Using Threshold Regression as an Approach to Incorporate Informative Missingness in Long Life Family Study Data

Genetics of aging is important since aging is a major risk factor in most diseases. Variables describing physiological state and cognitive functioning that influence morbidity and mortality risks can serve as biomarkers of aging. They change with increasing age and the ways in which these variables change can also influence these risks. Missing data due to dropout or death create problems in longitudinal studies producing biased results especially if the gap between exams is relatively long, as is the case in the Long Life Family Study (LLFS). We applied the threshold regression model to LLFS data to investigate the vitality and its rate, which are conceptualized as latent variables characterizing health and longevity, and to cope with such a problem. We performed genome-wide association study by sex and age groups to discover genetic signals on these phenotypes. We found 11 variants from the DACT2 gene, p-values < 1E-6 and variants rs12151399 (p-value = 8.43E-8, intron variant, gene AGAP1, in females), rs27958 (p-value = 8.39E-8, intron variant, gene ARHGAP26, in males) showing associations with the vitality. Olfactory receptors showed significant enrichment among the group of males over 80 years for the rate of aging phenotype. Results showed that vitality and its rate differ among sex and age groups. This work is an important step toward understanding the processes of aging linking the vitality with individual genetics using data from deceased and living individuals.

evSeq: Cost-Effective Amplicon Sequencing of Every Variant in a Protein Library

Widespread availability of protein sequence-fitness data would revolutionize both our biochemical understanding of proteins and our ability to engineer them. Unfortunately, even though thousands of protein variants are generated and evaluated for fitness during a typical protein engineering campaign, most are never sequenced, leaving a wealth of potential sequence-fitness information untapped. This largely stems from the fact that sequencing is unnecessary for many protein engineering strategies; the added cost and effort of sequencing is thus unjustified. Here, we present every variant sequencing (evSeq), an efficient protocol for sequencing a variable region within every variant gene produced during a protein engineering campaign at a cost of cents per variant. Execution of evSeq is simple, requires no sequencing experience to perform, relies only on resources and services typically available to biology labs, and slots neatly into existing protein engineering workflows. Analysis of evSeq data is likewise made simple by its accompanying software (found at github.com/fhalab/evSeq, documentation at fhalab.github.io/evSeq), which can be run on a personal laptop and was designed to be accessible to users with no computational experience. Low-cost and easy to use, evSeq makes collection of extensive protein variant sequence-fitness data practical.

Chromodomain protein regulates the expression of a subset of RIFINs in P. falciparum

Plasmodium falciparum expresses clonally variant proteins on the surface of infected erythrocytes to evade the host immune system. The clonally variant multigenes include var, rifin, and stevor, which express EMP1, RIFIN, and STEVOR proteins, respectively. The rifins are the largest multigene family and are essentially involved in the RBC rosetting, the hallmark of severe malaria. The regulators that control the RIFINs expression in P. falciparum have not been reported so far. This study reports a chromodomain-containing protein (PfCDP) that binds to H3K9me3 modification on P. falciparum chromatin. The ChIP- sequencing analysis revealed that the PfCDP is majorly associated with clonally variant gene families, primarily rifins in P. falciparum. Conditional deletion of the chromodomain (CD) gene in P. falciparum leads to the up-regulation of a subset of virulence genes, including rifins, a few var, and stevor genes. Further, we show that PfΔCDP P. falciparum lines promote the RBC rosetting. This study provides evidence of an epigenetic regulator mediated control on a subset of RIFINs expression and RBC rosetting by P. falciparum.

Plasmodium falciparum Acetyl-CoA Synthetase is essential for parasite intraerythrocytic development and chromatin modification

The malaria parasite Plasmodium falciparum possesses a unique Acetyl-CoA Synthetase (PfACS) which provides acetyl moieties for different metabolic and regulatory cellular pathways. We characterized PfACS and studied its role focusing on epigenetic modifications using the var gene family as reporter genes. For this, mutant lines to modulate plasmodial ACS expression by degron-mediated protein degradation or ribozyme induced transcript decay were created. Additionally, an ACS inhibitor was tested for its effectiveness and specificity in interfering with PfACS. The knockdown of PfACS or its inhibition led to impaired parasite growth. Decreased levels of PfACS also led to differential histone acetylation patterns, altered variant gene expression and concomitantly decreased cytoadherence of infected red blood cells containing knocked-down parasites. Further, ChIP analysis revealed the presence of PfACS in many loci in ring stage parasites, underscoring its involvement in the regulation of chromatin. Due to its significant differences to human ACS, PfACS seems an interesting target for drug development.

