Aptamers are short single-stranded DNA, RNA, or synthetic Xeno nucleic acids (XNA) molecules that can interact with corresponding targets with high affinity. Owing to their unique features, including low cost of production, easy chemical modification, high thermal stability, reproducibility, as well as low levels of immunogenicity and toxicity, aptamers can be used as an alternative to antibodies in diagnostics and therapeutics. Systematic evolution of ligands by exponential enrichment (SELEX), an experimental approach for aptamer screening, allows the selection and identification of in vitro aptamers with high affinity and specificity. However, the SELEX process is time consuming and characterization of the representative aptamer candidates from SELEX is rather laborious. Artificial intelligence (AI) could help to rapidly identify the potential aptamer candidates from a vast number of sequences. This review discusses the advancements of AI pipelines/methods, including structure-based and machine/deep learning-based methods, for predicting the binding ability of aptamers to targets. Structure-based methods are the most used in computer-aided drug design. For this part, we review the secondary and tertiary structure prediction methods for aptamers, molecular docking, as well as molecular dynamic simulation methods for aptamer–target binding. We also performed analysis to compare the accuracy of different secondary and tertiary structure prediction methods for aptamers. On the other hand, advanced machine-/deep-learning models have witnessed successes in predicting the binding abilities between targets and ligands in drug discovery and thus potentially offer a robust and accurate approach to predict the binding between aptamers and targets. The research utilizing machine-/deep-learning techniques for prediction of aptamer–target binding is limited currently. Therefore, perspectives for models, algorithms, and implementation strategies of machine/deep learning-based methods are discussed. This review could facilitate the development and application of high-throughput and less laborious in silico methods in aptamer selection and characterization.

Oligonucleotide aptamers are typically identified through a rigorous and time-consuming process known as systematic evolution of ligands by exponential enrichment (SELEX), which requires 20 to 30 iterative rounds to eliminate non/weak binding sequences and enrich tight binding sequences with high affinity. Moreover, inherent experimental biases and non-specific interactions within SELEX could inadvertently exclude high-affinity candidates, leading to a high failure rate. To address these challenges, we proposed DeepAptamer for identifying high-affinity sequences from unenriched early SELEX rounds. As a hybrid neural network model combining convolutional neural networks and bidirectional long short-term memory, DeepAptamer integrated sequence composition and structural features to predict aptamer binding affinities and potential binding motifs. Trained on comprehensive SELEX data, DeepAptamer outperformed existing models in accuracy as substantiated by experimental evidence. More importantly, DeepAptamer effectively identified key nucleotides for target binding. DeepAptamer can efficiently identify high-affinity aptamers against various targets, enhancing its potential to discover promising sequences in initial screening stages and obviating the 20–30 iterative selection rounds required for full enrichment of selection pools. This represented a notable leap forward in aptamer technology, with broad implications for its application across a spectrum of selection targets.

AAV-delivered CRISPR/Cas9 (AAV-CRISPR) has shown promising potentials in preclinical models to efficiently insert therapeutic gene sequences in somatic tissues. However, the AAV input doses required were prohibitively high and posed serious risk of toxicity. Here, we performed AAV-CRISPR mediated homology-independent knock-in at a new target site in mAlb 3’UTR and demonstrated that single dose of AAVs enabled long-term integration and expression of hF9 transgene in both adult and neonatal hemophilia B mice (mF9 −/−), yielding high levels of circulating human Factor IX (hFIX) and stable hemostasis restoration during entire 48-week observation period. Furthermore, we achieved hemostasis correction with a significantly lower AAV dose (2 × 10⁹ vg/neonate and 1 × 10¹⁰ vg/adult mouse) through liver-specific gene knock-in using hyperactive hF9^R338L variant. The plasma antibodies against Cas9 and AAV in the neonatal mice receiving low-dose AAV-CRISPR were negligible, which lent support to the development of AAV-CRISPR mediated somatic knock-in for treating inherited diseases.

