Development and Characterization of Monovalent and Bivalent RNA Aptamers Targeting the Common Pathway of Coagulation

Anticoagulant agents are commonly used drugs to reduce blood coagulation in acute and chronic clinical settings. Many of these drugs target the common pathway of coagulation because it is critical for thrombin generation and disruption of this portion of the pathway has profound effects on the hemostatic process. Currently available drugs for these indications struggle with balancing desired activity with immunogenicity and poor reversibility or irreversibility in the event of hemorrhage. While improvements are being made with the current drugs, new drugs with better therapeutic indices are needed for surgical intervention and chronic indications to prevent thrombosis from occurring.

A class of therapeutics known as aptamers may be able to meet the need for safer anticoagulant agents. Aptamer are short single-stranded RNA oligonucleotides that adopt specific secondary and tertiary structures based upon their sequence. They can be generated to both enzymes and cofactors because they derive their inhibitory activity by blocking protein-protein interactions, rather than active site inhibition. They inhibit their target proteins with a high level of specificity and bind with high affinity to their target. Additionally, they can be reversed using two different antidote approaches, specific oligonucleotide antidotes, or with cationic, “universal” antidotes. The reversal of their activity is both rapid and durable.

The ability of aptamers to be generated to cofactors has been conclusively proven by generating an aptamer targeting the common pathway coagulation cofactor, Factor V (FV). We developed two aptamers with anticoagulant ability that bind to both FV and FVa, the active cofactor. Both aptamers were truncated to smaller functional sizes and had specific point mutant aptamers developed for use as controls. The anticoagulant activity of both aptamer-mutant pairs was characterized using plasma-based clotting assays and whole blood assays. The mechanism of action resulting in anticoagulant activity was assessed for one aptamer. The aptamer was found to block FVa docking to membrane surfaces, a mechanism not previously observed in any of our other anticoagulant aptamers.

To explore development of aptamers as anticoagulant agents targeting the common pathway for surgical interventions, we fused two anticoagulant aptamers targeting Factor X and prothrombin into a single molecule. The bivalent aptamer was truncated to a minimal size while maintaining robust anticoagulant activity. Characterization of the bivalent aptamer in plasma-based clotting assays indicated we had generated a very robust anticoagulant therapeutic. Furthermore, we were able to simultaneously reverse the activity of both aptamers with a single oligonucleotide antidote. This rapid and complete reversal of anticoagulant activity is not available in the antithrombotic agents currently used in surgery.

Predicting Transcript Production Rates in Yeast With Sparse Linear Models

To provide biological insights into transcriptional regulation, a couple of groups have recently presented models relating the promoter DNA-bound transcription factors (TFs) to downstream gene’s mean transcript level or transcript production rates over time. However, transcript production is dynamic in response to changes of TF concentrations over time. Also, TFs are not the only factors binding to promoters; other DNA binding factors (DBFs) bind as well, especially nucleosomes, resulting in competition between DBFs for binding at same genomic location. Additionally, not only TFs, but also some other elements regulate transcription. Within core promoter, various regulatory elements influence RNAPII recruitment, PIC formation, RNAPII searching for TSS, and RNAPII initiating transcription. Moreover, it is proposed that downstream from TSS, nucleosomes resist RNAPII elongation.

Here, we provide a machine learning framework to predict transcript production rates from DNA sequences. We applied this framework in the S. cerevisiae yeast for two scenarios: a) to predict the dynamic transcript production rate during the cell cycle for native promoters; b) to predict the mean transcript production rate over time for synthetic promoters. As far as we know, our framework is the first successful attempt to have a model that can predict dynamic transcript production rates from DNA sequences only: with cell cycle data set, we got Pearson correlation coefficient Cp = 0.751 and coefficient of determination r2 = 0.564 on test set for predicting dynamic transcript production rate over time. Also, for DREAM6 Gene Promoter Expression Prediction challenge, our fitted model outperformed all participant teams, best of all teams, and a model combining best team’s k-mer based sequence features and another paper’s biologically mechanistic features, in terms of all scoring metrics.

Moreover, our framework shows its capability of identifying generalizable fea- tures by interpreting the highly predictive models, and thereby provide support for associated hypothesized mechanisms about transcriptional regulation. With the learned sparse linear models, we got results supporting the following biological insights: a) TFs govern the probability of RNAPII recruitment and initiation possibly through interactions with PIC components and transcription cofactors; b) the core promoter amplifies the transcript production probably by influencing PIC formation, RNAPII recruitment, DNA melting, RNAPII searching for and selecting TSS, releasing RNAPII from general transcription factors, and thereby initiation; c) there is strong transcriptional synergy between TFs and core promoter elements; d) the regulatory elements within core promoter region are more than TATA box and nucleosome free region, suggesting the existence of still unidentified TAF-dependent and cofactor-dependent core promoter elements in yeast S. cerevisiae; e) nucleosome occupancy is helpful for representing +1 and -1 nucleosomes’ regulatory roles on transcription.