Hierarchical modularity and the evolution of genetic interactomes across species.

To date, cross-species comparisons of genetic interactomes have been restricted to small or functionally related gene sets, limiting our ability to infer evolutionary trends. To facilitate a more comprehensive analysis, we constructed a genome-scale epistasis map (E-MAP) for the fission yeast Schizosaccharomyces pombe, providing phenotypic signatures for ~60% of the nonessential genome. Using these signatures, we generated a catalog of 297 functional modules, and we assigned function to 144 previously uncharacterized genes, including mRNA splicing and DNA damage checkpoint factors. Comparison with an integrated genetic interactome from the budding yeast Saccharomyces cerevisiae revealed a hierarchical model for the evolution of genetic interactions, with conservation highest within protein complexes, lower within biological processes, and lowest between distinct biological processes. Despite the large evolutionary distance and extensive rewiring of individual interactions, both networks retain conserved features and display similar levels of functional crosstalk between biological processes, suggesting general design principles of genetic interactomes.

Attenuating M-current suppression in vivo by a mutant Kcnq2 gene knock-in reduces seizure burden and prevents status epilepticus-induced neuronal death and epileptogenesis.

OBJECTIVES: The M-current is a low-threshold voltage-gated potassium current generated by Kv7 subunits that regulates neural excitation. It is important to note that M-current suppression, induced by activation of Gq-coupled neurotransmitter receptors, can dynamically regulate the threshold of action-potential firing and firing frequency. Here we sought to directly examine whether M-current suppression is involved in seizures and epileptogenesis. METHODS: Kv7.2 knock-in mice lacking the key protein kinase C (PKC) phosphorylation acceptor site for M-current suppression were generated by introducing an alanine substitution at serine residue 559 of mouse Kv7.2, mKv7.2(S559A). Basic electrophysiologic properties of the M-current between wild-type and Kv7.2(S559A) knock-in mice were analyzed in primary cultured neurons. Homozygous Kv7.2(S559A) knock-in mice were used to evaluate the protective effect of mutant Kv7.2 channel against chemoconvulsant-induced seizures. In addition, pilocarpine-induced neuronal damage and spontaneously recurrent seizures were evaluated after equivalent chemoconvulsant-induced status epilepticus was achieved by coadministration of the M-current-specific channel inhibitor, XE991. RESULT: Neurons from Kv7.2(S559A) knock-in mice showed normal basal M-currents. Knock-in mice displayed reduced M-current suppression when challenged by a muscarinic agonist, oxotremorine-M. Kv7.2(S559A) mice were resistant to chemoconvulsant-induced seizures with no mortality. Administration of XE991 transiently exacerbated seizures in knock-in mice equivalent to those of wild-type mice. Valproate, which disrupts neurotransmitter-induced M-current suppression, showed no additional anticonvulsant effect in Kv7.2(S559A) mice. After experiencing status epilepticus, Kv7.2(S559A) knock-in mice did not show seizure-induced cell death or spontaneous recurring seizures. SIGNIFICANCE: This study provides evidence that neurotransmitter-induced suppression of M-current generated by Kv7.2-containing channels exacerbates behavioral seizures. In addition, prompt recovery of M-current after status epilepticus prevents subsequent neuronal death and the development of spontaneously recurrent seizures. Therefore, prompt restoration of M-current activity may have a therapeutic benefit for epilepsy.

Modulation of Kv7 channels and excitability in the brain.

Neuronal Kv7 channels underlie a voltage-gated non-inactivating potassium current known as the M-current. Due to its particular characteristics, Kv7 channels show pronounced control over the excitability of neurons. We will discuss various factors that have been shown to drastically alter the activity of this channel such as protein and phospholipid interactions, phosphorylation, calcium, and numerous neurotransmitters. Kv7 channels locate to key areas for the control of action potential initiation and propagation. Moreover, we will explore the dynamic surface expression of the channel modulated by neurotransmitters and neural activity. We will also focus on known principle functions of neural Kv7 channels: control of resting membrane potential and spiking threshold, setting the firing frequency, afterhyperpolarization after burst firing, theta resonance, and transient hyperexcitability from neurotransmitter-induced suppression of the M-current. Finally, we will discuss the contribution of altered Kv7 activity to pathologies such as epilepsy and cognitive deficits.