Osteoporotic fracture has been regarded as one of the most common bone disorders in the aging society. The natural herb-derived small molecules were revealed as potential treatment approaches for osteoporotic fracture healing. Sesamin is a member of lignan family, which possesses estrogenic activity and plays a significant role in modulating bone homeostasis. Our previous study reported the promoting effect of sesamin on postmenopausal osteoporosis treatment. However, the role of sesamin in osteoporotic fracture healing has not been well studied yet. In this study, we further investigated the putative treatment effect of sesamin on osteoporotic fracture healing. Our study indicated that sesamin could activate bone morphogenetic protein 2 (BMP2) signaling pathway and further promotes in vitro chondrogenesis and angiogenesis activities. This promoting effect was abolished by the treatment of ERα inhibitor. In the osteoporotic bone fracture model, we demonstrated that sesamin markedly improves the callus formation and increases the cartilaginous area at the early-stage, as well as narrowing the fracture gap, and expands callus volume at the late-stage fracture healing site of the OVX mice femur. Furthermore, the angiogenesis at the osteoporotic fracture site was also significantly improved by sesamin treatment. In conclusion, our research illustrated the therapeutic potential and underlying regulation mechanisms of sesamin on osteoporotic fracture healing. Our studies shed light on developing herb-derived bioactive compounds as novel drugs for the treatment of osteoporotic fracture healing, especially for postmenopausal women with low estrogen level.

Osteoporosis, a common systematic bone homeostasis disorder related disease, still urgently needs innovative treatment methods. Several natural small molecules were found to be effective therapeutics in osteoporosis. In the present study, quercetin was screened out from a library of natural small molecular compounds by a dual luciferase reporter system. Quercetin was found to upregulate Wnt/β-catenin while inhibiting NF-κB signaling activities, and thereby rescuing osteoporosis-induced tumor necrosis factor alpha (TNFα) impaired BMSCs osteogenesis. Furthermore, a putative functional lncRNA, Malat1, was shown to be a key mediator in quercetin regulated signaling activities and TNFα-impaired BMSCs osteogenesis, as mentioned above. In an ovariectomy (OVX)-induced osteoporosis mouse model, quercetin administration could significantly rescue OVX-induced bone loss and structure deterioration. Serum levels of Malat1 were also obviously rescued in the OVX model after quercetin treatment. In conclusion, our study demonstrated that quercetin could rescue TNFα-impaired BMSCs osteogenesis in vitro and osteoporosis-induced bone loss in vivo, in a Malat1-dependent manner, suggesting that quercetin may serve as a therapeutic candidate for osteoporosis treatment.

As one of the leading causes of bone fracture in postmenopausal women and in older men, osteoporosis worldwide is attracting more attention in recent decades. Osteoporosis is a common disease mainly resulting from an imbalance of bone formation and bone resorption. Pharmaceutically active compounds that both activate osteogenesis, while repressing osteoclastogenesis hold the potential of being therapeutic medications for osteoporosis treatment. In the present study, sesamin, a bioactive ingredient derived from the seed of Sesamum Indicum, was screened out from a bioactive compound library and shown to exhibit dual-regulating functions on these two processes. Sesamin was demonstrated to promote osteogenesis by upregulating Wnt/β-catenin, while repressing osteoclastogenesis via downregulating NF-κB signaling . Furthermore, DANCR was found to be the key regulator in sesamin-mediated bone formation and resorption . In an ovariectomy (OVX)-induced osteoporotic mouse model, sesamin could rescue OVX-induced bone loss and impairment. The increased serum level of DANCR caused by OVX was also downregulated upon sesamin treatment. In conclusion, our results demonstrate that sesamin plays a dual-functional role in both osteogenesis activation and osteoclastogenesis de-activation in a DANCR-dependent manner, suggesting that it may be a possible medication candidate for osteoporotic patients with elevated DNACR expression levels.