Recently developed methods for video analysis, especially models for pose estimation and behavior classification, are transforming behavioral quantification to be more precise, scalable, and reproducible in fields such as neuroscience and ethology. These tools overcome long-standing limitations of manual scoring of video frames and traditional ‘center of mass’ tracking algorithms to enable video analysis at scale. The expansion of open-source tools for video acquisition and analysis has led to new experimental approaches to understand behavior. Here, we review currently available open-source tools for video analysis and discuss how to set up these methods for labs new to video recording. We also discuss best practices for developing and using video analysis methods, including community-wide standards and critical needs for the open sharing of datasets and code, more widespread comparisons of video analysis methods, and better documentation for these methods especially for new users. We encourage broader adoption and continued development of these tools, which have tremendous potential for accelerating scientific progress in understanding the brain and behavior. Traditional approaches to analyzing video data have involved researchers watching video playback and noting the times and locations of specific events of interest. These analyses are very time-consuming, require expert knowledge in the target species and experimental design, and are prone to user bias . Video rFor example, ‘reward seeking’ has been a popular topic in recent years and is typically measured using beam breaks between response and reward ports located inside an operant arena e.g., . By relyCommercial products  and open-source projects  have emerged in recent years  in terms of pixel locations on frames in the video recordings. Packages for pose estimation provide graphical user interfaces for defining keypoints and the keypoints are then analyzed with video analysis methods. In the example shown in The first step in setting up for video recording is to purchase a camera with an appropriate lens. Researchers should determine if they need precisely timed video frames, for example, for integration with electrical or optical recordings. Inexpensive USB webcams with frame rates of at least 30 fps are suitable for many neuroscience experiments. However, it is important to make sure that each camera is connected to a dedicated USB channel in the computer used for video recording. Webcam cameras can be a challenge to integrate with systems used for behavioral control and electrophysiology or imaging because they lack a means of precisely synchronizing video frames to other related data. As such, the timing of specific behaviors must be based on the animal’s location or an observable event in the video field .For more precise recordings, specialized cameras used in computer vision applications are needed . Power and combined data over Ethernet (GigE PoE) is commonly used as it combines long cable length headroom with joint DC power delivery. Alternatively, USB3 cameras can be used, but have a maximum data cable length of 5 m, although active extender cables are available. Most machine vision cameras (GigE PoE or USB3) have general-purpose input output capabilities that allow for time synchronization of multiple cameras with other laboratory equipment . A single camera running at high resolution or frame rate can quickly saturate a standard 1 Gbit Ethernet link. Therefore, it is important to consider the computer used to collect video data, ensuring that it has a fast processor with multiple cores and perhaps also a GPU, which can aid in handling video compression during data collection and can be used for off-line analysis using pose estimation methods.After choosing a camera, researchers must determine how to save and archive data from their recordings. By default, recorded videos from cameras may be in several formats, such as MP4 (MPEG-4 AVC/H. 264 encoding), MOV (MPEG-4 encoding), and AVI . These formats are generally universal and can be read by a variety of tools. Generally, video data files tend to be large (1 hr of RGB video at 30 Hz with resolution 1000×1000 can be 2–20 GB depending on compression) so data storage solutions for large-scale experiments are crucial. File compression should be evaluated before a system is deployed, as the computer used for video recordings must have sufficient memory (RAM) to remain stable over long recording sessions. In addition to considerations of file formats and codecs, it is important to plan for data storage. Many labs maintain internal lab servers for their video data. Cloud storage is another option to enable sharing. For sharing data publicly, there are a variety of hosting services available, such as the Open Science Foundation, Figshare, and Dryad (see section on ‘Best practices for experimenters and developers’ below for further comments on data archives and sharing).Once cameras and lenses are acquired and data formats and storage resolved, the next question is where to position the cameras relative to the experimental preparation. Occlusions due to obstacles, cables, or conspecifics will have effects on the usability of some video analysis methods. A bottom-up view  works best in an open-field, while a top-down approach can be useful for studies in operant chambers and home cages. Bottom-up views capture behavioral information from the position of the animal’s feet . When muIt is also necessary to think about lighting for the experimental setup. If all or some of the study is to be performed while house lights are off, then infrared (IR) lighting and IR-compatible cameras may be needed. One should consider if diffuse lighting will work or if modifications to eliminate reflective surfaces  are necessary. These can lead to artifacts in video recordings from devices like IR LEDs and other sources of illumination and complicate the training and interpretations of measures obtained with analyses such as pose estimation. For example, it is possible to reduce reflections from surfaces and objects that are in direct line with IR LEDs. For top-down recordings, cage floors can be made from colored materials to provide contrast such as Delrin or pre-anodized aluminum (an option for long-term use) and the metal pans typically used below operant chambers to collect animal waste can be painted with flat black paint. Addressing these issues before beginning an experiment can greatly improve the quality of video recordings.Finally, for some applications, it is necessary to invest time in calibrating the video system. Calibration is often overlooked and not easily accessible in many current software packages. The intrinsic parameters of a camera include the focal length of the lens and if the lens has obvious distortions . Extrinsic parameters also affect the quality of video recordings and are largely due to the camera’s position in the scene. It is fairly easy to calibrate a single camera using a checkerboard or ArUco board. To do so, one sweeps a precalibrated board manually around the field of view of a camera and uses the extracted images to estimate the camera’s intrinsic parameters . This approach can scale easily to cameras with overlapping fields of view but becomes difficult if larger camera networks do not share extrinsic parameters or need to be repeatedly recalibrated . If the environment has enough structure in it, structure from motion can estimate the intrinsic and extrinsic parameters by treating the multiple cameras as an exhaustive sweep of the environment. This process can be fully scripted and automatically performed on a daily basis leading to substantially increased reliability and precision in multi-camera system performance. Several references on these topics include Once video recordings are acquired, the researcher may proceed to setting up their computing environment for pose estimation and tracking. Modern markerless motion capture software tools like DeepLabCut  and SLEAResearchers will need to set up their software environment to be able to install and use pose tracking tools. Most commonly available open-source methods for pose estimation were developed using the Python language. It is highly recommended to make use of ‘environment managers’ such as Anaconda (‘conda’) which enable the creation of isolated installations of Python for each video analysis method of interest. This allows for the methods to be installed with all its dependencies without affecting other Python libraries on the system. Alternatives include Docker, which allows for running an entire virtual machine in isolation. This is done to facilitate the installation of GPU-related dependencies, which may be technically challenging for novice users.keypoint localization within a single image; multi-animal pose estimation refers to; multi-animal pose estimation refers to keypoint localization and part grouping of multiple animals within a single image; and multi-animal pose tracking refers to combined keypoint localization, part grouping, and identification across video frames.Pose tracking methods , part 1 Keypoint localization involves recovering the spatial coordinates of each distinct keypoint. This is normally done by estimating body part confidence maps, that is, image-based representations that encode the probability of the body part being located at each pixel. Recovering the coordinates of each body part is reduced to the task of finding the pixel with highest probability. A key consideration of this task is that the larger the image, the larger the confidence maps. Computer memory requirements can potentially exceed the capacity of most consumer-grade GPUs. This can be compensated by reducing the resolution of the confidence maps, though this comes at the cost of potentially reduced accuracy. Subpixel refinement methods are typically employed to compensate for this, but ultimately confidence map resolution is one of the most impactful choices for achieving reliable keypoint localization.one instance of each keypoint type present in the image, so keypoint localization is the only step strictly required. For multi-animal videos, however, there may be multiple instances of each keypoint type, for example, multiple ‘heads’. Part grouping refers to the task of determining the set of keypoint detections that belong to the same animal within an image. This is often approached in either a bottom-up or top-down fashion. In bottom-up models, all parts are detected, the association between them estimated  problem, where animals are matched across frames based on a model or assumption about motion; or a re-identification (ReID) problem, where distinctive appearance features are used to unambiguously identify an animal. Both MOT and ReID (and hybrids) are available as standalone functionality in open-source tools, as well as part of multi-animal pose tracking packages. While MOT-based approaches can function on videos of animals with nearly indistinguishable appearances, they are prone to the error propagation issue inherent in methods with temporal dependencies: switching an animal’s identity even once will mean it is wrong for all subsequent frames. This presents a potentially intractable problem for long-term continuous recordings which may be impossible to manually proofread. ReID-like methods circumvent this problem by detecting distinguishing visual features, though this may not be compatible with existing datasets or all experimental paradigms.The single most significant experimental consideration that will affect the identification problem is whether animals