraneous DNA from sources other than the species under investigation (e.g., microbial contamination), which needs to be removed prior to genetic analyses. For these reasons, we established a new pipeline, called Escalibur, which includes: functionalities, such as data trimming and mapping; selection of a suitable reference genome; removal of contaminating read data; recalibration of base calls; and variant-calling. Escalibur uses a proven gatk variant caller and workflow description language (WDL), and is, therefore, a highly efficient and scalable pipeline for the genome-wide identification of nucleotide variation in eukaryotes. This pipeline is available at https://gitlab.unimelb.edu.au/bioscience/escalibur (version 0.3-beta) and is essentially applicable to any prokaryote or eukaryote.

Blood-borne amyloid-beta dimer correlates with clinical markers of Alzheimer's disease.

Alzheimer's disease (AD) is the most common age-related dementia. Unfortunately due to a lack of validated biomarkers definitive diagnosis relies on the histological demonstration of amyloid-beta (Abeta) plaques and tau neurofibrillary tangles. Abeta processing is implicated in AD progression and many therapeutic strategies target various aspects of this biology. While Abeta deposition is the most prominent feature of AD, oligomeric forms of Abeta have been implicated as the toxic species inducing the neuronal dysfunction. Currently there are no methods allowing routine monitoring of levels of such species in living populations. We have used surface enhanced laser desorption ionization time of flight (SELDI-TOF) mass spectrometry incorporating antibody capture to investigate whether the cellular membrane-containing fraction of blood provides a new source of biomarkers. There are significant differences in the mass spectra profiles of AD compared with HC subjects, with significantly higher levels of Abeta monomer and dimer in the blood of AD subjects. Furthermore, levels of these species correlated with clinical markers of AD including brain Abeta burden, cognitive impairment and brain atrophy. These results indicate that fundamental biochemical events relevant to AD can be monitored in blood, and that the species detected may be useful clinical biomarkers for AD.

Effects of anticholinergic drugs on cognitive function in older Australians: results from the AIBL study.

BACKGROUND/AIMS: The nature and extent of adverse cognitive effects due to the prescription of anticholinergic drugs in older people with and without dementia is unclear. METHODS: We calculated the anticholinergic load (ACL) of medications taken by participants of the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of ageing, a cohort of 211 Alzheimer's disease (AD) patients, 133 mild cognitive impairment (MCI) patients and 768 healthy controls (HC) all aged over 60 years. The association between ACL and cognitive function was examined for each diagnostic group (HC, MCI, AD). RESULTS: A high ACL within the HC group was associated with significantly slower response speeds for the Stroop color and incongruent trials. No other significant relationships between ACL and cognition were noted. CONCLUSION: In this large cohort, prescribed anticholinergic drugs appeared to have modest effects upon psychomotor speed and executive function, but not on other areas of cognition in healthy older adults.

Larger temporal volume in elderly with high versus low beta-amyloid deposition.

