The Mäkinen Group is focused on determining causal evidence to understand the cellular biology of Alzheimer’s disease.

Alzheimer’s disease is the most common cause of dementia, and it typically affects people over the age of 65 with exponentially increasing risk towards the oldest age groups. We do not know what triggers it, nor do we fully understand the biological processes when the disease takes hold. The lack of etiological knowledge is a major roadblock to therapies: so far, all clinical trials targeted at the prevailing mechanistic hypothesis have failed.

Neuro-degenerative diseases may stem from impaired autophagy and dysfunctional endosomal vesicle trafficking in the aging brain. In Alzheimer’s disease, dysfunctional autophagic vacuoles (i.e. one of the intermediate stages of trafficking) accumulate in and between neurons. Furthermore, we found a genetic association and a selective gene expression signature that implicated the endo-lysosomal system genes as a group in both Alzheimer’s and Parkinson’s diseases. For these reasons, our mission is to connect the patterns of genetic disease associations with downstream effects on gene expression and on the regulation of the endo-lysosomal system.

Current projects:

We are conducting the first wave of time-series RNA-seq profiling of endo-lysosomal activation using live human cells and a genetically optimized tandem-fluorescent reporter protein. This will provide us with new insight into how the transcriptome responds in different phases of autophagic activation in diverse tissues and following diverse stimuli.

New computational work involves an expanded systems genetics study of Alzheimer’s disease and cognition in the UK Biobank. The results from these analyses will give us more detailed information on what parts of the endo-lysosomal system carry the genetic signals and whether these signals are shared with other age-associated morbidities.

In the background, we are also pursuing method development for integrative genomics and data-guided subgrouping to facilitate the analyses of complex biological datasets.

 

Three members of Makinen group looking at computer screens

Systems genetics of late-onset dementia

  • Mathematically comparable network hypotheses of Alzheimer’s disease etiology
  • Annotation of genegene interaction subnetworks within the endo-lysososmal system
  • New algorithms and visualization to test network hypotheses on genomewide associations
  • Network enrichment analyses of Alzheimer’s and Parkinson’s disease
  • Genetics and ageassociated morbidity in the UK Biobank

Transcriptional regulation during the activation of the endo-lysosomal system

  • Optimization of cell lines that express a tandemfluorescent reporter of autophagic flux
  • Total RNAseq of cell lines over multiple time points during activation of autophagy
  • Statistics and visualization of RNAseq time-series
  • Integrative genomics to compare transcriptional signatures with disease GWAS

Data-guided subgrouping of aging trajectories in human populations

  • Cardiovascular risk prediction from metabolomics and lipidomics
  • Characterization of metabolic ageing trajectories in longitudinal cohorts
  • Metabolomics of acute responses to food or glucose ingestion
  • Training workshop on clustering and subgrouping methods (the Numero R library

 

Authors
Title
Published In

Song G, Mutter S, Casey A, Sargeant T, Mäkinen V-P

Genetic variation within endolysosomal system is associated with late-onset Alzheimer’s disease

Brain, 144:2711-2720. (2018)

Song G, Mutter S, Casey A, Mäkinen V-P

Numero: a statistical framework to define multivariable subgroups in complex population-based datasets

Int J Epidemiology, in press. (2018)

Mäkinen V-P

Body Mass Index in Children Validated by Metabolic and Fat Mass Profiling

Editorial comment: J Am Coll Cardiol. (2018)

Mutter S, Casey AE, Zhen S, Shi Z, Mäkinen V-P

Multivariable analysis of nutritional and socio-economic profiles shows differences in incident anemia for Northern and Southern Jiangsu in China

Nutrients, 21:9 (2017)

Couto Alves A, Valcarcel B, Mäkinen V-P, Morin-Papunen L, Sebert S, Kangas AJ, Soininen P, Das S, De Iorio M, Coin L, Ala-Korpela M, Järvelin MR, Franks S

Metabolic profiling of Polycystic Ovary Syndrome Reveals Interactions with Abdominal Obesity

 Int J of Obesity 41:1331-1340. (2017)

 Lithovius R, Toppila I, Harjutsalo V, Forsblom C, Groop PH, Mäkinen V-P

Data-driven metabolic subtypes predict future adverse events in individuals with type 1 diabetes

Diabetologia, 60:1234-1243. (2017)

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