The MOV10 RNA helicase is a dosage-dependent host restriction factor for LINE1 retrotransposition in mice.

Transposable elements constitute nearly half of the mammalian genome and play important roles in genome evolution. While a multitude of both transcriptional and post-transcriptional mechanisms exist to silence transposable elements, control of transposition in vivo remains poorly understood. MOV10, an RNA helicase, is an inhibitor of mobilization of retrotransposons and retroviruses in cell culture assays. Here we report that MOV10 restricts LINE1 retrotransposition in mice. Although MOV10 is broadly expressed, its loss causes only incomplete penetrance of embryonic lethality, and the surviving MOV10-deficient mice are healthy and fertile. Biochemically, MOV10 forms a complex with UPF1, a key component of the nonsense-mediated mRNA decay pathway, and primarily binds to the 3' UTR of somatically expressed transcripts in testis. Consequently, loss of MOV10 results in an altered transcriptome in testis. Analyses using a LINE1 reporter transgene reveal that loss of MOV10 leads to increased LINE1 retrotransposition in somatic and reproductive tissues from both embryos and adult mice. Moreover, the degree of LINE1 retrotransposition inhibition is dependent on the Mov10 gene dosage. Furthermore, MOV10 deficiency reduces reproductive fitness over successive generations. Our findings demonstrate that MOV10 attenuates LINE1 retrotransposition in a dosage-dependent manner in mice.

The SAP domain of Ku facilitates its efficient loading onto DNA ends.

The evolutionarily conserved DNA repair complex Ku serves as the primary sensor of free DNA ends in eukaryotic cells. Its rapid association with DNA ends is crucial for several cellular processes, including non-homologous end joining (NHEJ) DNA repair and telomere protection. In this study, we conducted a transient kinetic analysis to investigate the impact of the SAP domain on individual phases of the Ku-DNA interaction. Specifically, we examined the initial binding, the subsequent docking of Ku onto DNA, and sliding of Ku along DNA. Our findings revealed that the C-terminal SAP domain of Ku70 facilitates the initial phases of the Ku-DNA interaction but does not affect the sliding process. This suggests that the SAP domain may either establish the first interactions with DNA, or stabilize these initial interactions during loading. To assess the biological role of the SAP domain, we generated Arabidopsis plants expressing Ku lacking the SAP domain. Intriguingly, despite the decreased efficiency of the ΔSAP Ku complex in loading onto DNA, the mutant plants exhibited full proficiency in classical NHEJ and telomere maintenance. This indicates that the speed with which Ku loads onto telomeres or DNA double-strand breaks is not the decisive factor in stabilizing these DNA structures.

Hormone-regulated expansins: Expression, localization, and cell wall biomechanics in Arabidopsis root growth.

Expansins facilitate cell expansion by mediating pH-dependent cell wall (CW) loosening. However, the role of expansins in controlling CW biomechanical properties in specific tissues and organs remains elusive. We monitored hormonal responsiveness and spatial specificity of expression and localization of expansins predicted to be the direct targets of cytokinin signaling in Arabidopsis (Arabidopsis thaliana). We found EXPANSIN1 (EXPA1) homogenously distributed throughout the CW of columella/lateral root cap, while EXPA10 and EXPA14 localized predominantly at 3-cell boundaries in the epidermis/cortex in various root zones. EXPA15 revealed cell-type-specific combination of homogenous vs. 3-cell boundaries localization. By comparing Brillouin frequency shift and AFM-measured Young's modulus, we demonstrated Brillouin light scattering (BLS) as a tool suitable for non-invasive in vivo quantitative assessment of CW viscoelasticity. Using both BLS and AFM, we showed that EXPA1 overexpression upregulated CW stiffness in the root transition zone (TZ). The dexamethasone-controlled EXPA1 overexpression induced fast changes in the transcription of numerous CW-associated genes, including several EXPAs and XYLOGLUCAN:XYLOGLUCOSYL TRANSFERASEs (XTHs), and associated with rapid pectin methylesterification determined by in situ Fourier-transform infrared spectroscopy in the root TZ. The EXPA1-induced CW remodeling is associated with the shortening of the root apical meristem, leading to root growth arrest. Based on our results, we propose that expansins control root growth by a delicate orchestration of CW biomechanical properties, possibly regulating both CW loosening and CW remodeling.