can be visually distinguished. A common experimental manipulation aimed at ameliorating this issue is to introduce visual markers to aid in unique identification of animals. This includes techniques such as grouping animals with different fur colors, painting them with non-toxic dyes , or attaTools that are based on deep learning work by training deep neural networks (models) to reproduce human annotations of behavior. Methods that strictly depend on learning from human examples are referred to as fully supervised. In the case of animal pose tracking, these supervisory examples (labels) are provided in the form of images and the coordinates of the keypoints of each animal that can be found in them. Most pose tracking software tools fall within this designation and provide graphical interfaces to facilitate labeling. The usability of these interfaces is a crucial consideration as most of the time spent in setting up a pose tracking system will be devoted to manual labeling. The more examples and the greater their diversity, the better that pose tracking models will perform. Previous work has shown that hundreds to thousands of labeled examples may be required to achieve satisfactory results, with a single example taking as much as 2 min to manually label . To mitiwhich body parts are selected as tracked keypoints. Body parts that are not easily located by the human eye will suffer from labeling inconsistency which may cause inferior overall performance as models struggle to find reliable solutions to detecting them. Sample diversity, on the other hand, refers to the notion that not all labeled examples have equal value when training neural networks. For example, labeling 1000 consecutive frames will ensure that the model is able to track data that looks similar to that segment of time, but will have limited capacity to generalize to data collected in a different session. As a best practice, labels should be sampled from the widest possible set of experimental conditions, time points, and imaging conditions that will be expected to be present in the final dataset.The rule of thumb is that ‘if you can see it, you can track it’, but this aphorism strongly depends on the examples provided to train the model. Important factors to consider in the labeling stage include labeling consistency and sample diversity. Labeling consistency involves minimizing the variability of keypoint placement within and across annotators which helps to ensure that models can learn generalizable rules for keypoint localization. This can be accomplished by formalizing a protocol for labeling, especially for ambiguous cases such as when an animal’s body part is occluded. For example, one convention may be to consistently place a ‘paw’ keypoint at the apex of the visible portion of the body rather than guessing where it may be located beneath an occluding object. Similarly, the principle of consistency should inform Improving the capability of models to generalize to new data with fewer (or zero) labels is a currently active area of research. Techniques such as transfer learning and self-supervised learning aim to reduce the labeling burden by training models on related datasets or tasks. For example, B-KinD  is able Several methods have emerged in recent years for 3D tracking based on pose data. For some applications, it is of interest to track animals in complete 3D space. This affords a more detailed representation of the kinematics by resolving ambiguities inherent in 2D projections – an especially desirable property when studying behaviors that involve significant out-of-plane movement, such as in large arenas or non-terrestrial behaviors.It is important to note that 3D motion capture comes at a significant increase in technical complexity. As discussed above (see ‘Data acquisition’), camera synchronization and calibration are paramount for applications using 3D tracking as the result of this step will inform downstream algorithms as to the relative spatial configuration of the individual cameras. This step may be sensitive to small camera movements that occur during normal operation of behavioral monitoring systems, potentially requiring frequent recalibration. The number and positioning of cameras are also major determinants of 3D motion capture performance, both of which may depend on the specific behavior of interest, arena size and bandwidth, and computing capabilities on the acquisition computer. In some cases, it may be easiest to use mirrors instead of multiple cameras to allow for recording behavior from multiple perspectives.triangulating them into 3D. 2D poses can be detected by training 2D pose models on each camera view independently, or by training a single model on all views, with varying results depending on how different the perspectives are. Once 2D poses can be obtained, methods such as Anipose  or time series from a dimensionality reduction method such as principal component analysis that accounts for the keypoints or the raw video. It is crucial that pose estimation is accurate as the segmentation capabilities of the subsequent methods is bounded by pose tracking quality. Highly noisy key points will drown out biological signals and make the segmentation results hard to interpret, especially for unsupervised methods. Furthermore, identity switches between virtual markers can be catastrophic for multi-animal tracking and segmentation. A summary of methods for behavioral segmentation is provided in Before selecting any approach to segment animal behavior, it is important to first define the desired outcome. If the goal is to identify episodes of well-defined behaviors like rearing or walking, then the most straightforward approach is to use a supervised method. Moreover, it is generally a good starting point to use a supervised learning approach and the outputs of these models can be layered on top of unsupervised models to give them immediate interpretability. One tradeoff, however, is the extensive training datasets that are often required to ensure good supervised segmentation. Such methods can be established quite easily using standard machine learning libraries available for the Python, R, and MATLAB, if one has already experience in building these methods. Alternatively, open-source packages such as SimBA  or MARS Interpreting the lower-dimensional structures in a 2D/3D projection plot can be difficult and it is advised to visualize examples from this projection space. Generative methods like VAME offer the possibility to sample cluster categories from this embedding space to qualitatively check if similar patterns are learned. Another task unsupervised methods are capable of is fingerprinting. Here, the embedding space is used as a signature to discern general changes in phenotypes . An alteFinally, as behavior is highly hierarchically structured, multiple spatio-temporal scales of description may be desired, for example, to account for bouts of locomotion and transitions running to escaping behavior . It is pHaving described how to set up and use video recording methods and analysis methods for pose estimation, we would like to close by discussing some best practices in the use and development of methods for video analysis, including recommendations for the open sharing of video data and analysis code.For those using video analysis methods in a laboratory setting, there are several key issues that should be followed as best practices. It is most crucial to develop a means of storing files in a manner in which they can be accessed in the lab, through cloud computing resources, and in data archives. These issues are discussed above in the ‘Hardware and software for data analysis’ section of this paper. Documentation of hardware is also a key best practice. All methods sections of manuscripts that use methods for video analysis should include details on the camera and lens that were used, the locations of and distances from the cameras relative to the behavioral arena, the acquisition rate and image resolution, environmental lighting , properties of the arena .NIH for data sharing, the open sharing of raw and processed videos and pose tracking data is crucial for research reproducibility and also for training new users on video methods. Several groups have created repositories to address this need . With widespread use, these repositories will help new users learn the required methods for data analysis, enable new analyses of existing datasets that could lead to new findings without having to do new experiments, and would enable comparisons of existing and newly developed methods for pose estimation and behavioral quantification. The latter benefit of data sharing could lead to insight into a major open question about methods for animal pose estimation: how choices about the parameters of any method for pose estimation or subsequent analysis impact analysis time, accuracy, and generalizability. Without these resources, it has not been possible to make confident statements about how existing methods compare across a wide range of datasets involving multiple types of research animals and in different experimental contexts. Guidance for how to implement data sharing can be found in several existing efforts of the machine learning community , evaluations on existing datasets and comparisons with baselines is important see , to demoMachine Learning Reproducibility Checklist. It is applicable to any computational model in general. Importantly, the checklist calls for including the range of hyperparameters considered for experiments, mean and variance of results from multiple runs, and an explanation of how samples were allocated for train/validation/test. Further guidance for sharing code is available in this GitHub resource: Publishing Research Code. It provides tips on open-sourcing research code, including specifications of code dependencies, training and evaluation code, and including pre-trained models as part of any code repository. Beyond these resources, we note that there is also a broader definition of reproducibility in that experiments should be robustly reproducible: experimental results should ideally not vary significantly under minor perturbations. For example, even if there are minor variations to lighting or arena size from the original experiments, the video analysis results should not change significantly. A framework to ensure robust reproducibility is currently an open question, but the existing frameworks should facilitate producing the same results under the same experimental conditions. Model interpretability is another important consideration depending on the purpose of the video analysis experiment. Many machine learning models are ‘black box’ models, and not easily interpretable; as such, post hoc explanations may not always be reliable (Third, reproducibility of results is crucial for acceptance of new methods for video analysis within the research community and for research transparency. Guidance for documenting the details of models and algorithms can be obtained from the reliable . One wayreliable  and neurreliable . These mWe hope that our review of the current state of open-source tools for behavioral video analysis will be helpful to the community. We described how to set up video methods in a lab, provided an overview on currently available methods, and provided guidance for best practices in using and developing the methods. As newer tools emerge and more research groups become proficient at using available methods, there is a clear potential for the tools to help with advancing our understanding of the neural basis of behavior.


