Identification of Targets from LRRK2 Rescue Phenotypes

Parkinson’s disease (PD) is an age-dependent neurodegenerative condition. Leucine-rich repeat kinase 2 (LRRK2) mutations are the most frequent cause of sporadic and autosomal dominant PD. The exact role of LRRK2 protective variants (R1398H, N551K) together with a pathogenic mutant (G2019S) in aging and neurodegeneration is unknown. We generated the following myc-tagged UAS-LRRK2 transgenic Drosophila: LRRK2 (WT), N551K, R1398H, G2019S single allele, and double-mutants (N551K/G2019S or R1398H/G2019S). The protective variants alone were able to suppress the phenotypic effects caused by the pathogenic LRRK2 mutation. Next, we conducted RNA-sequencing using mRNA isolated from dopaminergic neurons of these different groups of transgenic Drosophila. Using pathway enrichment analysis, we identified the top 10 modules (p < 0.05), with “LRRK2 in neurons in Parkinson’s disease” among the candidates. Further dissection of this pathway identified the most significantly modulated gene nodes such as eEF1A2, ACTB, eEF1A, and actin cytoskeleton reorganization. The induction of the pathway was successfully restored by the R1398H protective variant and R1398H-G2019S or N551K-G2019S rescue experiments. The oxidoreductase family of genes was also active in the pathogenic mutant and restored in protective and rescue variants. In summary, we provide in vivo evidence supporting the neuroprotective effects of LRRK2 variants. RNA sequencing of dopaminergic neurons identified upregulation of specific gene pathways in the Drosophila carrying the pathogenic variant, and this was restored in the rescue phenotypes. Using protective gene variants, our study identifies potential new targets and provides proof of principle of a new therapeutic approach that will further our understanding of aging and neurodegeneration in PD.

The Long Life Family Study: Sequencing Exceptional Pedigrees for Rare Protective Variants

The Long Life Family Study (LLFS) has longitudinally measured key aging phenotypes on 4,953 participants (539 pedigrees) in the USA and Denmark selected for exceptional familial longevity. On average, both generations of the LLFS sample are healthier than average for their age/sex, for many phenotypes. However, the pedigrees are heterogeneous, with different families showing familial clustering of protection for different phenotypes. Linkage analyses identified extremely strong genetic linkage peaks for many cross-sectional as well as longitudinal trajectory rates of change phenotypes. These peaks are NOT explained by GWAS SNPs (either measured or imputed). Pedigree specific HLODs and preliminary deep sequencing suggests that these peaks are driven by rare, protective variants running in selected pedigrees. Whole Genome Sequencing, a third longitudinal visit, and extensive OMICs (transcriptomics, epigenomics, metabolomics and proteomics) will help us resolve the mechanisms behind these protective genetically linked variants, and could illuminate new biology and enable new therapeutics.

GENOMICS OF SARS-COV-2: A STUDY

SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is a type of novel coronavirus responsible for the COVID-19 outbreak, which attained pandemic proportions in 2020. SARS-CoV-2 belongs to that group of coronaviruses which infect the lower respiratory tract. The complete genome was found to be 29.9kb in size. The viral genome encodes 16 non-structural proteins (Nsps), essential for pathogenesis and replication. Structural proteins- envelope (E), membrane (M), nucleocapsid (N), and spike (S) glycoprotein are also encoded, important for virus subtyping and response to vaccines. L and S type of SARS-CoV-2 were identified in Wuhan initially. Later on, as the pandemic spread across the globe, A and C subtypes were identified to me the common type of strains across the Americas and Europe respectively. In Asia, B subtype was found to be common. ACE2 (angiotensin-converting enzyme 2), a receptor is utilised by SARS-CoV-2 for entry into host cell. People of European descent have a higher frequency of alternate allele of rs763395248 SNP in T92I risk variant compared to African and Asian population. In Asian populations, SNPs like rs758278442 and rs759134032 in the region of protective variants (K31R and Y83H) of ACE2 gene show higher frequency of mutant alleles than American and European populations. According to a structural modelling study, certain ACE2 variants may provide potential resistance to SARS-CoV-2 infection.

Missense variants in ACE2 are predicted to encourage and inhibit interaction with SARS-CoV-2 Spike and contribute to genetic risk in COVID-19