Catsnap: a user-friendly algorithm for determining the conservation of protein variants reveals extensive parallelisms in the evolution of alternative splicing.

Understanding the evolutionary conservation of complex eukaryotic transcriptomes significantly illuminates the physiological relevance of alternative splicing (AS). Examining the evolutionary depth of a given AS event with ordinary homology searches is generally challenging and time-consuming. Here, we present Catsnap, an algorithmic pipeline for assessing the conservation of putative protein isoforms generated by AS. It employs a machine learning approach following a database search with the provided pair of protein sequences. We used the Catsnap algorithm for analyzing the conservation of emerging experimentally characterized alternative proteins from plants and animals. Indeed, most of them are conserved among other species. Catsnap can detect the conserved functional protein isoforms regardless of the AS type by which they are generated. Notably, we found that while the primary amino acid sequence is maintained, the type of AS determining the inclusion or exclusion of protein regions varies throughout plant phylogenetic lineages in these proteins. We also document that this phenomenon is less seen among animals. In sum, our algorithm highlights the presence of unexpectedly frequent hotspots where protein isoforms recurrently arise to carry physiologically relevant functions. The user web interface is available at https://catsnap.cesnet.cz/.

DIANA-miRGen v4: indexing promoters and regulators for more than 1500 microRNAs.

Deregulation of microRNA (miRNA) expression plays a critical role in the transition from a physiological to a pathological state. The accurate miRNA promoter identification in multiple cell types is a fundamental endeavor towards understanding and characterizing the underlying mechanisms of both physiological as well as pathological conditions. DIANA-miRGen v4 (www.microrna.gr/mirgenv4) provides cell type specific miRNA transcription start sites (TSSs) for over 1500 miRNAs retrieved from the analysis of >1000 cap analysis of gene expression (CAGE) samples corresponding to 133 tissues, cell lines and primary cells available in FANTOM repository. MiRNA TSS locations were associated with transcription factor binding site (TFBSs) annotation, for >280 TFs, derived from analyzing the majority of ENCODE ChIP-Seq datasets. For the first time, clusters of cell types having common miRNA TSSs are characterized and provided through a user friendly interface with multiple layers of customization. DIANA-miRGen v4 significantly improves our understanding of miRNA biogenesis regulation at the transcriptional level by providing a unique integration of high-quality annotations for hundreds of cell specific miRNA promoters with experimentally derived TFBSs.

The RNA helicase MOV10L1 binds piRNA precursors to initiate piRNA processing.

Piwi-piRNA (Piwi-interacting RNA) ribonucleoproteins (piRNPs) enforce retrotransposon silencing, a function critical for preserving the genome integrity of germ cells. The molecular functions of most of the factors that have been genetically implicated in primary piRNA biogenesis are still elusive. Here we show that MOV10L1 exhibits 5'-to-3' directional RNA-unwinding activity in vitro and that a point mutation that abolishes this activity causes a failure in primary piRNA biogenesis in vivo. We demonstrate that MOV10L1 selectively binds piRNA precursor transcripts and is essential for the generation of intermediate piRNA processing fragments that are subsequently loaded to Piwi proteins. Multiple analyses suggest an intimate coupling of piRNA precursor processing with elements of local secondary structures such as G quadruplexes. Our results support a model in which MOV10L1 RNA helicase activity promotes unwinding and funneling of the single-stranded piRNA precursor transcripts to the endonuclease that catalyzes the first cleavage step of piRNA processing.

ENNGene: an Easy Neural Network model building tool for Genomics.