Wild-type sTREM2 blocks Aβ aggregation and neurotoxicity, while the Alzheimer’s R47H mutant does the opposite

Missense mutations (e.g. R47H) of the microglial receptor TREM2 increase risk of Alzheimer’s disease (AD), and the soluble ectodomain of wild-type TREM2 (sTREM2) appears to protect in vivo, but the underlying mechanisms are unclear. We show that Aβ oligomers bind to TREM2, inducing shedding of sTREM2. Wild-type sTREM2 inhibits Aβ oligomerization, fibrillization and neurotoxicity, and disaggregates preformed Aβ oligomers and protofibrils. In contrast, the R47H AD-risk variant of sTREM2 is less able to bind and disaggregate oligomeric Aβ, but rather promotes Aβ protofibril formation and neurotoxicity. Thus, in addition to mediating phagocytosis, wild-type TREM2 may protect against amyloid pathology by Aβ-induced release of sTREM2 that blocks Aβ aggregation and neurotoxicity; while R47H sTREM2 promotes Aβ aggregation into neurotoxic forms, which may explain why the R47H variant gene increases AD risk several fold.

Germline Sequencing DNA Repair Genes in 5545 Men With Aggressive and Nonaggressive Prostate Cancer

Background There is an urgent need to identify factors specifically associated with aggressive prostate cancer (PCa) risk. We investigated whether rare pathogenic, likely pathogenic, or deleterious (P/LP/D) germline variants in DNA repair genes are associated with aggressive PCa risk in a case-case study of aggressive vs nonaggressive disease. Methods Participants were 5545 European-ancestry men, including 2775 nonaggressive and 2770 aggressive PCa cases, which included 467 metastatic cases (16.9%). Samples were assembled from 12 international studies and germline sequenced together. Rare (minor allele frequency < 0.01) P/LP/D variants were analyzed for 155 DNA repair genes. We compared single variant, gene-based, and DNA repair pathway-based burdens by disease aggressiveness. All statistical tests are 2-sided. Results BRCA2 and PALB2 had the most statistically significant gene-based associations, with 2.5% of aggressive and 0.8% of nonaggressive cases carrying P/LP/D BRCA2 alleles (odds ratio [OR] = 3.19, 95% confidence interval [CI] = 1.94 to 5.25, P = 8.58 × 10-7) and 0.65% of aggressive and 0.11% of nonaggressive cases carrying P/LP/D PALB2 alleles (OR = 6.31, 95% CI = 1.83 to 21.68, P = 4.79 × 10-4). ATM had a nominal association, with 1.6% of aggressive and 0.8% of nonaggressive cases carrying P/LP/D ATM alleles (OR = 1.88, 95% CI = 1.10 to 3.22, P = .02). In aggregate, P/LP/D alleles within 24 literature-curated candidate PCa DNA repair genes were more common in aggressive than nonaggressive cases (carrier frequencies = 14.2% vs 10.6%, respectively; P = 5.56 × 10-5). However, this difference was non-statistically significant (P = .18) on excluding BRCA2, PALB2, and ATM. Among these 24 genes, P/LP/D carriers had a 1.06-year younger diagnosis age (95% CI = -1.65 to 0.48, P = 3.71 × 10-4). Conclusions Risk conveyed by DNA repair genes is largely driven by rare P/LP/D alleles within BRCA2, PALB2, and ATM. These findings support the importance of these genes in both screening and disease management considerations.

Identifying digenic disease genes using machine learning in the undiagnosed diseases network

ABSTRACTRare diseases affect hundreds of millions of people worldwide, and diagnosing their genetic causes is challenging. The Undiagnosed Diseases Network (UDN) was formed in 2014 to identify and treat novel rare genetic diseases, and despite many successes, more than half of UDN patients remain undiagnosed. The central hypothesis of this work is that many unsolved rare genetic disorders are caused by multiple variants in more than one gene. However, given the large number of variants in each individual genome, experimentally evaluating even just pairs of variants for potential to cause disease is currently infeasible. To address this challenge, we developed DiGePred, a random forest classifier for identifying candidate digenic disease gene pairs using features derived from biological networks, genomics, evolutionary history, and functional annotations. We trained the DiGePred classifier using DIDA, the largest available database of known digenic disease causing gene pairs, and several sets of non-digenic gene pairs, including variant pairs derived from unaffected relatives of UDN patients. DiGePred achieved high precision and recall in cross-validation and on a held out test set (PR area under the curve >77%), and we further demonstrate its utility using novel digenic pairs from the recent literature. In contrast to other approaches, DiGePred also appropriately controls the number of false positives when applied in realistic clinical settings like the UDN. Finally, to facilitate the rapid screening of variant gene pairs for digenic disease potential, we freely provide the predictions of DiGePred on all human gene pairs. Our work facilitates the discovery of genetic causes for rare non-monogenic diseases by providing a means to rapidly evaluate variant gene pairs for the potential to cause digenic disease.