ß-Amyloid deposition is one of the main hallmarks of Alzheimer's disease thought to eventually cause neuronal death. Post-mortem and neuroimaging studies have consistently reported cases with documented normal cognition despite high ß-amyloid burden. It is of great interest to understand what differentiates these particular subjects from those without ß-amyloid deposition or with both ß-amyloid deposition and cognitive deficits, i.e. what allows these subjects to resist the damage of the pathological lesions. [¹¹C]Pittsburgh compound B positron emission tomography and magnetic resonance brain scans were obtained in 149 participants including healthy controls and patients with subjective cognitive impairment, mild cognitive impairment and Alzheimer's disease. Magnetic resonance data were compared between high versus low-[11C]Pittsburgh compound B cases, and between high-[¹¹C]Pittsburgh compound B cases with versus those without cognitive deficits. Larger temporal (including hippocampal) grey matter volume, associated with better episodic memory performance, was found in high- versus low-[¹¹C]Pittsburgh compound B healthy controls. The same finding was obtained using different [¹¹C]Pittsburgh compound B thresholds, correcting [¹¹C]Pittsburgh compound B data for partial averaging, using age, education, Mini-Mental State Examination, apolipoprotein E4 and sex-matched subsamples, and using manual hippocampal delineation instead of voxel-based analysis. By contrast, in participants with subjective cognitive impairment, significant grey matter atrophy was found in high-[¹¹C]Pittsburgh compound B cases compared to low-[¹¹C]Pittsburgh compound B cases, as well as in high-[¹¹C]Pittsburgh compound B cases with subjective cognitive impairment, mild cognitive impairment and Alzheimer's disease compared to high-[¹¹C]Pittsburgh compound B healthy controls. Larger grey matter volume in high-[¹¹C]Pittsburgh compound B healthy controls may reflect either a tissue reactive response to ß-amyloid or a combination of higher 'brain reserve' and under-representation of subjects with standard/low temporal volume in the high-[¹¹C]Pittsburgh compound B healthy controls. Our complementary analyses tend to support the latter hypotheses. Overall, our findings suggest that the deleterious effects of ß-amyloid on cognition may be delayed in those subjects with larger brain (temporal) volume.

A domain level interaction network of amyloid precursor protein and Abeta of Alzheimer's disease.

The primary constituent of the amyloid plaque, beta-amyloid (Abeta), is thought to be the causal "toxic moiety" of Alzheimer's disease. However, despite much work focused on both Abeta and its parent protein, amyloid precursor protein (APP), the functional roles of APP and its cleavage products remain to be fully elucidated. Protein-protein interaction networks can provide insight into protein function, however, high-throughput data often report false positives and are in frequent disagreement with low-throughput experiments. Moreover, the complexity of the CNS is likely to be under represented in such databases. Therefore, we curated the published work characterizing both APP and Abeta to create a protein interaction network of APP and its proteolytic cleavage products, with annotation, where possible, to the level of APP binding domain and isoform. This is the first time that an interactome has been refined to domain level, essential for the interpretation of APP due to the presence of multiple isoforms and processed fragments. Gene ontology and network analysis were used to identify potentially novel functional relationships among interacting proteins.

Protein Folding Database (PFD 2.0): an online environment for the International Foldeomics Consortium.

The Protein Folding Database (PFD) is a publicly accessible repository of thermodynamic and kinetic protein folding data. Here we describe the first major revision of this work, featuring extensive restructuring that conforms to standards set out by the recently formed International Foldeomics Consortium. The database now adopts standards for data acquisition, analysis and reporting proposed by the consortium, which will facilitate the comparison of folding rates, energies and structure across diverse sets of proteins. Data can now be easily deposited using a rich set of deposition tools. Enhanced search tools allow sophisticated searching and graphical data analysis affords simple data analysis online. PFD can be accessed freely at http://www.foldeomics.org/pfd/.

A blood-based signature of cerebrospinal fluid Aß1-42 status.

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 ß1-42 (Aß1-42) 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 Aß1-42 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, Aß1-42, 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 Aß1-42 levels transitioned to an AD diagnosis over 120 months significantly faster than those with predicted normal CSF Aß1-42 levels and that the resulting model also validates reasonably across PET Aß1-42 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 Aß1-42 status, the earliest risk indicator for AD, with high accuracy.

A hybrid approach for automated mutation annotation of the extended human mutation landscape in scientific literature.

As the cost of DNA sequencing continues to fall, an increasing amount of information on human genetic variation is being produced that could help progress precision medicine. However, information about such mutations is typically first made available in the scientific literature, and is then later manually curated into more standardized genomic databases. This curation process is expensive, time-consuming and many variants do not end up being fully curated, if at all. Detecting mutations in the literature is the first key step towards automating this process. However, most of the current methods have focused on identifying mutations that follow existing nomenclatures. In this work, we show that there is a large number of mutations that are missed by using this standard approach. Furthermore, we implement the first mutation annotator to cover an extended mutation landscape, and we show that its F1 performance is the same performance as human annotation (F1 78.29 for manual annotation vs F1 79.56 for automatic annotation).