BACKGROUND: The recent big data revolution in Genomics, coupled with the emergence of Deep Learning as a set of powerful machine learning methods, has shifted the standard practices of machine learning for Genomics. Even though Deep Learning methods such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are becoming widespread in Genomics, developing and training such models is outside the ability of most researchers in the field. RESULTS: Here we present ENNGene-Easy Neural Network model building tool for Genomics. This tool simplifies training of custom CNN or hybrid CNN-RNN models on genomic data via an easy-to-use Graphical User Interface. ENNGene allows multiple input branches, including sequence, evolutionary conservation, and secondary structure, and performs all the necessary preprocessing steps, allowing simple input such as genomic coordinates. The network architecture is selected and fully customized by the user, from the number and types of the layers to each layer's precise set-up. ENNGene then deals with all steps of training and evaluation of the model, exporting valuable metrics such as multi-class ROC and precision-recall curve plots or TensorBoard log files. To facilitate interpretation of the predicted results, we deploy Integrated Gradients, providing the user with a graphical representation of an attribution level of each input position. To showcase the usage of ENNGene, we train multiple models on the RBP24 dataset, quickly reaching the state of the art while improving the performance on more than half of the proteins by including the evolutionary conservation score and tuning the network per protein. CONCLUSIONS: As the role of DL in big data analysis in the near future is indisputable, it is important to make it available for a broader range of researchers. We believe that an easy-to-use tool such as ENNGene can allow Genomics researchers without a background in Computational Sciences to harness the power of DL to gain better insights into and extract important information from the large amounts of data available in the field.

Sequence-dependent but not sequence-specific piRNA adhesion traps mRNAs to the germ plasm.

The conserved Piwi family of proteins and piwi-interacting RNAs (piRNAs) have a central role in genomic stability, which is inextricably linked to germ-cell formation, by forming Piwi ribonucleoproteins (piRNPs) that silence transposable elements. In Drosophila melanogaster and other animals, primordial germ-cell specification in the developing embryo is driven by maternal messenger RNAs and proteins that assemble into specialized messenger ribonucleoproteins (mRNPs) localized in the germ (pole) plasm at the posterior of the oocyte. Maternal piRNPs, especially those loaded on the Piwi protein Aubergine (Aub), are transmitted to the germ plasm to initiate transposon silencing in the offspring germ line. The transport of mRNAs to the oocyte by midoogenesis is an active, microtubule-dependent process; mRNAs necessary for primordial germ-cell formation are enriched in the germ plasm at late oogenesis via a diffusion and entrapment mechanism, the molecular identity of which remains unknown. Aub is a central component of germ granule RNPs, which house mRNAs in the germ plasm, and interactions between Aub and Tudor are essential for the formation of germ granules. Here we show that Aub-loaded piRNAs use partial base-pairing characteristics of Argonaute RNPs to bind mRNAs randomly in Drosophila, acting as an adhesive trap that captures mRNAs in the germ plasm, in a Tudor-dependent manner. Notably, germ plasm mRNAs in drosophilids are generally longer and more abundant than other mRNAs, suggesting that they provide more target sites for piRNAs to promote their preferential tethering in germ granules. Thus, complexes containing Tudor, Aub piRNPs and mRNAs couple piRNA inheritance with germline specification. Our findings reveal an unexpected function for piRNP complexes in mRNA trapping that may be generally relevant to the function of animal germ granules.

Economic crisis in Greece: The invisible enemy of blood donation or not?

INTRODUCTION: Volunteering presupposes having free time and refers to the provision of services without the motivation of material reward, for the benefit of society. In this study, we aimed to provide insight into the impact of economic crisis on blood donors and their motivation to donate blood during that period. STUDY DESIGN AND METHODS: We asked blood donors about their blood donation activity and motivation to donate using a standardized, anonymous questionnaire (n = 3000). Descriptive analysis was performed for the consideration of donor turnout during this economic period. The results were analyzed using the χ2 test and Spearman's correlation coefficient. RESULTS: Regarding gender, 68.2% were males, while 31.8% were females. Most blood donors donated voluntarily (75.8%) and only 24.2% were replacement or family blood donors. The economic crisis has affected the inhabitants of Athens more than the inhabitants of the province (χ2 = 9.910,p = 0.007). The influence of economic crisis on the regular blood donors' quality of life was greater than the non-regular donors (χ2 = 16.227,p < 0.001). According to our results, the economic crisis reduced the quality of life, but it did not affect the frequency of blood donations in a percentage of 87,3%. Not any significant difference was found between employment status, economic crisis and blood donation. CONCLUSION: Although the economic crisis has affected the lives of blood donors, it does not seem to affect the frequency of blood donation. We suggest that blood collection services should consider specialist campaigns that focus on the altruistic motivation of donors during an economic crisis.

A MicroRNA precursor surveillance system in quality control of MicroRNA synthesis.