Zea mays L.), the most important food, feed, and energy crop. Two categories of BNIs are released from maize roots; hydrophobic and hydrophilic BNIs, that determine BNI-capacity in root systems. Zeanone is a recently discovered hydrophobic compound with BNI-activity, released from maize roots. The objectives of this study were to understand/quantify the relationship between zeanone activity and hydrophobic BNI-capacity. We assessed genetic variability among 250 CIMMYT maize lines (CMLs) characterized for hydrophobic BNI-capacity and zeanone activity, towards developing genetic markers linked to this trait in maize. CMLs with high BNI-capacity and ability to release zeanone from roots were identified. GWAS was performed using 27,085 SNPs , and phenotypic information for BNI-capacity and zeanone production from root systems. Eighteen significant markers were identified; three associated with specific BNI-activity (SBNI), four with BNI-activity per plant (BNIPP), another ten were common between SBNI and BNIPP, and one with zeanone release. Further, 30 annotated genes were associated with the significant SNPs; most of these genes are involved in pathways of “biological process”, and one (AMT5) in ammonium regulation in maize roots. Although the inbred lines in this study were not developed for BNI-traits, the identification of markers associated with BNI-capacity suggests the possibility of using these genomic tools in marker-assisted selection to improve hydrophobic BNI-capacity in maize.Biological nitrification inhibition (BNI) is a plant function where root systems release antibiotic compounds (BNIs) specifically aimed at suppressing nitrifiers to limit soil-nitrate formation in the root zone. Little is known about BNI-activity in maize ( Reducing N losses from N fertilizers will improve N uptake and N use efficiency, and can improve crop productivity4. Biological nitrification inhibition (BNI) is one approach to reduce soil N losses and is a plant-based natural process that can reduce N fertilizer demand while sustaining agricultural systems6. Several studies have reported BNI-activity in forage grasses12, legumes15, and cereal crops20, suggesting the possibility of selection and genetic improvement for this trait.Massive application of nitrogen (N) fertilizers to agricultural crops, including maize, has economic and ecological implications ranging from its important contribution to food production and the “green revolution”3. Together, i.e., hydrophobic and hydrophilic BNI-compounds determine the BNI-capacity (i.e. nitrification inhibitory capacity) of root systems. The relative contributions of hydrophobic and hydrophilic BNI-compounds to the BNI-capacity of root systems may vary among plant species. For example, hydrophobic BNIs contribute about 60% of BNI-capacity in sorghum, whereas hydrophilic BNIs contribute nearly 90% of BNI-capacity in wheat3. Root systems of a wild wheat (Leymus raceamosus (Lam.) Tzvelev) have nearly 20-times higher BNI-capacity (i.e. ability to release BNI-activity from roots) compared to most cultivated wheats (Triticum aestivum L.); most of this inhibitory effect comes from hydrophilic-BNIs16. Recently, the chromosome region controlling production of BNIs from roots in Leymus raceamosus was identified and successfully transferred to elite wheat cultivars, without collateral effects on the elite agronomic features21. In rice (Oryza sativa), high BNI-activity was reported and the responsible compound was identified22. Recent reports suggest that in maize, hydrophobic BNI-activity released from root systems accounts for 30–40% of BNI-capacity of root systems; a major portion of this inhibitory effect is attributed to a novel compound, named as ‘zeanone’23. This compound was recognized as a 1,4-naphothoquinone with a redox-active bicyclic structure, similar to the activities in the 1,4-quinone (p-benzoquinone) structure of sorgoleone discovered in sorghum18. Sorgoleone is reported to block both ammonia monooxygenase (AMO) and hydroxylamine oxidoreductase (HAO) pathways18, the two enzymatic pathways that play critical roles in the oxidation of NH4+ to NO2− in Nitrosomonas spp.24.Plant roots release two classes of BNI-compounds, hydrophobic and hydrophilic; this categorization is based on their relative solubility in and affinity for water. Hydrophobic BNI-compounds are mostly confined to and active in the rhizosphere, whereas hydrophilic BNI compounds’ impact may extend beyond the rhizosphere33 and mapping of quantitative trait loci (QTL)40 have enabled applications such as marker assisted selection (MAS) and genomic selection (GS), which are widely used to accelerate and increase the accuracy of maize improvement45. Recently, maize inbred lines developed by the International Maize and Wheat Improvement Center (CIMMYT) were characterized using molecular markers, and GWAS was applied to identify genomic regions associated with resistance to different diseases50, drought and heat stress tolerance52, and adaptation to diverse N environments53. Several genes were identified as linked with biological processes related to nitrogen use efficiency (NUE)55.Identification and characterization of genetic diversity for traits of interest, such as BNI-capacity, is crucial for breeding programs and genetic improvement efforts. Molecular marker methods, including genome-wide association studies (GWAS)Assess the genetic variation among 250 CIMMYT maize lines (CMLs) for hydrophobic BNI-capacity (i.e. hydrophobic BN-compounds released from roots). We studied only hydrophobic BNI-capacity because phenotyping hydrophilic BNI-activity is prohibitively cumbersome (can phenotype only 10–15 lines per year), and because hydrophobic BNI accounts for nearly half of total BNI-capacity in maize.Assess genetic variation for ‘zeanone’  release and its association with hydrophobic BNI-activity.Identify molecular markers and potential candidate genes associated with hydrophobic BNI-capacity and/or zeanone release from maize root systems, paving the way for molecular marker-based strategies to allow the effective development of germplasm able to reduce nitrifier activity and soil-nitrate formation in maize production systems.The objectives of the present study were to:−1 root dwt) and zeanone activity (zeanone levels in root exudate samples) had skewed distributions, with large variation; CVs larger than 100%. BNIPP, SBNI and zeanone had means of 8.10 ATU plant−1, 184.1 ATU g−1 and 59,458 zeanone intensity, respectively. The median was smaller than the mean for each trait, highlighting the skewed distribution of the data . Similarly, 90% of CMLs had SBNI between 0 and 344.5 ATU g−1, while the remaining 10% had values between 364.8 and 1604.4 ATU g−1. Zeanone intensity ranged from 579 to 624,377, and 90% of the lines had values below 164,354. Broad sense heritabilities exceeded 64% for all traits, and were close to 90% for SBNI and BNIPP, indicating strong repeatability for these data. Zeanone intensity per plant did not explain much of the variation (among 250 CMLs) for total hydrophobic BNI-activity, but BNIPP and SBNI were significantly, albeit weakly associated with this trait, R2 7.64% and 8.36% (P < 0.001), respectively , indicating the possibility of existence of zeanone isomers among maize germplasm , specific BNI (SBNI) , 3 (3), 4 (2), 5 (1), 8 (3), 9 (1), and 10 (1); while those significant for BNIPP were located on chromosomes 1 (4 SNPs), 3 (3), 4 (3), 5 (1), 8 (2), and 9 (1). Moreover, ten SNPs had pleiotropic effects for SBNI and BNIPP, indicating potential to select simultaneously for both traits in a breeding program.No linkage disequilibrium (LD) decay distance between GWAS-significant markers was shorter than 1Mbp , downstream (24.24%), upstream 19.05%), or intron variants (16.02%), fewer than 12% of SNPs were annotated as utr_3_prime, intergenic, utr_5_prime, exon, splice_site_region, and splice_site_acceptor genomic regions 16. A wild relative of wheat (Leymus racemosus (Lam.) Tzvelev) released about 20-times more BNI-activity than root system of cultivated wheat16. Three elite sorghum cultivars  differed in their production of sorgoleone, a major hydrophobic BNI component; GDLP 34-5-5-3 and Hybridsorgo showed more sorgoleone release and hydrophobic BNI-capacity than IS4124557. In maize, four BNI-compounds were identified from hydrophobic BNI-activity, of which ‘zeanone’ was the dominant inhibitor23. Here, we have documented substantial genetic variation for hydrophobic BNI-capacity  in a set of 250 CMLs. Consistent with previous research, we found a range of expression for the hydrophobic BNI-components, with only 10% of the CMLs having high BNI-capacity, with maxima reaching 56.35 ATU plant−1 for BNIPP, 1,694 ATU g−1 root dwt for SBNI, and 788,584 zeanone intensity for the released zeanone.Crops vary for BNI-capacity, and many plant species have some levels of BNI-activity in their root systems. BNI-capacity within a crop could be influenced by both genetics and its interaction with the environment59. Nitrogen losses from maize production systems are largely due to nitrifier-activity and rapid generation of soil-nitrates, responsible for low nitrogen recovery and NUE in maize farming61. Use of nitrification inhibitors (such as dicyandiamide) to reduce nitrogen losses in maize farming and to improve soil-NH4+ levels has been recommended63; however, lack of consistency and high costs of synthetic nitrification inhibitors has limited their adoption4. Because plant BNI-activity delivers nitrification inhibitors from root systems directly to soil nitrifier-microsites where nitrifier-activity is concentrated, it is practical and economical6 to genetically exploit BNI-traits to develop maize cultivars that can regulate soil nitrifier-activity and limit soil-nitrate formation. Recently it was reported that zeanone, an important hydrophobic BNI-compound, contributed 28% of the hydrophobic BNI-capacity in maize cultivar cv. ‘Peter Corn’23. For our set of 250 CMLs, however, only 7.6% of the variation for BNIPP was explained by zeanone intensity 64, could include variation for BNI-activity. Interestingly, although we have no explanation, the correlation between zeanone intensity and BNIPP was larger  for quality protein maize (QPM)65 germplasm than non-QPM germplasm . Similar values were found for the correlation between zeanone intensity and SBNI, which was larger for QPM  than for non-QPM lines . Identifying additional compounds contributing to hydrophobic BNI-activity in the rhizosphere may be crucial to understanding these relationships and to design and implement breeding programs for increased BNI-activity of root systems. The hydrophobic BNI-capacity in maize appears to be stronger (5 to 50 ATU plant−1) than reported for wheat (5 to 10 ATU plant−1), and similar to that in sorghum, which has one of the strongest reported hydrophobic BNI-capacities3.The CMLs originated from diverse germplasm sources, and their genetic diversity, which was confirmed previously by molecular markers66. The GWAS analysis using 182,252 SNPs, extracted from another CML panel, identified 5 and 6 significant SNPs for grain yield under optimum and low-N conditions, respectively53. Also related to N use in maize, a GWAS for an IMAS (Improved Maize for African Soils) panel identified 47 genomic regions strongly (2.5 × 10−6 ≤ P ≤ 9.9 × 10−4) associated with grain yield under low N conditions, explaining from 3.4 to 10.6% of the total phenotypic variation67. In this study, we found 18 markers significantly associated with hydrophobic BNI-capacity and one of them linked exclusively to zeanone activity. Understanding the processes causing BNI-effects and identification of genes that increase their expression in maize could be useful for improving performance in regions with low soil fertility or limited access to nitrogen fertilizers. Molecular markers linked to genes or functional loci associated with BNI-capacity could be used in breeding programs, as previously shown for NUE53. Currently, there are no precedents for using molecular markers for BNI in maize or any other crop. Further, we identified 30 candidate genes potentially associated with BNI-activity. The marker 100063204|F|0–36:T > A-36:T > A, associated with SBNI-activity and located on chromosome 10 (133240300 bp), is in the region of Zm00001d025894 , which has been annotated as a protein coding gene involved in the regulation of ammonium transportation through cell membranes. This