CuII(atsm) improves the neurological phenotype and survival of SOD1G93A mice and selectively increases enzymatically active SOD1 in the spinal cord.

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 CuII(atsm) tested for therapeutic efficacy in mice expressing SOD1G93A on a mixed genetic background. Oral administration of CuII(atsm) delayed the onset of neurological symptoms, improved locomotive capacity and extended overall survival. Although the ALS-like phenotype of SOD1G93A mice is instigated by expression of the mutant SOD1, we show the improved phenotype of the CuII(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 CuII(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 CuII(atsm) as a treatment option for ALS as well as insight to the CNS-selective effects of mutant SOD1.

Managing and mining protein crystallization data.

The crystallization of macromolecules remains a major bottleneck in structural biology. The routine screening of more than one thousand crystallization conditions and subsequent optimization by fine screening presents a challenge to conventional laboratory notebook keeping. In addition, the development of high-throughput robotic crystallization and imaging systems presents a pressing need for low-cost laboratory information management system (LIMS). Here we describe CLIMS2, a crystallization LIMS that features a simple, user-friendly graphical interface, allowing the storage, management, retrieval and mining of crystallization data. The CLIMS2 executable and documentation is freely available at http://clims.med.monash.edu.au.

Lead and manganese levels in serum and erythrocytes in Alzheimer's disease and mild cognitive impairment: results from the Australian Imaging, Biomarkers and Lifestyle Flagship Study of Ageing.

We examined serum and erythrocyte lead and manganese levels in the Australian Imaging, Biomarkers and Lifestyle Flagship Study of Ageing (AIBL), which contains over 1000 registrants including over 200 cases of Alzheimer's disease (AD) and 100 mildly cognitively impaired (MCI) individuals. After correcting for confounding effects of age, collection site and sex, we found a significant decrease in serum manganese levels in AD subjects compared to healthy controls. Analysis of smaller subset of erythrocytes revealed no difference in either lead or manganese levels in AD. Although lead and manganese have neurotoxic effects and may be involved in AD pathology, our results showed that neither metal in serum nor erythrocytes are suitable biomarkers in our cohort. However, prospective studies might reveal whether the burden of either metal modifies disease outcomes.

Predicting Alzheimer disease from a blood-based biomarker profile: A 54-month follow-up.

OBJECTIVE: We assessed a blood-based signature, which previously demonstrated high accuracy at stratifying individuals with high or low neocortical ß-amyloid burden (NAB), to determine whether it could also identify individuals at risk of progression to Alzheimer disease (AD) within 54 months. METHODS: We generated the blood-based signature for 585 healthy controls (HCs) and 74 participants with mild cognitive impairment (MCI) from the Australian Imaging, Biomarkers and Lifestyle Study who underwent clinical reclassification (blinded to biomarker findings) at 54-month follow-up. The individuals were split into estimated high and low NAB groups based on a cutoff of 1.5 standardized uptake value ratio. We assessed the predictive accuracy of the high and low NAB groupings based on progression to mild cognitive impairment or AD according to clinical reclassification at 54-month follow-up. RESULTS: Twelve percent of HCs with estimated high NAB progressed in comparison to 5% of HCs with estimated low NAB (odds ratio = 2.4). Forty percent of the participants with MCI who had estimated high NAB progressed in comparison to 5% of the participants with MCI who had estimated low NAB (odds ratio = 12.3). These ratios are in line with those reported for Pittsburgh compound B-PET results. Individuals with estimated high NAB had faster rates of memory decline than those with estimated low NAB. CONCLUSION: These findings suggest that a simple blood-based signature not only provides estimates of NAB but also predicts cognitive decline and disease progression, identifying individuals at risk of progressing toward AD at the prodromal and preclinical stages.