MicroRNAs (miRNAs) are essential for regulation of gene expression. Though numerous miRNAs have been identified by high-throughput sequencing, few precursor miRNAs (pre-miRNAs) are experimentally validated. Here we report a strategy for constructing high-throughput sequencing libraries enriched for full-length pre-miRNAs. We find widespread and extensive uridylation of Argonaute (Ago)-bound pre-miRNAs, which is primarily catalyzed by two terminal uridylyltransferases: TUT7 and TUT4. Uridylation by TUT7/4 not only polishes pre-miRNA 3' ends, but also facilitates their degradation by the exosome, preventing clogging of Ago with defective species. We show that the exosome exploits distinct substrate preferences of DIS3 and RRP6, its two catalytic subunits, to distinguish productive from defective pre-miRNAs. Furthermore, we identify a positive feedback loop formed by the exosome and TUT7/4 in triggering uridylation and degradation of Ago-bound pre-miRNAs. Our study reveals a pre-miRNA surveillance system that comprises TUT7, TUT4, and the exosome in quality control of miRNA synthesis.

Kc167, a widely used Drosophila cell line, contains an active primary piRNA pathway.

PIWI family proteins bind to small RNAs known as PIWI-interacting RNAs (piRNAs) and play essential roles in the germline by silencing transposons and by promoting germ cell specification and function. Here we report that the widely used Kc167 cell line, derived from Drosophila melanogaster embryos, expresses piRNAs that are loaded to Aub and Piwi. Kc167 piRNAs are produced by a canonical, primary piRNA biogenesis pathway, from phased processing of precursor transcripts by the Zuc endonuclease, Armi helicase, and dGasz mitochondrial scaffold protein. Kc167 piRNAs derive from cytoplasmic transcripts, notably tRNAs and mRNAs, and their abundance correlates with that of parent transcripts. The expression of Aub is robust in Kc167, that of Piwi is modest, while Ago3 is undetectable, explaining the lack of transposon-related piRNA amplification by the Aub-Ago3, ping-pong mechanism. We propose that the default state of the primary piRNA biogenesis machinery is random transcript sampling to allow generation of piRNAs from any transcript, including newly acquired retrotransposons. This state is unmasked in Kc167, likely because they do not express piRNA cluster transcripts in sufficient amounts and do not amplify transposon piRNAs. We use Kc167 to characterize an inactive isoform of Aub protein. Since most Kc167 piRNAs are genic, they can be mapped uniquely to the genome, facilitating computational analyses. Furthermore, because Kc167 is a widely used and well-characterized cell line that is easily amenable to experimental manipulations, we expect that it will serve as an excellent system to study piRNA biogenesis and piRNA-related factors.

CLIPSeqTools--a novel bioinformatics CLIP-seq analysis suite.

Immunoprecipitation of RNA binding proteins (RBPs) after in vivo crosslinking, coupled with sequencing of associated RNA footprints (HITS-CLIP, CLIP-seq), is a method of choice for the identification of RNA targets and binding sites for RBPs. Compared with RNA-seq, CLIP-seq analysis is widely diverse and depending on the RBPs that are analyzed, the approaches vary significantly, necessitating the development of flexible and efficient informatics tools. In this study, we present CLIPSeqTools, a novel, highly flexible computational suite that can perform analysis from raw sequencing data with minimal user input. It contains a wide array of tools to provide an in-depth view of CLIP-seq data sets. It supports extensive customization and promotes improvization, a critical virtue, since CLIP-seq analysis is rarely well defined a priori. To highlight CLIPSeqTools capabilities, we used the suite to analyze Ago-miRNA HITS-CLIP data sets that we prepared from human brains.

FUS regulates genes coding for RNA-binding proteins in neurons by binding to their highly conserved introns.

Dominant mutations and mislocalization or aggregation of Fused in Sarcoma (FUS), an RNA-binding protein (RBP), cause neuronal degeneration in Amyotrophic Lateral Sclerosis (ALS) and Frontotemporal Lobar Degeneration (FTLD), two incurable neurological diseases. However, the function of FUS in neurons is not well understood. To uncover the impact of FUS in the neuronal transcriptome, we used high-throughput sequencing of immunoprecipitated and cross-linked RNA (HITS-CLIP) of FUS in human brains and mouse neurons differentiated from embryonic stem cells, coupled with RNA-seq and FUS knockdowns. We report conserved neuronal RNA targets and networks that are regulated by FUS. We find that FUS regulates splicing of genes coding for RBPs by binding to their highly conserved introns. Our findings have important implications for understanding the impact of FUS in neurodegenerative diseases and suggest that perturbations of FUS can impact the neuronal transcriptome via perturbations of RBP transcripts.