gene family  was previously reported to be involved in substrate-inducible regulation of ammonium uptake in maize roots68. An appropriate proportion of ammonium may boost maize root proliferation and improve N nutrient uptake, which could have an impact on fertilizer use efficiency69. In addition, ammonium uptake and assimilation were reported to stimulate synthesis and release of BNI-substances from roots of wheat, sorghum and brachiaria70. Some of the genes we identified as possibly associated with BNI-activity have other important functions. For example, the putative gene Zm00001d018004 , was enriched by the SNP (2466232|F|0–67:C > T-67:C > T) with the greatest effect on SBNI and BNIPP simultaneously in this study. Rop1 plays an important role in the adaptation of plants to different environmental situations71, it has also influences on the infection capacity of sugarcane mosaic virus (SCMV) in maize plants; enhancing the infection in maize plants when it is silenced and decreasing it with its overexpression72. Marker 4582532|F|0–24:C > G-24:C > G has been also significant for both, SBNI and BNIPP, and has enriched the putative gene Zm00001d051139 (umc1702), which is involved in the directed movement of nitrogen-containing compounds between cells  is cost- and time-efficient because there is no need to generate specific mapping populations to find associations between genotypic variations and phenotype measurementsThe identification of 18 SNPs associated with hydrophobic BNI-activity, suggests the potential to implement MAS or GS in breeding for nitrification inhibition in maize. In addition, the detection of 6 candidate genes related to NUE shows that our GWAS analysis identified genomic regions associated with N-related traits; with special attention on the SNP that has been locating in the region of AMT5, a protein coding gene involved in the regulation of ammonium transportation. Our results were obtained from a panel of elite inbred lines (CMLs), which was not a mapping population developed for BNI-activity. We are currently developing four doubled haploid (DH) maize populations from crosses between CMLs identified here with contrasting hydrophobic BNI-capacity and zeanone activity. This will be a valuable next step to generate genetic markers for possible use in breeding for enhanced BNI-activity in maize. Another complex area of urgently needed research is to develop and validate high throughput methods to measure hydrophobic and hydrophilic BNI-activity for plants in the field rather than grown in glasshouses.We are in a “discovery phase” for BNI in maize. This study identified significant variation among CMLs for hydrophobic BNI-capacity, with some CMLs having tenfold greater capacity than others, and some CMLs having high levels of zeanone intensity, a major hydrophobic BNI-component released from maize roots. Other key findings from this study include: (1) identification of 18 SNPs associated with hydrophobic BNI-activity, suggesting potential to implement GS or MAS in breeding for nitrification inhibition in maize; (2) the detection of 6 candidate genes related to NUE shows that our GWAS analysis identified potential genomic regions associated with N-related traits; (3) significant markers were observed in genomic regions with low signal and high LD decay distance (> 1Mbp), suggesting the absence of selection for these traits in the CMLs, 4) Exposure of a gene with known functions in the regulation of ammonium transport across the cell membrane. The ongoing creation of doubled haploid lines from crosses of high by low BNI-activity elite inbred lines will allow scientists to develop a specific set of markers that could help in a potential breeding process of BNI in maize.All experimental research was conducted in alignment with CIMMYT policies and procedures (Guidelines and policies – CIMMYT). In our study the sample materials used are inbreed lines developed as international public goods by CIMMYT. These materials were sourced from the CIMMYT genebank, where materials are conserved in trust and shared under the Standard Material Transfer Agreement (SMTA) of the International Treaty on Plant Genetic Resources for Food and Agriculture.www.seedsofdiscovery.org) developed for 475 CIMMYT maize inbred lines (CMLs), available in https://data.cimmyt.org/, to select 250 CMLs with seeds available from CIMMYT’s germplasm bank (see Supplementary Table 77 2-stage-D method in which: (1) the allele frequencies from 169,061 SNP-DarTseq markers with fewer than 50% of missing values, and the modified Rogers genetic distance (MRD)78 between every pair of lines were calculated, and (2) the CMLs were clustered based on the MRD, using Ward’s hierarchical method79. Eleven groups were identified, and their average MRD values were calculated. The number of lines to be selected from each group was defined, proportionally to their diversity, and 1,000 possible subsets were identified by a stratified random sampling procedure. The average diversity value for each candidate subset was calculated and the subset of 250 CMLs with the largest diversity was selected. Minimum, mean, and maximum diversity values were calculated for the whole collection and the selected core subset  with 40 mL aerated distilled water in darkness at 25 °C for 24 h (h). The soaked seeds were wrapped with a wet towel to allow sprouting for another 24 h. The germinated seeds were arranged in a folded cliff of a filter paper, supported by hard plastic plates with a rubber band, and kept in a growth box containing 200 mL of 200 mM CaSO4 aqueous solution at the bottom; the capillary force kept the seeds in the cliff fold of the filter paper moist during the seedling growth period. The seedling-grow-boxes were kept on racks in a growth chamber, with fluorescent lighting with a photoperiod of 13/11 h (day/night). Roots of twelve-day-old seedlings were used for assessing hydrophobic BNI-activity, using two replications of 20 seedlings for each genotype (CML).Hydrophobic BNI-capacity was assessed using a modified method developed originally for sorghumTwenty seedlings were randomly selected, their roots were separated and dipped in 50 mL of 1% acidified DCM  for 60 s (sec) before removing and drying the roots using paper towels. The roots were then oven-dried at 70 °C for 48 h before measuring their dry weight. The DCM wash was filtered and evaporated in vacuum at 40 °C, and the residue was extracted with 20 mL methanol. The methanol extract was evaporated and condensed to 100 mL of methanol; this methanol extract was evaporated to dryness, and extracted with DMSO (dimethylsulfoxide), which was used for determining BNI-activity using bioluminescence assay described below.Nitrosomonas described earlier8. In brief, the recombinant strain of N. europaea expresses luciferases of luxA and luxB genes from the marine bacterium Vibrio harveyi and produces a specific luminescence pattern with two distinct peaks during a 30-s measurement period. The key functional relationship between bioluminescence emission and NO2− production is linear when using the synthetic nitrification inhibitor, allylthiourea (AT) as a standard. The inhibition caused by AT of 0.22 μM in the assay medium, ED80 in bioluminescence and NO2− production, is defined as 1 allylthiourea unit (ATU). The inhibitory activities of root extracts, root exudates and compounds are expressed in ATU based on dose–response standard curve of AT. The biological nitrification inhibitory activities in this study were expressed as per plant (BNIPP [ATU plant−1]), where the obtained inhibitory activity was divided by number of seedlings used, and the specific activity (SBNI [ATU g−1 root dry wt.]), where the inhibitory activity was calculated per mass of root-DCM wash.The methanol extract was evaporated and extracted with 20 µL dimethylsulfoxide (DMSO); 1 µL of this sample is used to determine BNI-activity using the luminescent recombinant 23. Semi-quantification was achieved as follows, an aliquot of the methanol extract (5 uL) was analyzed by Shimadzu LCMS-2020 equipped with DUIS-2020 on a TSKgel Super-ODS . Zeanone was detected with a mass spectrometric detection in positive mode, single ion monitoring with m/z 219 representing zeanone with 0.5 s event time. Semi-quantification was achieved based on the peak area eluted at 528 s, representing zeanone. The peak area indicates relative content of zeanone in the sample, but not the absolute content; therefore, we refer to the peak area as zeanone intensity.The methanol extract was analyzed for zeanone according to Otaka et al.Phenotypic data from 250 CMLs can be found as Supplementary Table 81 used DArTseq technology (www.diversityarrays.com) to characterize the 28,000 CIMMYT maize germplasm collections, including elite inbred lines CMLs82. This method is based on a complexity reduction process where a combination of two enzymes is implemented to generate a genome representation, followed by sequencing of the resulting fragments83. A proprietary analytical pipeline developed by DArT P/L was used to generate allele calls for SNP (co-dominant) and Silico-DArT (dominant) markers from the same assays85. The same pipeline was used to develop a new SNP calling exclusively for the 250 CMLs that were evaluated in this study. A matrix was developed, with DArTseq-SNP markers in the rows, and genotypes in the columns, where the genotypic data scoring represents the dominant homozygous as 0, recessive homozygous as 1 and the heterozygote as 2, according to Petroli and Kilian84. In this study we decided to replace the heterozygote genotypes (score 2) by a missing data score (-) because we are working with homozygous lines and to its small representation (1.6%) in our data set. A set of filtering parameters  was then applied. Marker sequences were blasted against the Zea mays L. reference genome (B73 RefGen_v4), and only SNPs with a unique physical position were considered in further analyses. An additional marker selection criterion was applied, where monomorphic markers and those in which only one CML presented a different allele were removed from analyses. Based on these criteria, from a total of 56,411 SNPs obtained, we selected 27,085 high quality polymorphisms for the GWAS analysis , I is the identity matrix. We considered an additional model in which K is the kinship relationship obtained from marker data. Significance values were adjusted by FDR (−Log10) method87 to reduce the rate of false positives. R-square was calculated for significant markers to evaluate the importance of the marker effect.For the association analysis we included in the analytical model the dimensions of the principal component analysis. We tested the models using 0, 2, 3, 5, and 10 principal dimensions, then for each model we had two different models, one without the genetic relationship between CMLs, and another model considering the genetic relatedness between them. Comparison between models was performed considering the Bayesian Information Criterion (BIC) and q-q plots of p-values. Based on these parameters, the best models did not include principal dimensions for SBNI and BNIPP but included the kinship matrix, while for Zeonane the best model did not include neither. GWAS was performed using the following linear model using Tassel 5To measure the linkage disequilibrium (LD) between GWAS-significant markers we calculated the standardized regression coefficient (SRC) at a maximum distance of 1Mbp. LD decay curves were estimated by chromosome, and the SRC between SNPs were calculated. Within those windows, we traced putative genes that could be related to processes involving the participation of nitrogen as an essential element.88. The maize B73v.4 gene annotation was downloaded as a gff3 file from the Maize Genetics and Genomics Database (MaizeGDB) (https://www.maizegdb.org/assembly). Functional enrichment analysis of the annotated genes was performed using the ClueGO plugin for cytoscape89, and its results were plotted with the R package ggplot290. Mapchart software was used to plot gene distributions across the maize genome91.We used SnpEff software to conduct functional annotations and predictions for the target SNPsSupplementary Figure 1.Supplementary Figure 2.Supplementary Figure 3.Supplementary Figure 4.Supplementary Figure 5.Supplementary Figure 6.Supplementary Table 1.Supplementary Table 2.Supplementary Table 3.Supplementary Table 4.Supplementary Table 5.Supplementary Table 6.Supplementary Table 7.


