Integrating protein networks and machine learning for disease stratification in the Hereditary Spastic Paraplegias.

The Hereditary Spastic Paraplegias are a group of neurodegenerative diseases characterized by spasticity and weakness in the lower body. Owing to the combination of genetic diversity and variable clinical presentation, the Hereditary Spastic Paraplegias are a strong candidate for protein-protein interaction network analysis as a tool to understand disease mechanism(s) and to aid functional stratification of phenotypes. In this study, experimentally validated human data were used to create a protein-protein interaction network based on the causative genes. Network evaluation as a combination of topological analysis and functional annotation led to the identification of core proteins in putative shared biological processes, such as intracellular transport and vesicle trafficking. The application of machine learning techniques suggested a functional dichotomy linked with distinct sets of clinical presentations, indicating that there is scope to further classify conditions currently described under the same umbrella-term of Hereditary Spastic Paraplegias based on specific molecular mechanisms of disease

Cytosolic sequestration of spatacsin by Protein Kinase A and 14-3-3 proteins

Mutations in SPG11, encoding spatacsin, constitute the major cause of autosomal recessive Hereditary Spastic Paraplegia (HSP) with thinning of the corpus callosum. Previous studies showed that spatacsin orchestrates cellular traffic events through the formation of a coat-like complex and its loss of function results in lysosomal and axonal transport impairments. However, the upstream mechanisms that regulate spatacsin trafficking are unknown. Here, using proteomics and CRISPR/Cas9-mediated tagging of endogenous spatacsin, we identified a subset of 14-3-3 proteins as physiological interactors of spatacsin. The interaction is modulated by Protein Kinase A (PKA)-dependent phosphorylation of spatacsin at Ser1955, which initiates spatacsin trafficking from the plasma membrane to the intracellular space. Our study provides novel insight in understanding spatacsin physio- pathological roles with mechanistic dissection of its associated pathways

Human mutations in SLITRK3 implicated in GABAergic synapse development in mice

This study reports on biallelic homozygous and monoallelic de novo variants in SLITRK3 in three unrelated families presenting with epileptic encephalopathy associated with a broad neurological involvement characterized by microcephaly, intellectual disability, seizures, and global developmental delay. SLITRK3 encodes for a transmembrane protein that is involved in controlling neurite outgrowth and inhibitory synapse development and that has an important role in brain function and neurological diseases. Using primary cultures of hippocampal neurons carrying patients' SLITRK3 variants and in combination with electrophysiology, we demonstrate that recessive variants are loss-of-function alleles. Immunostaining experiments in HEK-293 cells showed that human variants C566R and E606X change SLITRK3 protein expression patterns on the cell surface, resulting in highly accumulating defective proteins in the Golgi apparatus. By analyzing the development and phenotype of SLITRK3 KO (SLITRK3-/-) mice, the study shows evidence of enhanced susceptibility to pentylenetetrazole-induced seizure with the appearance of spontaneous epileptiform EEG as well as developmental deficits such as higher motor activities and reduced parvalbumin interneurons. Taken together, the results exhibit impaired development of the peripheral and central nervous system and support a conserved role of this transmembrane protein in neurological function. The study delineates an emerging spectrum of human core synaptopathies caused by variants in genes that encode SLITRK proteins and essential regulatory components of the synaptic machinery. The hallmark of these disorders is impaired postsynaptic neurotransmission at nerve terminals; an impaired neurotransmission resulting in a wide array of (often overlapping) clinical features, including neurodevelopmental impairment, weakness, seizures, and abnormal movements. The genetic synaptopathy caused by SLITRK3 mutations highlights the key roles of this gene in human brain development and function

Systems biology approaches for neurodegeneration and macroautophagy

Neurodegenerative diseases have been identified and studied for decades but disease-modifying drugs are still unavailable for their majority. Their genetic and clinical complexity renders the identification of the precise molecular disease mechanism challenging. Holistic approaches that allow the analysis of diseases in a systems level, studying multiple genes and their protein products simultaneously, could aid in the endeavour to find treatment for neurodegenerative diseases, such as the Hereditary Spastic Paraplegias (HSPs) and Parkinson’s disease (PD). Firstly, a protein-protein interaction network (PPIN) analysis was performed centred on proteins derived from genes that lead to HSPs, revealing that their majority share at least one interactor. This suggests that they participate in common biological processes and pathways. Enrichment analysis highlighted membrane trafficking and vesicle mediated pathways as important for the HSPs. Furthermore, the clinical complexity of the disease led to the investigation of potential mechanistic differences of the disease depending on the mode of inheritance, type of HSP, and clinical features. The analysis of the latter also utilised basic machine learning tools (principal component analysis and hierarchical clustering) and suggested the existence of 2 subgroups of HSPs with divergent disease mechanisms. To investigate how a fundamental cellular process can contribute to disease, macroautophagy was studied, as it is associated with multiple neurodegenerative diseases. This connection was investigated initially by creating 5 PPINs (macroautophagy, PD, Alzheimer’s disease, Amyotrophic lateral sclerosis, and Frontotemporal dementia), and examining their overlap. As the intersection between all studied neurodegenerative diseases and macroautophagy was extensive, I focused on the relationship between macroautophagy and PD. This required the creation of a mathematical model of the initial stages of macroautophagy, in which differential protein amounts were used to simulate a healthy person versus a person with PD. Interestingly, this distinction in amounts was sufficient to simulate differential kinetics of macroautophagy

Tissue specific LRRK2 interactomes reveal a distinct striatal functional unit.