Mitochondrial protein BmPAPI modulates the length of mature piRNAs.

PIWI proteins and their associated PIWI-interacting RNAs (piRNAs) protect genome integrity by silencing transposons in animal germlines. The molecular mechanisms and components responsible for piRNA biogenesis remain elusive. PIWI proteins contain conserved symmetrical dimethylarginines (sDMAs) that are specifically targeted by TUDOR domain-containing proteins. Here we report that the sDMAs of PIWI proteins play crucial roles in PIWI localization and piRNA biogenesis in Bombyx mori-derived BmN4 cells, which harbor fully functional piRNA biogenesis machinery. Moreover, RNAi screenings for Bombyx genes encoding TUDOR domain-containing proteins identified BmPAPI, a Bombyx homolog of Drosophila PAPI, as a factor modulating the length of mature piRNAs. BmPAPI specifically recognized sDMAs and interacted with PIWI proteins at the surface of the mitochondrial outer membrane. BmPAPI depletion resulted in 3'-terminal extensions of mature piRNAs without affecting the piRNA quantity. These results reveal the BmPAPI-involved piRNA precursor processing mechanism on mitochondrial outer membrane scaffolds.

TarBase 6.0: capturing the exponential growth of miRNA targets with experimental support.

As the relevant literature and the number of experiments increase at a super linear rate, databases that curate and collect experimentally verified microRNA (miRNA) targets have gradually emerged. These databases attempt to provide efficient access to this wealth of experimental data, which is scattered in thousands of manuscripts. Aim of TarBase 6.0 (http://www.microrna.gr/tarbase) is to face this challenge by providing a significant increase of available miRNA targets derived from all contemporary experimental techniques (gene specific and high-throughput), while incorporating a powerful set of tools in a user-friendly interface. TarBase 6.0 hosts detailed information for each miRNA-gene interaction, ranging from miRNA- and gene-related facts to information specific to their interaction, the experimental validation methodologies and their outcomes. All database entries are enriched with function-related data, as well as general information derived from external databases such as UniProt, Ensembl and RefSeq. DIANA microT miRNA target prediction scores and the relevant prediction details are available for each interaction. TarBase 6.0 hosts the largest collection of manually curated experimentally validated miRNA-gene interactions (more than 65,000 targets), presenting a 16.5-175-fold increase over other available manually curated databases.

Functional microRNA targets in protein coding sequences.

MOTIVATION: Experimental evidence has accumulated showing that microRNA (miRNA) binding sites within protein coding sequences (CDSs) are functional in controlling gene expression. RESULTS: Here we report a computational analysis of such miRNA target sites, based on features extracted from existing mammalian high-throughput immunoprecipitation and sequencing data. The analysis is performed independently for the CDS and the 3(')-untranslated regions (3(')-UTRs) and reveals different sets of features and models for the two regions. The two models are combined into a novel computational model for miRNA target genes, DIANA-microT-CDS, which achieves higher sensitivity compared with other popular programs and the model that uses only the 3(')-UTR target sites. Further analysis indicates that genes with shorter 3(')-UTRs are preferentially targeted in the CDS, suggesting that evolutionary selection might favor additional sites on the CDS in cases where there is restricted space on the 3(')-UTR.

DIANA-microT Web server upgrade supports Fly and Worm miRNA target prediction and bibliographic miRNA to disease association.

microRNAs (miRNAs) are small endogenous RNA molecules that are implicated in many biological processes through post-transcriptional regulation of gene expression. The DIANA-microT Web server provides a user-friendly interface for comprehensive computational analysis of miRNA targets in human and mouse. The server has now been extended to support predictions for two widely studied species: Drosophila melanogaster and Caenorhabditis elegans. In the updated version, the Web server enables the association of miRNAs to diseases through bibliographic analysis and provides insights for the potential involvement of miRNAs in biological processes. The nomenclature used to describe mature miRNAs along different miRBase versions has been extensively analyzed, and the naming history of each miRNA has been extracted. This enables the identification of miRNA publications regardless of possible nomenclature changes. User interaction has been further refined allowing users to save results that they wish to analyze further. A connection to the UCSC genome browser is now provided, enabling users to easily preview predicted binding sites in comparison to a wide array of genomic tracks, such as single nucleotide polymorphisms. The Web server is publicly accessible in www.microrna.gr/microT-v4.