In mitochondria, protein and ATP synthesis and various metabolic pathways are directly regulated by Mg2+. MRS2, a magnesium channel located in the inner mitochondrial membrane, mediates the influx of Mg2+ into the mitochondrial matrix and regulates Mg2+ homeostasis. Knockdown of MRS2 in human cells leads to reduced uptake of Mg2+ into mitochondria and disruption of the mitochondrial metabolism. Despite the importance of MRS2, the Mg2+ translocation and regulation mechanisms of MRS2 are still unclear. Here, using cryo-EM we report the structures of human MRS2 in the presence and absence of Mg2+ at 2.8 Å and 3.3 Å, respectively. From the homo-pentameric structures, we identify R332 and M336 as major gating residues, which are then tested using mutagenesis and two cellular divalent ion uptake assays. A network of hydrogen bonds is found connecting the gating residue R332 to the soluble domain, potentially regulating the gate. Two Mg2+-binding sites are identified in the MRS2 soluble domain, distinct from the two sites previously reported in CorA, a homolog of MRS2 in prokaryotes. Altogether, this study provides the molecular basis for understanding the Mg2+ translocation and regulatory mechanisms of MRS2.Magnesium ions (Mg 2+ channel MRS2 and identified key residues in gating. This work provides insights into the gating and regulatory mechanisms of MRS2.Magnesium homeostasis is key for cellular functions. Here, authors report structures of human mitochondrial Mg It acts as a cofactor of more than 600 enzymes, including protein kinases, ATPases, exonucleases, and other nucleotide-related enzymes. It has been shown that Mg2+ is involved in various physiological functions such as muscle contraction, vasodilation, neuronal signaling, and immunity3. Intracellular Mg2+ concentrations are tightly regulated and dysregulation of Mg2+ homeostasis is associated with diseases including muscular dysfunction, bone wasting, immunodeficiency, cardiac syndromes, neuronal disorders, obesity, Parkinson’s disease, and cancer6.Magnesium , oxidative metabolism and directly participates in mitochondrial metabolism in form of MgATP2-9. Although the high inner mitochondrial membrane potential (about −180 mV) can theoretically drive a large electrophoretic Mg2+ influx into the mitochondrial matrix, the free Mg2+ concentration in the matrix is found to be comparable to that in the cytosol (~0.8 mM), implying Mg2+ transport is tightly regulated in order to maintain normal mitochondrial physiology10. Most of the Mg2+ influx into mitochondria is mediated by the Mitochondrial RNA Splicing 2 (MRS2) channel, located in the inner mitochondrial membrane14. Knockdown of MRS2 in human cells leads to reduced uptake of Mg2+ into mitochondria, loss of respiratory complex I, disruption of mitochondrial metabolism, and cell death16. A loss of function mutation disrupting MRS2 is also associated with demyelination syndrome in rats17. Recent studies in mice show that MRS2 is required for lactate-mediated Mg2+-uptake in mitochondria18, and knockout of MRS2 causes reprogramming of the metabolism including upregulation of thermogenesis, oxidative phosphorylation and fatty acid catabolism via HIF1α transcriptional regulation19. Despite the physiological implications of MRS2, little is known about its Mg2+ translocation and regulatory mechanisms.The total Mg2+ concentration through a negative feedback mechanism, where at low Mg2+ concentrations, unbinding of Mg2+ ions from the CorA soluble domain favors a series of conductive states, allowing Mg2+ permeation21. MRS2 and CorA only share a sequence identity of ~14%, so insight into MRS2 Mg2+ translocation and regulatory mechanism based on CorA has been limited. Until very recently, the only MRS2 structure reported is the monomeric N-terminal soluble domain of yeast MRS222, lacking the transmembrane domain. A recent study of the N-terminal domain of human MRS2 revealed it forms a dimer, contrasting the pentameric assembly of CorA23. However, the missing structural information of the MRS2 full-length protein including the pore region hampers the understanding of how Mg2+ translocation occurs through the pore and how MRS2 is regulated.MRS2 belongs to the CorA protein superfamily characterized by a highly conserved Glycine-Methionine-Asparagine (GMN) motif in the loop between transmembrane helix 1 and 2. In prokaryotes, CorA regulates the intracellular Mg2+. N-terminal sequencing of MRS2 revealed the mitochondrial transit peptide cleavage site at residue 71. From the homo-pentameric structures, M336 and R332 are identified as key gating residues, which are further tested by mutagenesis, and Mg2+-dependent growth and Ni2+-sensitivity assays. The structures also reveal that MRS2 possesses two Mg2+-binding motifs in the soluble domain between neighboring subunits, which are different from known bacterial CorA structures. Finally, an inter-subunit salt bridge between R116 and E291 in the soluble domain is found. Disruption of this salt bridge leads to an increase in channel activity. Altogether, this study provides insight into how translocation of Mg2+ is mediated via MRS2 and how it is regulated.Here, we use single particle cryo-EM to determine the structures of human MRS2 in the presence and absence of Mg2+ using affinity and size-exclusion chromatography  which suggested a cleavage at residue 50. Here, we used an experimental approach, N-terminal sequencing, of purified human MRS2 and the first amino acid detected is residue 71 starting with threonine  at its N-terminus, which is cleaved after translocation into the inner mitochondrial membrane. As such, we expressed human MRS2 conjugated with a C-terminal FLAG tag in Expi293F cells and purified it in the presence of Mg31 and archael32 members of this protein family and is contrary to the most recent prediction of human MRS2 to form a dimer23. The final 3D reconstruction of MRS2 in the presence of Mg2+ (referred to as MRS2-Mg2+) with C5 symmetry and without symmetry applied (C1) resulted in maps with average resolutions of 2.8 Å and 3.1 Å, respectively, with the highest local resolution estimated to be 2.3 Å   were identified along the pore and two  were found in between neighboring soluble domains in the MRS2-Mg2+ structures , closest to the mitochondrial intermembrane space is coordinated by the highly conserved GMN motif. Mg2+-1 interacts with a ring of backbone carbonyl oxygens of G360 and amine group of N362 with a distance of 3.8 Å and 4.3 Å, respectively, indicating that the Mg2+-1 is in hydrated form36. Mg2+-2, Mg2+-3 and Mg2+-4 are coordinated by a ring of hydroxyl oxygens of T346, carbonyl oxygens of N339, and carboxylic oxygens of D329 with a distance of 5.2 Å, 4.3 Å and 3.5 Å, respectively. A highly negatively charged surface was observed in the loop located at the intermembrane space and the soluble domain in the matrix facing the pore  to control Mg2+ flux40. Interestingly, R332 is conserved among eukaryotes and for example also found in yeast Mrs2 but not in prokaryotic CorA homologs  can only grow either by supplementing the growth medium with high Mg2+ concentrations, or by complementation with a functional Mg2+-transporter/channel. Although expression of the WT HsMRS2 and single gating mutants R332A or M336A is not sufficient to support the growth of the strain without the supplementation of Mg2+, double mutant R332A/M336A complements the growth synergistically  have been identified in the interface of neighboring subunits between their soluble N-terminal domains termed soluble sites 1 and 2  between subunit E and A of MjCorA is close to soluble site 1 in MRS2; however, it is coordinated by E155, Y186, T90 and Q62, which is different to the groups of negatively charged residues  in MRS2  with C5 symmetry and without symmetry applied (C1) were determined at an average resolution of 3.3 Å and 3.6 Å, respectively, with the highest local resolution estimated to be 2.7 Å  have been identified in the soluble domains of adjacent subunits30. Mg2+ ions are coordinated by D89 and D253 at the M1 site, and D253, E88, L12, D175 at the M2 site. It has been proposed that these M1 and M2 sites serve as divalent cation sensors responding to intracellular ion concentrations and modulating channel activation. Unbinding of Mg2+ in the low affinity M1 and M2 sites under low intracellular Mg2+ concentration facilitates channel opening. TmCorA D253K mutation abolished the Mg2+-dependent protease susceptibility28 and Mg2+-dependent channel inhibition20. Previous EPR, cryo-EM and MD-simulation studies of CorA show dramatic movements of the soluble domain under EDTA condition and suggest that the inter-subunit binding of Mg2+ in the soluble domain holds the five subunits in the closed, less flexible conformation44. In the present human MRS2-Mg2+ structure, two inter-subunit Mg2+ ions were also identified but these are in different positions when compared to those in CorA . In general, the reported structures45 are in agreement with ours, with 0.652 Å Cα RMSD between structures in the presence of Mg2+. Two Mg2+ ions are identified along the pore of the reported structures, corresponding to Mg2+−1, 4 in our study. Two additional Mg2+ ions  have been found in both of our structures, coordinated by a ring of hydroxyl oxygens of T346 and carbonyl oxygens of N339 with a distance of 5.2 Å and 4.3 Å, respectively. We reason that the additional two Mg2+ ions identified is likely due to higher Mg2+ concentration (40 mM) in our sample, compared to 20 mM used in the previous publication45. Interestingly, a tentative Cl− has been placed in the R332-ring which is proposed to facilitate Mg2+-permeation by masking the electrostatic repulsion caused by the R332-ring45. Besides, MD-simulation demonstrates that the presence of Cl− around the R332-ring lowers the energy barrier for Mg2+ permeation. When we built the MRS2-Mg2+ and MRS2-EDTA models based on our cryo-EM maps, we also see a consistent density above the R332-ring in both structures  under various conditions have been reported40, other proposed mechanisms of CorA channel opening include helix rotation of TMs displacing the hydrophobic gating residues46, as well as relaxation/bending motion of the soluble domains coupled with TM movement leading to gate opening39  following the protocol