Oligonucleotides can be used to modulate gene expression via a range of processes including RNAi, target degradation by RNase H-mediated cleavage, splicing modulation, non-coding RNA inhibition, gene activation and programmed gene editing. As such, these molecules have potential therapeutic applications for myriad indications, with several oligonucleotide drugs recently gaining approval. However, despite recent technological advances, achieving efficient oligonucleotide delivery, particularly to extrahepatic tissues, remains a major translational limitation. Here, we provide an overview of oligonucleotide-based drug platforms, focusing on key approaches - including chemical modification, bioconjugation and the use of nanocarriers - which aim to address the delivery challenge.

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Advances in oligonucleotide drug delivery

Diabetes is one of the fastest growing diseases worldwide, projected to affect 693 million adults by 2045. Devastating macrovascular complications (cardiovascular disease) and microvascular complications (such as diabetic kidney disease, diabetic retinopathy and neuropathy) lead to increased mortality, blindness, kidney failure and an overall decreased quality of life in individuals with diabetes. Clinical risk factors and glycaemic control alone cannot predict the development of vascular complications; numerous genetic studies have demonstrated a clear genetic component to both diabetes and its complications. Early research aimed at identifying genetic determinants of diabetes complications relied on familial linkage analysis suited to strong-effect loci, candidate gene studies prone to false positives, and underpowered genome-wide association studies limited by sample size. The explosion of new genomic datasets, both in terms of biobanks and aggregation of worldwide cohorts, has more than doubled the number of genetic discoveries for both diabetes and diabetes complications. We focus herein on genetic discoveries for diabetes and diabetes complications, empowered primarily through genome-wide association studies, and emphasize the gaps in research for taking genomic discovery to the next level.

Genetics of diabetes mellitus and diabetes complications.

Nanophotonics has garnered intensive attention due to its unique capabilities in molding the flow of light in the subwavelength regime. Metasurfaces (MSs) and photonic integrated circuits (PICs) enable the realization of mass-producible, cost-effective, and highly efficient flat optical components for imaging, sensing, and communications. In order to enable nanophotonics with multi-purpose functionalities, chalcogenide phase-change materials (PCMs) have been introduced as a promising platform for tunable and reconfigurable nanophotonic frameworks. Integration of non-volatile chalcogenide PCMs with unique properties such as drastic optical contrasts, fast switching speeds, and long-term stability grants substantial reconfiguration to the more conventional static nanophotonic platforms. In this review, we discuss state-of-the-art developments as well as emerging trends in tunable MSs and PICs using chalcogenide PCMs. We outline the unique material properties, structural transformation, electro-optic, and thermo-optic effects of well-established classes of chalcogenide PCMs. The emerging deep learning-based approaches for the optimization of reconfigurable MSs and the analysis of light-matter interactions are also discussed. The review is concluded by discussing existing challenges in the realization of adjustable nanophotonics and a perspective on the possible developments in this promising area.

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Tunable nanophotonics enabled by chalcogenide phase-change materials

Cryo-electron microscopy (cryoEM) is becoming the preferred method for resolving protein structures. Low signal-to-noise ratio (SNR) in cryoEM images reduces the confidence and throughput of structure determination during several steps of data processing, resulting in impediments such as missing particle orientations. Denoising cryoEM images can not only improve downstream analysis but also accelerate the time-consuming data collection process by allowing lower electron dose micrographs to be used for analysis. Here, we present Topaz-Denoise, a deep learning method for reliably and rapidly increasing the SNR of cryoEM images and cryoET tomograms. By training on a dataset composed of thousands of micrographs collected across a wide range of imaging conditions, we are able to learn models capturing the complexity of the cryoEM image formation process. The general model we present is able to denoise new datasets without additional training. Denoising with this model improves micrograph interpretability and allows us to solve 3D single particle structures of clustered protocadherin, an elongated particle with previously elusive views. We then show that low dose collection, enabled by Topaz-Denoise, improves downstream analysis in addition to reducing data collection time. We also present a general 3D denoising model for cryoET. Topaz-Denoise and pre-trained general models are now included in Topaz. We expect that Topaz-Denoise will be of broad utility to the cryoEM community for improving micrograph and tomogram interpretability and accelerating analysis. The low signal-to-noise ratio (SNR) in cryoEM images can make the first steps in cryoEM structure determination challenging, particularly for non-globular and small proteins. Here, the authors present Topaz-Denoise, a deep learning based method for micrograph denoising that significantly increases the SNR of cryoEM images and cryoET tomograms, which helps to accelerate the cryoEM pipeline.

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Topaz-Denoise: general deep denoising models for cryoEM and cryoET.