Using Attribution Sequence Alignment to Interpret Deep Learning Models for miRNA Binding Site Prediction.

MicroRNAs (miRNAs) are small non-coding RNAs that play a central role in the post-transcriptional regulation of biological processes. miRNAs regulate transcripts through direct binding involving the Argonaute protein family. The exact rules of binding are not known, and several in silico miRNA target prediction methods have been developed to date. Deep learning has recently revolutionized miRNA target prediction. However, the higher predictive power comes with a decreased ability to interpret increasingly complex models. Here, we present a novel interpretation technique, called attribution sequence alignment, for miRNA target site prediction models that can interpret such deep learning models on a two-dimensional representation of miRNA and putative target sequence. Our method produces a human readable visual representation of miRNA:target interactions and can be used as a proxy for the further interpretation of biological concepts learned by the neural network. We demonstrate applications of this method in the clustering of experimental data into binding classes, as well as using the method to narrow down predicted miRNA binding sites on long transcript sequences. Importantly, the presented method works with any neural network model trained on a two-dimensional representation of interactions and can be easily extended to further domains such as protein-protein interactions.

Genomic benchmarks: a collection of datasets for genomic sequence classification.

BACKGROUND: Recently, deep neural networks have been successfully applied in many biological fields. In 2020, a deep learning model AlphaFold won the protein folding competition with predicted structures within the error tolerance of experimental methods. However, this solution to the most prominent bioinformatic challenge of the past 50 years has been possible only thanks to a carefully curated benchmark of experimentally predicted protein structures. In Genomics, we have similar challenges (annotation of genomes and identification of functional elements) but currently, we lack benchmarks similar to protein folding competition. RESULTS: Here we present a collection of curated and easily accessible sequence classification datasets in the field of genomics. The proposed collection is based on a combination of novel datasets constructed from the mining of publicly available databases and existing datasets obtained from published articles. The collection currently contains nine datasets that focus on regulatory elements (promoters, enhancers, open chromatin region) from three model organisms: human, mouse, and roundworm. A simple convolution neural network is also included in a repository and can be used as a baseline model. Benchmarks and the baseline model are distributed as the Python package 'genomic-benchmarks', and the code is available at https://github.com/ML-Bioinfo-CEITEC/genomic_benchmarks . CONCLUSIONS: Deep learning techniques revolutionized many biological fields but mainly thanks to the carefully curated benchmarks. For the field of Genomics, we propose a collection of benchmark datasets for the classification of genomic sequences with an interface for the most commonly used deep learning libraries, implementation of the simple neural network and a training framework that can be used as a starting point for future research. The main aim of this effort is to create a repository for shared datasets that will make machine learning for genomics more comparable and reproducible while reducing the overhead of researchers who want to enter the field, leading to healthy competition and new discoveries.

Transfer Learning Allows Accurate RBP Target Site Prediction with Limited Sample Sizes.

RNA-binding proteins are vital regulators in numerous biological processes. Their disfunction can result in diverse diseases, such as cancer or neurodegenerative disorders, making the prediction of their binding sites of high importance. Deep learning (DL) has brought about a revolution in various biological domains, including the field of protein-RNA interactions. Nonetheless, several challenges persist, such as the limited availability of experimentally validated binding sites to train well-performing DL models for the majority of proteins. Here, we present a novel training approach based on transfer learning (TL) to address the issue of limited data. Employing a sophisticated and interpretable architecture, we compare the performance of our method trained using two distinct approaches: training from scratch (SCR) and utilizing TL. Additionally, we benchmark our results against the current state-of-the-art methods. Furthermore, we tackle the challenges associated with selecting appropriate input features and determining optimal interval sizes. Our results show that TL enhances model performance, particularly in datasets with minimal training data, where satisfactory results can be achieved with just a few hundred RNA binding sites. Moreover, we demonstrate that integrating both sequence and evolutionary conservation information leads to superior performance. Additionally, we showcase how incorporating an attention layer into the model facilitates the interpretation of predictions within a biologically relevant context.