described previously47. Cells were harvested 48 h after transfection, and resuspended in Lysis buffer  supplemented with cOmplete, EDTA-free protease inhibitor cocktail (Roche). After cell disruption by sonication on ice, the membrane fraction was collected by ultracentrifugation at 100,000 × g for 1 h at 4 °C. The pelleted membranes were resuspended with Lysis buffer and pelleted again at 100,000 × g for 1 h. The membrane pellets were flash frozen in liquid nitrogen and stored at −80 °C until use. For solubilization, membranes at a protein concentration of ~4 mg/ml were solubilized with 1% (w:v) n-dodecyl-β-d-maltopyranoside (DDM) (Anatrace) and 0.1% cholesteryl hemisuccinate Tris salt (CHS) (Anatrace) for 2 h at 4 °C, followed by another round of ultracentrifugation to remove insoluble material. The supernatant was incubated with ANTI-FLAG M2 Affinity Gel (Millipore) on an end-over-end rotator for 2 h at 4 °C. The MRS2-bound beads were incubated in Lysis buffer containing 1% lauryl maltose-neopentyl glycol (LMNG) for 30 min at 4 °C, followed by extensive washing with Washing buffer (Lysis buffer supplemented with 0.01% LMNG). Proteins were eluted with 0.2 mg/ml 3× FLAG peptide. Eluted fractions were concentrated to ~3 mg/ml using a 50 kDa cut-off centrifugal filter and loaded onto a Superdex 200 Increase 3.2/300 size-exclusion column (Cytiva) with SEC buffer . Peak fractions (~0.5 mg/ml) were collected for cryo-EM grid preparation. For the MRS2 sample in 1 mM EDTA, the sample was prepared in the same procedure except that the sample was dialyzed overnight to remove MgCl2, followed by size-exclusion chromatography with 1 mM EDTA SEC buffer . The protein fractions were resolved and analyzed using 4–12% Bis-Tris gel (Invitrogen) for SDS-PAGE and 4–16% Bis-Tris gel (Invitrogen) for BN-PAGE.Full-length human MRS2 (Uniprot Q9HD23) conjugated with a FLAG tag at the C-terminus was expressed in Expi293F cells . Cells at a density of 2.0−3.0 × 10Purified MRS2 was resolved by SDS-PAGE, followed by transferring to methanol activated PVDF membrane in a XCell Mini-Cell Blot Module (Thermo Fisher) at 30 V for 2 h. The MRS2 band on PVDF was stained with Ponceau S, excised and shipped to Creative Proteomics for N-terminal sequencing via Edman degradation.48 at a nominal magnification of 25,000x, a pixel size of 3.04 Å/px, and defocus range between −1.5 µm and −2.5 µm. A total of 459 images was collected. Image processing was performed using cisTEM v1.0.049. 215,448 particles were picked from the micrographs after CTF estimation, extracted with a box of 108 px (~328 Å) and analyzed by 2D classification.3 µL of MRS2 (0.02 mg/mL) was applied on a glow discharged carbon-coated 400 square mesh copper grid (EMS) and incubate for 1 min. The grid was then blotted by filter paper (Whatman) and washed once with 3 µL of Nano-W negative staining solution (Nanoprobes) followed by incubation with 3 µL of Nano-W for 1 min. The grid was blotted to remove excess staining solution and air-dried by waving. Images were recorded using an FEI Tecnai T20 TEM operated at 200 kV with a direct electron detector K2 Summit (Gatan Inc). Data was collected using SerialEM3 μl purified MRS2 at approximately 0.5 mg/ml was applied to a glow-discharged 400-mesh R 1.2/1.3 Cu grid (Quantifoil). The cryo grids were blotted for 6 s at 4 °C and 95%, and plunge-frozen into liquid ethane using a Leica EM GP2 (Leica) and stored in liquid nitrogen. The grids were screened on an FEI Tecnai T20 TEM before data collection.48 using a Titan Krios  operated at 300 kV and equipped with an energy filter and K3 camera (Gatan Inc.). Movies of 50 frames with a dose of 1 e−/Å2 per frame  were recorded at a nominal magnification of 105,000x, corresponding to a physical pixel size of 0.83 Å/px (super-resolution pixel size 0.415 Å/px) in CDS mode at a dose rate of 10 e-/px/s and a defocus range of −0.7 to −2.0 µm. In total, 3991 and 9656 movies were collected for MRS2-Mg2+ and MRS2-EDTA, respectively . Particles were then subjected to 2D classification to remove junk particles. 573,010 good particles selected from one round of 2D classification was subjected to ab-initio reconstruction (K = 2) and heterogenous refinement (K = 2) with C1 symmetry to further sort particles. Non-uniform refinement was performed with 450,554 selected particles, followed by local motion correction and CTF refinement to correct for beam-tilt, spherical aberrations, and per-particle defocus parameters. Non-uniform refinement with polished particles resulted in maps at 2.8 Å (with C5 symmetry applied) and 3.1 Å (with C1 symmetry applied), according to gold-standard FSC = 0.143 criterion  was performed with the subset of particles after 2D classification, followed by heterogenous refinement (K = 3) using all particles with C1 symmetry was applied to remove junk particles. Particles from one class  were re-extracted at 1x binned pixel size of 0.83 Å and subjected to non-uniform refinement followed by another round of heterogenous refinement (K = 2) to further sort particles. Final non-uniform refinement by using 1,744,117 particles yielded maps at 3.3 Å (with C5 symmetry applied) and 3.6 Å (with C1 symmetry applied) according to gold-standard FSC = 0.143 criterion.2+ (C1 and C5) and MRS2-EDTA (C1 and C5) have been made available and have the following accession codes: EMD−41624, EMD-41629, EMD-41628 and EMD-41630, respectively.The summary of data collection and imaging processing parameters are shown in Supplementary Tables 2+, predicted MRS2 AlphaFold51 structure (AF-Q9HD23-F1) was used as an initial model. The initial model was rigid-body fitted into the local resolution filtered map using UCSF Chimera v.1.1652. The model was then manually rebuilt in COOT v.0.9.753 using the local resolution filtered map, which was generated from refinement with C5 applied. The Mg2+ ions assigned in the pore regions were confirmed in the C1 map. Loop regions  were built with the unsharpened map. Iterative rounds of manual refinement in COOT and real-space refinement in Phenix v.1.20.1-448754 were performed. For the MRS2-EDTA structures, models were generated from multiple rounds of manual refinement and real-space refinements with the final model of MRS2-Mg2+ as initial model. The quality of the model and fit to the density was assessed using MolProbity55 and Phenix54. All structural figures were prepared using UCSF Chimera v.1.1652 or UCSF ChimeraX v.1.456. PDBs have been made available together with the EM maps with the following PDB IDs: 8TUL (MRS2-Mg2+) and 8TUP (MRS2-EDTA).For the atomic model of MRS2-Mg6 cells/ml were transfected with the MRS2-GFP or MRS2(71-443)-GFP encoding pCMV plasmid using polyethylenimine (PEI) following the protocol described previously47. 24 h after transfection, the cells were stained with 100 nM MitoTracker Red (Thermo Fisher Scientific) or 1 µM ER-Tracker Red (Thermo Fisher Scientific) for 30 min at 37°C. The cells were washed twice with PBS and imaged using a Zeiss LSM 880 confocal laser scanning microscope.Expi293F cells at a density of 2.0–3.0 × 102+-auxotrophic E. coli strain , which has been used previously42, was transformed with His-TmCorA, MRS2(71-443)-FLAG, its mutants or without insert in a pET28a backbone. The transformants were inoculated and grown in kanamycin (50 µg/mL) containing LB medium (LBK) supplemented with 100 mM MgSO4, shaking at 250 rpm and 37 °C overnight. The overnight cultures were used to inoculate fresh LBK in a 1:100 ratio and grown until they reached an OD600 of 0.6. The bacterial cultures were then diluted 10-fold serially with LBK medium, spotted onto LBK agar plates containing 0.1 mM IPTG and 0 or 100 mM MgSO4, and incubated at 30 °C for 2 days. Plates were imaged using ChemiDoc MP Imaging System (Bio-rad).The MgTmCorA, MRS2(71-443)-FLAG, its mutants or without insert in a pET28a backbone. The transformants were inoculated and grown in LBK medium, shaking at 250 rpm and 37 °C overnight. The overnight cultures were used to inoculate fresh LBK in a 1:100 ratio and grown until they reached an OD600 of 0.6. The bacterial cultures were then diluted 10-fold serially with LBK medium, spotted onto LBK agar plates containing 0.1 mM IPTG, and 0 or 1.8 mM NiCl2, and incubated at 30 °C overnight. Plates were imaged using ChemiDoc MP Imaging System (Bio-rad).BL21(DE3)  was transformed with His-Further information on research design is available in the Supplementary InformationPeer Review FileReporting SummarySource Data


The aim of this paper is to examine the link between severity in exposure to parental problem drinking in a Swedish national population sample of children aged 15–16 years. Specifically, we assessed whether the risk of poor health, poor relationships and a problematic school situation increase with severity in exposure to parental problem drinking.National population survey from 2017 with a representative sample of 5 576 adolescents born in 2001. Logistic regression models were used to estimate odds ratios (ORs) with 95% confidence intervals (95% CIs). A short version of The Children of Alcoholics Screening Test, CAST-6, was used to identify children with problem-drinking parents. Health status, social relations and school situation were assessed by well-established measures.The risk of having poor health, poor school performance and poor social relations increased with severity of parental problem drinking. The risk was lowest among children least severely affected  and highest among children most severely affected . The risk became lower when adjusting for gender and socioeconomic position but were still higher compared to children without problem-drinking parents. Appropriate screening and intervention programs are necessary for children with problem-drinking parents especially when exposure is severe but also at mild forms of exposure. A number of studies have shown that children of problem-drinking parents have an elevated risk of adverse consequences in many areas, e.g. health problems, bad family and peer relations, substance use problems and poor school adjustment.et al.A study by Raitasalo ,,The aim of this article is therefore to assess the importance of severity in exposure to parental problem drinking in a Swedish national population sample of children aged 15–16 years. More specifically, the number