The apolipoprotein E4 (APOE4) allele represents the single greatest risk factor for late-onset Alzheimer's disease (AD) and accumulating evidence suggests that fragmentation with a toxic-gain of function may be a key molecular step associated with this risk. Recently, we demonstrated strong immunoreactivity of a 151 amino-terminal fragment of apoE4 (E4-fragment) within the nucleus of microglia in the human AD brain. In vitro, this fragment led to toxicity and activation of inflammatory processes in BV2 microglia cells. Additionally, a transcriptome analysis following exogenous treatment of BV2 microglia cells with this E4 fragment led to a > 2-fold up regulation of 1,608 genes, with many genes playing a role in inflammation and microglia activation. To extend these findings, we here report a similar transcriptome analysis in BV2 microglia cells following treatment with full-length ApoE4 (FL-ApoE4). The results indicated that full-length ApoE4 had a very small effect on gene expression compared to the fragment. Only 48 differentially expressed genes (DEGs) were identified (p < 0.05, and greater than 2-fold change). A gene ontology analysis of these DEGs indicated that they are not involved in inflammatory and activation processes, in contrast to the genes up regulated by the E4-fragment. In addition, genes that showed a negative fold-change upon FL-E4 treatment typically showed a strong positive fold-change upon treatment with the fragment (Pearson's r = -0.7). Taken together, these results support the hypothesis that a key step in the conversion of microglia to an activated phenotype is proteolytic cleavage of FL-ApoE4. Therefore, the neutralization of this amino-terminal fragment of ApoE4, specifically, may serve as an important therapeutic strategy in the treatment of AD.

Climate change presents distinct ecological and physiological challenges to plants as extreme climate events become more common. Understanding how species have adapted to drought, especially ecologically important nonmodel organisms, will be crucial to elucidate potential biological pathways for drought adaptation and inform conservation strategies. To aid in genome-to-phenome research, a draft genome was assembled for a diploid individual of Artemisia tridentata subsp. tridentata, a threatened keystone shrub in western North America. While this taxon has few genetic resources available and genetic/genomics work has proven difficult due to genetic heterozygosity in the past, a draft genome was successfully assembled. Aquaporin (AQP) genes and their promoter sequences were mined from the draft genome to predict mechanisms regulating gene expression and generate hypotheses on key genes underpinning drought response. Fifty-one AQP genes were fully assembled within the draft genome. Promoter and phylogenetic analyses revealed putative duplicates of A. tridentata subsp. tridentata AQPs which have experienced differentiation in promoter elements, potentially supporting novel biological pathways. Comparison with nondrought-tolerant congener supports enrichments of AQP genes in this taxon during adaptation to drought stress. Differentiation of promoter elements revealed that paralogues of some genes have evolved to function in different pathways, highlighting these genes as potential candidates for future research and providing critical hypotheses for future genome-to-phenome work.

Ribozymes are RNA molecules that catalyze biochemical reactions. Self-cleaving ribozymes are a common naturally occurring class of ribozymes that catalyze site-specific cleavage of their own phosphodiester backbone. In addition to their natural functions, self-cleaving ribozymes have been used to engineer control of gene expression because they can be designed to alter RNA processing and stability. However, the rational design of ribozyme activity remains challenging, and many ribozyme-based systems are engineered or improved by random mutagenesis and selection (in vitro evolution). Improving a ribozyme-based system often requires several mutations to achieve the desired function, but extensive pairwise and higher-order epistasis prevent a simple prediction of the effect of multiple mutations that is needed for rational design. Recently, high-throughput sequencing-based approaches have produced data sets on the effects of numerous mutations in different ribozymes (RNA fitness landscapes). Here we used such high-throughput experimental data from variants of the CPEB3 self-cleaving ribozyme to train a predictive model through machine learning approaches. We trained models using either a random forest or long short-term memory (LSTM) recurrent neural network approach. We found that models trained on a comprehensive set of pairwise mutant data could predict active sequences at higher mutational distances, but the correlation between predicted and experimentally observed self-cleavage activity decreased with increasing mutational distance. Adding sequences with increasingly higher numbers of mutations to the training data improved the correlation at increasing mutational distances. Systematically reducing the size of the training data set suggests that a wide distribution of ribozyme activity may be the key to accurate predictions. Because the model predictions are based only on sequence and activity data, the results demonstrate that this machine learning approach allows readily obtainable experimental data to be used for RNA design efforts even for RNA molecules with unknown structures. The accurate prediction of RNA functions will enable a more comprehensive understanding of RNA fitness landscapes for studying evolution and for guiding RNA-based engineering efforts.