Mutations in LRRK2 are the most common genetic cause of Parkinson's disease. Despite substantial research efforts, the physiological and pathological role of this multidomain protein remains poorly defined. In this study, we used a systematic approach to construct the general protein-protein interactome around LRRK2, which was then evaluated taking into consideration the differential expression patterns and the co-expression behaviours of the LRRK2 interactors in 15 different healthy tissue types. The LRRK2 interactors exhibited distinct expression features in the brain as compared to the peripheral tissues analysed. Moreover, a high degree of similarity was found for the LRRK2 interactors in putamen, caudate and nucleus accumbens, thus defining a potential LRRK2 functional cluster within the striatum. The general LRRK2 interactome paired with the expression profiles of its members constitutes a powerful tool to generate tissue-specific LRRK2 interactomes. We exemplified the generation of the tissue-specific LRRK2 interactomes and explored the functions highlighted by the "core LRRK2 interactors" in the striatum in comparison with the cerebellum. Finally, we illustrated how the LRRK2 general interactome reported in this manuscript paired with the expression profiles can be used to trace the relationship between LRRK2 and specific interactors of interest, here focusing on the LRRK2 interactors belonging to the Rab protein family

Co-expression analysis on the LRRK2_int.

A) Pair-wise Tukey’s test was performed to compare the co-expression coefficients (interactors vs LRRK2) across different tissues. Tissues were ranked according to the results. The bar graph shows that putamen, nucleus accumbens, caudate and hypothalamus are tissues with the highest ranks. Liver presents a rank of 0, meaning the co-expression coefficients of LRRK2 interactors are the lowest in comparison with any other tissues analysed. B) The heatmap was generated from the coefficient matrix derived from the co-expression analysis (Heatmap_Co-ex). Darker colour represents higher co-expression coefficient. The horizonal dendrogram of Heatmap_Co-ex was extracted as Den_Co-ex1, which shows the hierarchical clustering of tissues in which the LRRK2 interactors exhibited similar co-expression patterns with LRRK2. The vertical dendrogram of Heatmap_Co-ex was extracted as Den_Co-ex2, which shows the hierarchical clustering of interactors based on the similarity of their co-expression figures with LRRK2 across different tissues. Den_Co-ex2 was cut to generate 6 clusters of LRRK2 interactors (Cluster A-F, marked in green, blue, yellow, red, purple and turquoise, respectively). Interactors in Cluster D presents the highest level of overall co-expression behaviour with LRRK2 across different tissues (referred as Co-ex_ClusterLRRK2). Abbreviations: ACC: Anterior Cingulate Cortex; AMYG: Amygdala; CAU: caudate; CR: cerebellum; FC: frontal cortex; HP: hippocampus; HYPT: hypothalamus; NAc: nucleus accumbens; PUT: putamen; SN: substantia nigra; SPC: spinal cord c-1; Kidney_c: kidney cortex.</p

Top term in the Go:BP functional enrichment of the DEA Cluster_LRRK2.

Top term in the Go:BP functional enrichment of the DEA ClusterLRRK2.</p

Functional roles of Rab interactors of LRRK2.

The heatmap shows the functional groups that included the Rab proteins presented in the LRRK2int. Blue squares represent the presence of a certain Rab interactor in a given functional group identified in the functional enrichment analysis for the general LRRK2int.</p

S1 Table -

Table A in S1 Table. PINOT Method. Table B in S1 Table. Merged list. Table C in S1 Table. FS>2. Table D in S1 Table. family.check. Table E in S1 Table. Original.enrichment. Table F in S1 Table. GOterms(LRRK2 only). Table G in S1 Table. functional.groups. Table H in S1 Table. RNA.QC. Table I in S1 Table. DEA.ranks. Table J in S1 Table. DEA.clusters. Table K in S1 Table. Coex.clusters. Table L in S1 Table Tissue specific ints. Table M in S1 Table. CAU-NAC-PUT-CR. Table N in S1 Table. enrichment (XLSX)</p

PPI quality control pipeline for LRRK2 interactome construction.

Human PPI data downloaded from PINOT, HIPPIE and MIST databases were merged after ID conversion using HGNC gene symbols. Merged data underwent interaction detection method reassignment using an in-house dictionary. Publication score (PS) was defined as the number of papers in which a PPI was reported, while method score (MS) was defined as the number of different methods by which a PPI was detected. Final score (FS) was calculated as PS + MS. PPIs with FS ≤ 2 were excluded from further analysis.</p