of problems experienced with parental drinking in the measure CAST-6  will be analysed in relation to the risk of poor health, poor relationships and a problematic school situation. A recent study based on this data found that children experiencing at least three of the six problems included in CAST-6  had an elevated risk of poor general health as well psychosomatic problems compared with other children.,,However, no previous study has examined if the risk of problems is influenced by severity in parental problem drinking. As girls and children with a lower socioeconomic position experience more problems related to parental drinking as well as more health problemsThe research question to be addressed in this article is the following:To what extent does the risk of poor health, poor relationships and a problematic school situation increase with severity in exposure to parental problem drinking in a Swedish population sample of children aged 15–16 years?We used baseline data from Futura01, which is a national prospective longitudinal study of ∼5549 Swedish youth born in 2001. The baseline data collection (T1) was carried out during spring 2017 when the respondents attended the 9th grade and were 15–16 years old. The sampling was conducted by Statistics Sweden and includes a random sample of 500 schools. In each school one class was selected randomly and all students in the selected classes were then asked to participate in the study by filling out a paper and pen questionnaire during school hours. After several reminders 343 schools decided to participate, implying a participation rate of 68.8% at the school level. All in all, 6777 questionnaires were returned of which 5576 had filled in the questionnaire and given informed consent to participate in the study. This corresponds to a response-rate of 82.3% at the individual level. Nineteen observations were excluded due to missing answers on individual items and eight respondents had given unrealistic answers. Seventy-two respondents did not answer CAST-6 resulting in 5477 complete cases in the analytical sample. Analysis of the representativeness of the sample showed no significant differences between schools that participated and those that did not. At the school level, meta-data on parental education and immigrant background was compared as was the overall average grades for all the year 9 students. The schools also had a high geographical representation and similar prevalence rates as in other national school surveys conducted at the same time but without demanding informed consent. Nevertheless, there was a minimal overrepresentation of boys in the current sample (49.5%) in comparison with the population (49%).,A short version of The Children of Alcoholics Screening Test, CAST-6, was used to discriminate between children with problem-drinking parents and other children. CAST-6 includes 6 true/false items instead of the 30 items from the original scale and has been shown to have the same validity.In this article, we will use CAST-6 as an ordinal scale of severity with the assumption that more reported problems indicate a more severe exposure to parental problem drinking. In order to obtain enough cases within each category, we collapsed the responses to CAST-6 as follows: 1–2 problems (low severity), 3–4 (moderate severity) and 5–6 (high severity). We then examined whether the prevalence of key problems varied across these three levels of severity. The following problems were analysed: poor physical and mental health, poor social relationships and poor school satisfaction. To estimate risks for these adverse consequences, the outcomes were dichotomized into a negative and positive category. How these outcomes were measured specifically are described below.Physical and mental health include the general health status and three indicators of psychosomatic problems including stomach pain, stress and headache.General health status was measured with a five-point Likert scale ranging from ‘very good’, ‘rather good’, ‘either good or bad’, ‘rather bad’ or ‘very bad’. Respondents were classified as having poor health status if they reported ‘very bad’, ‘rather bad’ or ‘neither good or bad’.Psycho-somatic problems were measured by questions adapted from the Health Behaviors of School age Children (HBSC) including how often they suffered from stomach pain, stress and headache. The responses were dichotomized into at least ‘once a week’ and less often based on five response options ranging from ‘everyday’ to ‘seldom/never’.social relations was measured with questions of how satisfied the respondent was with his/her relationship to father, mother and friends ranging from very happy, happy, not so happy to not happy at all. Response options ‘not so happy’ and ‘not happy at all’ were assumed to measure a poor relationship.The quality of the respondents’ school satisfaction, we used the question ‘How do you like school?’ and responses with a five-point Likert scale ranging from ‘very good’, ‘rather good’, ‘neither good or bad’, rather bad or very bad. Low school satisfaction was defined as ‘neither good or bad’, ‘rather bad’ or ‘very bad’. Another aspect of school satisfaction was truancy which was measured by the question ‘Do you skip school?’ with those responding at least ‘some time every semester’ being positive cases. Finally, experiences of having been bullied were measured using questions addressing whether this had happened during the last 12 months.To measure Think back to the last 12 months. Have you ever been unable to buy something that you wanted and that other’s your age have, because you could not afford it? Those responding ‘yes’ to this question were assumed to have a low socioeconomic position. The analysis was not pre-registered and the results should be considered exploratory.The analyses consist of a graphical presentation of the proportion with various problems at different numbers of problems (0–6) with parental drinking. Estimation of logistic regression models was used to compare the risk of problems at low, moderate and high severity in exposure to parental problem drinking to those without exposure to parental problem drinking. Crude models as well as models adjusted for gender and socioeconomic position were estimated. As an indicator of low socioeconomic position, we used a measure of not being able to buy things others can afford based on the question: The proportion of adolescents reporting a poor health status increased by the number of reported problems with parental drinking according to all studied health indicators, i.e. poor general health and experiences of psychosomatic problems such as stomachache, headache and stress . The risP < 0.01). This pattern is found for all health indicators and remains with control for gender and socioeconomic position in the adjusted models, except for the risk of stomachache.The increased risk of poor health status by severity in exposure to parental drinking is verified in The risk becomes higher in groups with moderate and high severity in exposure to parental drinking respectively where all estimates are statistically significant also in adjusted models.The number of reported problems with parental drinking is associated with a worse relationship with friends and both parents . The assThe graphical result is reflected in logistic regressions with ORs for a poor relation to the father being higher at all problem levels in comparison with the relation to the mother and friends . For insschool situation increases by the number of reported problems with parental drinking in a similar manner according to all three studied indicators (The proportion of adolescents reporting a poor dicators .The increased risk of a poor school situation by severity in exposure to parental drinking is verified in Although it is well established that children with problem-drinking parents have an elevated risk of a poor life situation in many respects, there is a lack of studies addressing the importance of severity in exposure to parental problem drinking. We found only one study where there was no evidence that severity was related to the risk of harm in children.We found that higher severity in exposure to parental problem drinking among 15–16-year-olds in a Swedish national sample was related to a higher risk that the child had a reduced life situation in terms of poor health, poor social relations and poor school performance. An elevated risk was found already at low severity in exposure suggesting that there is no clear threshold for when the risk of problems becomes elevated. This finding has some support in earlier studies suggesting that also low levels of parental drinking may be associated with increased risk of psychosomatic problems to children.There are several limitations in this study that need to be acknowledged. First, causality cannot be established here due to the cross-sectional design. Thus, we cannot rule out that some other factor is related to both exposure to parental problem drinking, and the included measures of a poor life situation. Although it is reassuring that the findings remained with control for socioeconomic position and gender, we were not able to control for the suggested high comorbidity between mental disorders and alcohol use disorders.The only previous study on the importance of severity of parental alcohol problems in relation to the effects for children was based on the most more severe clinical cases of parental alcohol problems with limited generalizability to the wider population of adolescents. The major strength of this study is thus that it used a large and high-quality national sample of young people with a high representativeness.The risk of problems related to health, social relations and school performance increased with severity of exposure to parental problem drinking for children in a national sample of 15–16-year-olds in Sweden. An elevated risk was found also at low severity in exposure to parental problem drinking and became higher by increasing severity. These findings suggest that children with problem-drinking parents should be offered help especially at severe exposure to parental problem drinking but that support is warranted also at milder forms of exposure.ckad022_Supplementary_DataClick here for additional data file.