Ribozymes are RNA molecules that catalyze biochemical reactions. Self-cleaving ribozymes are a common naturally occurring class of ribozymes that catalyze site-specific cleavage of their own phosphodiester backbone. In addition to their natural functions, self-cleaving ribozymes have been used to engineer control of gene expression because they can be designed to alter RNA processing and stability. However, the rational design of ribozyme activity remains challenging, and many ribozyme-based systems are engineered or improved by random mutagenesis and selection (in vitro evolution). Improving a ribozyme-based system often requires several mutations to achieve the desired function, but extensive pairwise and higher-order epistasis prevent a simple prediction of the effect of multiple mutations that is needed for rational design. Recently, high-throughput sequencing-based approaches have produced data sets on the effects of numerous mutations in different ribozymes (RNA fitness landscapes). Here we used such high-throughput experimental data from variants of the CPEB3 self-cleaving ribozyme to train a predictive model through machine learning approaches. We trained models using either a random forest or long short-term memory (LSTM) recurrent neural network approach. We found that models trained on a comprehensive set of pairwise mutant data could predict active sequences at higher mutational distances, but the correlation between predicted and experimentally observed self-cleavage activity decreased with increasing mutational distance. Adding sequences with increasingly higher numbers of mutations to the training data improved the correlation at increasing mutational distances. Systematically reducing the size of the training data set suggests that a wide distribution of ribozyme activity may be the key to accurate predictions. Because the model predictions are based only on sequence and activity data, the results demonstrate that this machine learning approach allows readily obtainable experimental data to be used for RNA design efforts even for RNA molecules with unknown structures. The accurate prediction of RNA functions will enable a more comprehensive understanding of RNA fitness landscapes for studying evolution and for guiding RNA-based engineering efforts.

The study of telomere dynamics in health and aging has been facilitated by qPCR-based telomere length measurements. The widespread application of this approach in vertebrates has been limited by the challenge of developing appropriate reference primers in organisms with genomes that have not been sequenced. Here we report reference primers to highly conserved DNA elements and

Self-cleaving ribozymes are RNA molecules that catalyze the cleavage of their own phosphodiester backbones. These ribozymes are found in all domains of life and are also a tool for biotechnical and synthetic biology applications. Self-cleaving ribozymes are also an important model of sequence to function relationships for RNA because their small size simplifies synthesis of genetic variants and self-cleaving activity is an accessible readout of the functional consequence of the mutation. Here we used a high-throughput experimental approach to determine the relative activity for every possible single and double mutant of five self-cleaving ribozymes. From this data, we comprehensively identified non-additive effects between pairs of mutations (epistasis) for all five ribozymes. We analyzed how changes in activity and trends in epistasis map to the ribozyme structures. The variety of structures studied provided opportunities to observe several examples of common structural elements, and the data was collected under identical experimental conditions to enable direct comparison. Heat-map based visualization of the data revealed patterns indicating structural features of the ribozymes including paired regions, unpaired loops, non-canonical structures and tertiary structural contacts. The data also revealed signatures of functionally critical nucleotides involved in catalysis. The results demonstrate that the data sets provide structural information similar to chemical or enzymatic probing experiments, but with additional quantitative functional information. The large-scale data sets can be used for models predicting structure and function and for efforts to engineer self-cleaving ribozymes.

Telomere length dynamics are an established biomarker of health and ageing in animals. The study of telomeres in numerous species has been facilitated by methods to measure telomere length by real‐time quantitative PCR (qPCR). In this method, telomere length is determined by quantifying the amount of telomeric DNA repeats in a sample and normalizing this to the total amount of genomic DNA. This normalization requires the development of genomic reference primers suitable for qPCR, which remains challenging in nonmodel organism with genomes that have not been sequenced. Here we report reference primers that can be used in qPCR to measure telomere lengths in any vertebrate species. We designed primer pairs to amplify genetic elements that are highly conserved between evolutionarily distant taxa and tested them in species that span the vertebrate tree of life. We report five primer pairs that meet the specificity and reproducibility standards of qPCR. In addition, we demonstrate an approach to choose the best primers for a given species by testing the primers on multiple individuals within a species and then applying an established computational tool. These reference primers can facilitate qPCR‐based telomere length measurements in any vertebrate species of ecological or economic interest.

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