Splicing of genomic exons into mRNAs is a critical prerequisite for the accurate synthesis of human proteins. Genetic variants impacting splicing underlie a substantial proportion of genetic disease, but are challenging to identify beyond those occurring at donor and acceptor dinucleotides. To address this, various methods aim to predict variant effects on splicing. Recently, deep neural networks (DNNs) have been shown to achieve better results in predicting splice variants than other strategies. It has been unclear how best to integrate such process-specific scores into genome-wide variant effect predictors. Here, we use a recently published experimental data set to compare several machine learning methods that score variant effects on splicing. We integrate the best of those approaches into general variant effect prediction models and observe the effect on classification of known pathogenic variants. We integrate two specialized splicing scores into CADD (Combined Annotation Dependent Depletion; cadd.gs.washington.edu
 ), a widely used tool for genome-wide variant effect prediction that we previously developed to weight and integrate diverse collections of genomic annotations. With this new model, CADD-Splice, we show that inclusion of splicing DNN effect scores substantially improves predictions across multiple variant categories, without compromising overall performance. While splice effect scores show superior performance on splice variants, specialized predictors cannot compete with other variant scores in general variant interpretation, as the latter account for nonsense and missense effects that do not alter splicing. Although only shown here for splice scores, we believe that the applied approach will generalize to other specific molecular processes, providing a path for the further improvement of genome-wide variant effect prediction.

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CADD-Splice—improving genome-wide variant effect prediction using deep learning-derived splice scores

Policies aiming to preserve vegetated coastal ecosystems (VCE; tidal marshes, mangroves and seagrasses) to mitigate greenhouse gas emissions require national assessments of blue carbon resources. Here, we present organic carbon (C) storage in VCE across Australian climate regions and estimate potential annual CO2 emission benefits of VCE conservation and restoration. Australia contributes 5–11% of the C stored in VCE globally (70–185 Tg C in aboveground biomass, and 1,055–1,540 Tg C in the upper 1 m of soils). Potential CO2 emissions from current VCE losses are estimated at 2.1–3.1 Tg CO2-e yr-1, increasing annual CO2 emissions from land use change in Australia by 12–21%. This assessment, the most comprehensive for any nation to-date, demonstrates the potential of conservation and restoration of VCE to underpin national policy development for reducing greenhouse gas emissions. Policies aiming to preserve vegetated coastal ecosystems (VCE) to mitigate greenhouse gas emissions require national assessments of blue carbon resources. Here the authors assessed organic carbon storage in VCE across Australian and the potential annual CO2 emission benefits of VCE conservation and find that Australia contributes substantially the carbon stored in VCE globally.

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Australian vegetated coastal ecosystems as global hotspots for climate change mitigation

Estimates of organic carbon (Corg) storage by seagrass meadows which consider inter-habitat variability are essential to understand their potential to sequester carbon dioxide (CO2) and derive robust global and regional estimates of blue carbon storage. In this study, we provide baseline estimates of seagrass extent, and soil Corg stocks and accumulation rates from different seagrass habitats at Rottnest Island (in Amphibolis spp., Posidonia spp., Halophila ovalis and mixed Posidonia/Amphibolis spp. meadows). The Corg stocks in 0.5 m thick seagrass soil deposits, derived from 24 cores, were 5.1+-0.7 kg Corg m-2 (mean+-S.E, ranging from 0.05 to 12.9 kg Corg m-2), accumulating at 23.2+-3.2 g Corg m-2 yr-1 (ranging from 0.22 to 58.9 g Corg m-2 yr-1) over the last decades. There were significant differences in Corg content (%) and stocks (mg Corg cm-3), stable carbon isotope composition of the soil organic matter (d13C) and soil grain size among the seagrass meadows studied, highlighting that biotic and abiotic factors influence seagrass soil Corg storage. Mixed meadows of Posidonia/Amphibolis spp. and monospecific meadows of Posidonia spp. and Amphibolis spp. had the highest Corg stocks (ranging from 6.2 to 6.4 kg Corg m-2), while Halophila spp. meadows had the lowest Corg stocks (1.2+-0.6 kg Corg m-2). We estimated a total soil Corg stock of 48.1±8.5 Gg Corg beneath the 755 ha of Rottnest Island’s seagrasses, and a Corg sequestration capacity of 0.81+-0.06 Gg Corg yr-1, which is equivalent to the sequestration of ~22% of the island’s current annual CO2 emissions. Our results contribute to the existing global dataset on seagrass soil Corg storage and show a significant potential of seagrass to sequester CO2, which are particularly relevant in the context of achieving carbon neutrality through conservation actions in environmentally-marketed, tourist destinations such as Rottnest Island.

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Camila Bedulli

Papers

Australian vegetated coastal ecosystems as global hotspots for climate change mitigation

Contribution of Seagrass Blue Carbon Toward Carbon Neutral Policies in a Touristic and Environmentally-Friendly Island