In the wake of the COVID-19 pandemic, video consultation was introduced in general practice in many countries around the world as a solution to provide remote health care to patients. It was assumed that video consultation would find widespread adoption in post–COVID-19 general practice. However, adoption rates remain low across countries in Northern Europe, suggesting that barriers to its use exist among general practitioners and other practice staff. In this viewpoint, we take a comparative approach, reflecting on similarities and differences in implementation conditions of video consultations in 5 Northern European countries’ general practice settings that might have created barriers to its use within general practice. We convened at a cross-disciplinary seminar in May 2022 with researchers and clinicians from 5 Northern European countries with expertise in digital care in general practice, and this viewpoint emerged out of dialogues from that seminar. We have reflected on barriers across general practice settings in our countries, such as lacking technological and financial support for general practitioners, that we feel are critical for adoption of video consultation in the coming years. Furthermore, there is a need to further investigate the contribution of cultural elements, such as professional norms and values, to adoption. This viewpoint may inform policy work to ensure that a sustainable level of video consultation use can be reached in the future, one that reflects the reality of general practice settings rather than policy optimism. Video consultation—defined here as a synchronous communication between the general practitioner (GP) and the patient using video calling —was bareDuring the COVID-19 pandemic, when video consultation was abruptly introduced in general practice, there was a strong belief that the pandemic would serve as “the window of opportunity”  for its Studies on patient experiences with video consultation in general practice show that patients tend to show high satisfaction, valuing aspects such as convenience, flexibility, and efficiency -11. It sIn this viewpoint, we are therefore primarily concerned with discussing possible barriers to video consultation use among GPs and practice staff, comparing the use and implementation conditions of video consultation between general practice settings in 5 countries .A digital health research network in Denmark, the so-called vCare network, convened an interdisciplinary seminar in May 2022, inviting known experts and existing collaborators from Northern Europe  who were involved in research on video consultations. The purpose of the seminar was to discuss and debate the differences and similarities in implementation and adoption processes across Northern Europe and to further discuss challenges for future adoption and how research might address this. We were particularly interested in exploring some of the systemic and organizational conditions leading to the current implementation status, illustrating elements that might be crucial for successful adoption in future general practices. To guide our conversations and help generate ideas we had agreed on a set of questions, answers to which might lead to explaining failures and partial successes. We asked the attendees (N=21) from each of the countries to collaborate on collecting information on the following aspects:The overall organization and structure of the national health care system and of general practice or primary careNational and government visions and ambitions for the digitalization of health careThe trigger of video consultation use and implementationConditions for implementation Volume of use before, during, and after the COVID-19 pandemicChallenges: why it has not taken off as expected across our countries and future perspectivesThis viewpoint emerges out of that dialogue. After the seminar, the information relating to the above aspects (in written form) was extracted, and quantifiable information obtained from existing sources is presented in The opinions presented in this viewpoint are based on research evidence and clinician experiences. There is, therefore, a risk that biases for or against video consultation influence the viewpoint presented. To increase our reflexivity regarding this point, we had a session at the seminar in which subjective attitudes and the perceived influence of these attitudes on our research were discussed. We also discussed the inherent discourses of the concepts that we use  and to which extent our researcher identity within, for example, medical research, implementation research, and social research was shaping the conclusions made. We were careful not to represent “digitalization enthusiasm” or “digitalization criticality.” Representing Northern European health care, our viewpoint can only be transferred to settings with similar conditions and similar digitalization level and readiness.Discussing the current challenges that general practices face regarding implementation of video consultation and drawing on current literature, we also provide suggestions as to the potential action points that may need to be considered if successful adoption of video consultation is the goal.Research on video consultation adoption in general practice has shown that it is reliant on several multilevel factors from health care digitalization level  to technical infrastructure, IT literacy, and readiness of users  ,11,14-20Turning to the macro policy level, introducing video consultation as a “need-to” consultation form during COVID-19 only accelerated recent years’ policy-driven ambitions to digitalize general practice in Northern Europe to manage health care demand and related costs, increase efficiency, quality, and access .For example, government policy in the United Kingdom has been focused even before COVID-19 on establishing a “digital-first” primary care . In the We argue, however, that there is a misalignment in some countries between policy-level digitalization enthusiasm and the conditions needed for action toward implementation in the clinical context in which GPs and practice staff are operating. This includes ensuring on a national level that technical and financial conditions are met.Across the countries represented here, video consultation is provided by different companies with the common goal of facilitating two-way secure video consultation between GP and patients. Although GPs in Germany and the Netherlands can choose between several solutions with varying user-friendliness, in Denmark, one specific secure solution (the “My Doctor” app) has been endorsed on an organizational level as the “official” solution in general practice and made available for free to GPs and patients. In Norway, GPs must both arrange and pay for the solution themselves creating a further threshold for implementation. In the United Kingdom, there are video consultation options included in the platforms available for providing patients with web-based services in general practice. General practices choose which platform to offer, pay for it, and it is free at the point of use for patients.Currently, not all platforms described in the previous paragraph can be easily integrated into the GP’s electronic patient record system, creating a barrier for using video consultation since the technological setup is then perceived as inefficient. We believe that the integration of the video consulting technological solution in the GPs’ electronic systems is critical for adoption of video consultation in the coming years. This fits with evidence from the United Kingdom showing that integration of technological solutions with GP workflows is a key enabler in increasing GP use .Focused action aimed at securing sufficient financial support and incentives is needed in several of our countries. Although the implementation of video consultation is supported financially in Denmark and the Netherlands, for example, by agreeing on a reimbursement that is equal or slightly higher than the one for in-person visits (and in Denmark much higher than the reimbursements of both e-consultation and telephone consultation), in Germany, GPs may experience an adverse cost-to-benefit effort due to capped budget. In Norway, billing rates for video consultation are the same as they are for telephone and e-consultations, which does not stimulate GPs to change their mode of contact from telephone to video consultations. In the United Kingdom, there are not individual costs associated with consultation types, with funding attributed at a patient level regardless of consultation type used.Efforts to improve technological and financial support for the implementation of video consultation need to be accompanied by a focus on understanding, and possibly challenging, GP-perceived barriers toward remote care on a cultural level. By this, we mean the deeply held cultural or professional norms, beliefs, values, and behaviors that relate to the perceived function of general practice and the role of a GP. Evidence shows that in general, GPs are more reluctant than patients about using video consultation , due to Consulting the doctor face-to-face has for years been considered the “gold standard” for securing access to care, quality of care, and patient safety. Many GPs and practice staff view and experience the technologically mediated video consultation as constraining their ability to provide high-quality health care to patients. This is due to technological challenges  , impaireWe argue that if video consultation is ever to become a part of routine general practice, GPs and practice staff need to reflect personally and collectively on how video consultation impacts their professional identity and the norms established in their clinic for how they should work. Research on digital innovation in organizations shows that establishing a shared vision for the adoption process of digital technology is a determinant of success, leading to enhanced commitment, responsibility, and cooperation .In a general practice setting, this process of establishing a shared vision could also involve exploring perceived limitations and potentials of video consultation use for a general practice culture, reflecting for example on attitudes and sense-making toward digital technology, as these have been shown to impact the adoption process of digital technologies to a large extent -30. The We argue that the more we know about GPs’ and general practice staffs’ understanding of and attitudes toward video consultation in the context of their work within general practice, the more we can work on developing educational initiatives that target users’ uncertainties, worries, and “tensions,” hereby reaching a sustainable level of video consultation use that reflect the reality of practice rather than policy optimism and the promissory value of video consultations.Barriers to use can also be a legitimate reaction to perceptions of inappropriate use or minimal relative advantage for general practice compared to the use of other methods of consulting ,14. As GLooking ahead, general practice should consider a new perspective on video consultation beyond viewing it as a crisis and “need-to” tool tied to the COVID-19 pandemic or as “the new normal.” Entering dialogues with other GP colleagues, technology providers, politicians, and patients about their needs and visions for future digital care in general practice seems essential, for example, through cocreative processes and user boards.Three years following its abrupt and large-scale introduction in general practice, video consultation is still in the process of finding its clear role and advantage—an adaptive process that, as with other health technologies, can be messy, unpredictable, and discontinuous ,31.Further research is needed to investigate how to develop, resource, and manage flexible technological and financial solutions required to support appropriate implementation in general practices. This kind of research should be conducted in close collaboration with those invested in implementation processes  to ensure alignment between different interests and perspectives.Furthermore, research is needed that addresses concerns and skepticisms among users as well as nonusers, including how video consultation practices are perceived to influence professional values and norms, access to care (equity aspects), and quality of care. Addressing issues of safe and ethical use of video consultation, longitudinal studies and register-based research is needed as well as qualitative approaches that enable microanalysis of interaction patterns.In this viewpoint, we have explored similarities and differences across our countries regarding the use and implementation conditions of video consultation in general practice within the context of consistently low use of video consultation across the included countries. We have reflected on barriers across general practice settings in our countries, such as lacking technological and financial support for GPs, that we feel are critical for adoption of video consultation in the coming years. Furthermore, there is a need to further investigate the contribution of cultural elements to adoption. This viewpoint may inform policy work to ensure that a sustainable level of video consultation use can be reached in the future, one that reflects the reality of general practice settings rather than policy optimism.