SARS-CoV-2 invades host cells via an endocytic pathway that begins with the interaction of the SARS-CoV-2 Spike glycoprotein (S-protein) and human Angiotensin-converting enzyme 2 (ACE2). Genetic variability in ACE2 may be one factor that mediates the broad-spectrum severity of SARS-CoV-2 infection and COVID-19 outcomes. We investigated the capacity of ACE2 variation to influence SARS-CoV-2 infection with a focus on predicting the effect of missense variants on the ACE2 SARS-CoV-2 S-protein interaction. We validated the mCSM-PPI2 variant effect prediction algorithm with 26 published ACE2 mutant SARS-CoV S-protein binding assays and found it performed well in this closely related system (True Positive Rate = 0.7, True Negative Rate = 1). Application of mCSM-PPI2 to ACE2 missense variants from the Genome Aggregation Consortium Database (gnomAD) identified three that are predicted to strongly inhibit or abolish the S-protein ACE2 interaction altogether (p.Glu37Lys, p.Gly352Val and p.Asp355Asn) and one that is predicted to promote the interaction (p.Gly326Glu). The S-protein ACE2 inhibitory variants are expected to confer a high degree of resistance to SARS-CoV-2 infection whilst the S-protein ACE2 affinity enhancing variant may lead to additional susceptibility and severity. We also performed in silico saturation mutagenesis of the S-protein ACE2 interface and identified a further 38 potential missense mutations that could strongly inhibit binding and one more that is likely to enhance binding (Thr27Arg). A conservative estimate places the prevalence of the strongly protective variants between 12-70 per 100,000 population but there is the possibility of higher prevalence in local populations or those underrepresented in gnomAD. The probable interplay between these ACE2 affinity variants and ACE2 expression polymorphisms is highlighted as well as gender differences in penetrance arising from ACE2’s situation on the X-chromosome. It is also described how our data can help power future genetic association studies of COVID-19 phenotypes and how the saturation mutant predictions can help design a mutant ACE2 with tailored S-protein affinity, which may be an improvement over a current recombinant ACE2 that is undergoing clinical trial.Key results1 ACE2 gnomAD missense variant (p.Gly326Glu) and one unobserved missense mutation (Thr27Arg) are predicted to enhance ACE2 binding with SARS-CoV-2 Spike protein, which could result in increased susceptibility and severity of COVID-193 ACE2 missense variants in gnomAD plus another 38 unobserved missense mutations are predicted to inhibit Spike binding, these are expected to confer a high degree of resistance to infectionThe prevalence of the strongly protective variants is estimated between 12-70 per 100,000 population but higher prevalence may exist in local populations or those underrepresented in gnomADA strategy to design a recombinant ACE2 with tailored affinity towards Spike and its potential therapeutic value is presentedThe predictions were extensively validated against published ACE2 mutant binding assays for SARS-CoV Spike protein

Testing gene-environment interactions for rare and/or common variants in sequencing association studies

The risk of many complex diseases is determined by a complex interplay of genetic and environmental factors. Advanced next generation sequencing technology makes identification of gene-environment (GE) interactions for both common and rare variants possible. However, most existing methods focus on testing the main effects of common and/or rare genetic variants. There are limited methods developed to test the effects of GE interactions for rare variants only or rare and common variants simultaneously. In this study, we develop novel approaches to test the effects of GE interactions of rare and/or common risk, and/or protective variants in sequencing association studies. We propose two approaches: 1) testing the effects of an optimally weighted combination of GE interactions for rare variants (TOW-GE); 2) testing the effects of a weighted combination of GE interactions for both rare and common variants (variable weight TOW-GE, VW-TOW-GE). Extensive simulation studies based on the Genetic Analysis Workshop 17 data show that the type I error rates of the proposed methods are well controlled. Compared to the existing interaction sequence kernel association test (ISKAT), TOW-GE is more powerful when there are GE interactions’ effects for rare risk and/or protective variants; VW-TOW-GE is more powerful when there are GE interactions’ effects for both rare and common risk and protective variants. Both TOW-GE and VW-TOW-GE are robust to the directions of effects of causal GE interactions. We demonstrate the applications of TOW-GE and VW-TOW-GE using an imputed data from the COPDGene Study.

Test Gene-Environment Interactions for Multiple Traits in Sequencing Association Studies

The risk of many complex diseases is determined by a complex interplay of genetic and environmental factors. Data on multiple traits is often collected for many complex diseases in order to obtain a better understanding of the diseases. Examination of gene-environment interactions (GxEs) for multiple traits can yield valuable insights about the etiology of the disease and increase power in detecting disease associated genes. Most existing methods focus on testing gene-environment interaction (GxE) for a single trait. In this study, we develop novel approaches to test GxEs for multiple traits in sequencing association studies. We first perform transformation of multiple traits by using either principle component analysis or standardization analysis. Then, we detect the effect of GxE for each transferred phenotypic trait using novel proposed tests: testing the effect of an optimallyweighted combination of GxE (TOW-GE) and/or variable weight TOW-GE (VW-TOW-GE). Finally, we employ the Fisher’s combination test to combine the p-values of TOW-GE and/or VW-TOW-GE. Extensive simulation studies based on the Genetic Analysis Workshop 17 data show that the type I error rates of the proposed methods are well controlled. Compared to the existing interaction sequence kernel association test (ISKAT), TOW-GE is more powerful when there are only rare risk and protective variants; VW-TOW-GE is more powerful when there are both rare and common risk and protective variants. Both TOW-GE and VW-TOW-GE are robust to directions of effects of causal GxEs. Application to the COPDGene Study demonstrates that our proposed methods are very powerful.