Integration of and transcriptomics and proteomics

Similar to what was done with the small RNA and transcriptomics datasets, the overlapping DE results for transcriptomics and proteomics experiments were also explored in an integrative fashion. In this case, we looked for the concordant overlap between the coding mRNAs and their respective protein product (i.e. up- and down-regulated transcript-protein pairs). Factors related to transcript-protein metabolism and kinetics (e.g., synthesis and degradation rates of proteins) were out of the scope of this study and were therefore not considered for the integrative analyses presented here, although they are likely to significantly contribute to the overall picture.

Proteomics and RNA sequencing results are fundamentally very different, starting by the nature of the signals and the coverage of those techniques. That makes the comparison of quantitative values (in this case, counts and intensities) by bioinformatical pipelines rather challenging. Therefore, we decided to apply a manual cross-correlation method for the integration, similarly to what was done for the manual integration of the miRNA and transcriptomics data. The much smaller range of DE proteins (127) was also favorable for the employment of such an approach. As presented in section 3.11.2, the total number of uniquely identified proteins was more than 20 times smaller than the number of mapped transcripts. Therefore, several of the DE transcripts found in the RNA sequencing results did not have their respective protein product identified in the proteomics results. The opposite also happened, to a lesser extent. Overall, disregarding the validity of the interaction pairs (i.e. the directionality of the fold changes in expression) the integrative approaches applied here revealed that 30 candidates were found significantly regulated in both transcriptomics and proteomics experiments (around 24% of all the proteomics DE results). When considering the valid pairs with concordant fold change in expression in both datasets, a final list of 13 candidates was retrieved from the

98 integration of the transcriptomics and proteomics datasets. Seven of them presented up-regulation in the PD samples (ATP6V1E1, C9ORF47, CD47, ACOT7, RPL35, SEC23A and RPN1) while six candidates were found down-regulated in PD, both in transcriptomics and proteomics (TH, DBT, CD200, RAB18, NIPSNAP3A UBE2L3). It is important to mention that the final list of candidates selected from the integration of transcriptomics and proteomics is currently being validated by Western Blotting and the results will not figure in the scope of this thesis. As discussed in sections 4.4 and 4.5, the presence of the dopamine marker TH among the down-regulated candidates in both datasets, as well as the present of coincidentally enriched pathways throughout the omics datasets (e.g. inflammatory response, metabolism- and apoptosis-related processes) provide additional reliability to the functional annotation presented for each approach.

4.7. Validation of sequencing results in human midbrain tissue by q-RT-PCR 4.7.1. miRNA data validation

After integration and the final miRNA candidate selection (section 3.11.1), nine miRNAs were validated in the human midbrain tissue (upon fresh RNA isolation and reverse transcription for cDNA preparation). For the miRNAs found down-regulated in PD by sequencing experiments, all selected candidates (let-7g-5p, 20a-5p, 145-5p, miR-98-5p) were successfully validated by q-RT-PCR, presenting the same expression directionality as observed in the sequencing. For the miRNAs found up-regulated in PD by small RNA sequencing, 2 out of 5 (let-7i and miR-29c) presented significant differences in the validation studies with a regulation in the opposite direction compared to what was observed in the sequencing. Similar findings with an opposing regulation of one and the same target from discovery to validation studies have been frequently reported in biomarker studies using PD body fluids (Roser et al., 2018b; Halbgebauer et al., 2016). A number of factors might influence the outcome of validation studies. For example, alternative splicing often leads to discordant results between sequencing and q-RT-PCR experiments (Nazarov et al., 2017). The sensitivity of the two methods might also be an issue. A study reported a sensitivity threshold for the validation of sequencing results by q-RT-PCR. Validation of significantly DE miRNAs identified by Solexa sequencing was not

99 possible for species presenting very low sequencing reads (<100 reads per species) (Cristino et al., 2011). In addition, the design of different commercially available primers and the number of amplified exons might also interfere with the validation of candidates identified by the sequencing. The opposing results presented here could be further explored by repeating the experiments with additional primers for the selected candidates, for example. Overall, the results showed that four out of the nine selected miRNA candidates were successfully validated by q-RT-PCR and concordant to the small RNA sequencing data.

An overview of the biological relevance of all DE miRNAs is presented in section 4.4.

For the validated miRNAs (let-7g-5p, miR-20a-5p, miR-145-5p, miR-98-5p), several of them have been implicated in neuronal- and neurodegeneration-related processes. A comprehensive study showed that let-7g-5p is among the top 40 highly expressed miRNA found in human brains (Shao et al., 2010). Deregulation of let-7g-5p has been also linked to neurodegenerative diseases. For instance, this miRNA has been included in a plasma biomarker panel of signature miRNAs able to distinguish AD patients from controls with high accuracy (Kumar et al., 2013). It has been also found significantly down-regulated in peripheral blood of Amyotrophic Lateral Sclerosis (ALS) patients (Liguori et al., 2018). Let-7g-5p has also been shown be deregulated in PD models of αSyn overexpression (Asikainen et al., 2010), and the complementary strand of this miRNA, let-7g-3p, has been found differentially expressed in PD CSF (Gui et al., 2015a). Similarly, miR-20a-5p has been found in altered levels in both AD CSF and PD cortical brain regions (Chatterjee and Roy, 2017;

Riancho et al., 2017). This miRNA is known to regulate neuronal differentiation (Cui et al., 2016). Another of the validated miRNAs, miR-98-5p, has been extensively linked to AD. It has been shown to regulate amyloid β-peptide (Aβ) protein production (Li et al., 2016) and has been found in decreased levels in AD serum (Tan et al., 2014). Finally, miR-145-5p has been implicated in a variety of neuronal-related processes. For example, it is known to play a role in neuronal differentiation as well as to induce glial inflammatory insults (Jauhari et al., 2018; Li et al., 2013b). Remarkably, miR-145-5p is known to regulate, for example, microglia activation and astrocyte injury in models of cerebral stroke (Qi et al., 2017; Xie et al., 2017; Zheng et al., 2017), matching the intense inflammatory and immune response activation observed in our PD samples. Our findings provide more robust evidence for the

100 validated miRNA candidates as important regulators in neurodegenerative processes taking place in PD.