XE991 and Linopirdine Are State-Dependent Inhibitors for Kv7/KCNQ Channels that Favor Activated Single Subunits.

M-channel inhibitors, especially XE991, are being used increasingly in animal experiments; however, insufficient characterization of XE991 at times confounds the interpretation of results when using this compound. Here, we demonstrate that XE991 and linopirdine are state-dependent inhibitors that favor the activated-subunit of neuronal Kv7/KCNQ channels. We performed patch-clamp experiments on homomeric Kv7.2 or heteromeric Kv7.2/3 channels expressed in Chinese hamster ovary cells to characterize XE991 and linopirdine. Neither inhibitor was efficacious around the resting membrane potential of cells in physiologic conditions. Inhibition of Kv7.2 and Kv7.2/3 channels by XE991 was closely related with channel activation. When the voltage dependence of activation was left-shifted by retigabine or right-shifted by the mutation, Kv7.2(R214D), the shift in half-activation voltage proportionally coincided with the shift in the half-effective potential for XE991 inhibition. Inhibition kinetics during XE991 wash-in was facilitated at depolarized potentials. Ten-minute washout of XE991 resulted in ∼30% current recovery, most of which was attributed to surface transport of Kv7.2 channels. Linopirdine also exhibited similar inhibition characteristics, with the exception of near- complete current recovery after washout at depolarized potentials. Inhibition kinetics of both XE991 and linopirdine was not as sensitive to changes in voltage as would be predicted by open- channel inhibition. Instead, they were well explained by binding to a single activated subunit. The characteristics of XE991 and linopirdine should be taken into account when these M-channel inhibitors are used in experiments.

Coordinated signal integration at the M-type potassium channel upon muscarinic stimulation.

Several neurotransmitters, including acetylcholine, regulate neuronal tone by suppressing a non-inactivating low-threshold voltage-gated potassium current generated by the M-channel. Agonist dependent control of the M-channel is mediated by calmodulin, activation of anchored protein kinase C (PKC), and depletion of the phospholipid messenger phosphatidylinositol 4,5-bisphosphate (PIP2). In this report, we show how this trio of second messenger responsive events acts synergistically and in a stepwise manner to suppress activity of the M-current. PKC phosphorylation of the KCNQ2 channel subunit induces dissociation of calmodulin from the M-channel complex. The calmodulin-deficient channel has a reduced affinity towards PIP2. This pathway enhances the effect of concomitant reduction of PIP2, which leads to disruption of the M-channel function. These findings clarify how a common lipid cofactor, such as PIP2, can selectively regulate ion channels.

Genome-wide epistatic expression quantitative trait loci discovery in four human tissues reveals the importance of local chromosomal interactions governing gene expression.

BACKGROUND: Epistasis (synergistic interaction) among SNPs governing gene expression is likely to arise within transcriptional networks. However, the power to detect it is limited by the large number of combinations to be tested and the modest sample sizes of most datasets. By limiting the interaction search space firstly to cis-trans and then cis-cis SNP pairs where both SNPs had an independent effect on the expression of the most variable transcripts in the liver and brain, we greatly reduced the size of the search space. RESULTS: Within the cis-trans search space we discovered three transcripts with significant epistasis. Surprisingly, all interacting SNP pairs were located nearby each other on the chromosome (within 290 kb-2.16 Mb). Despite their proximity, the interacting SNPs were outside the range of linkage disequilibrium (LD), which was absent between the pairs (r(2) < 0.01). Accordingly, we redefined the search space to detect cis-cis interactions, where a cis-SNP was located within 10 Mb of the target transcript. The results of this show evidence for the epistatic regulation of 50 transcripts across the tissues studied. Three transcripts, namely, HLA-G, PSORS1C1 and HLA-DRB5 share common regulatory SNPs in the pre-frontal cortex and their expression is significantly correlated. This pattern of epistasis is consistent with mediation via long-range chromatin structures rather than the binding of transcription factors in trans. Accordingly, some of the interactions map to regions of the genome known to physically interact in lymphoblastoid cell lines while others map to known promoter and enhancer elements. SNPs involved in interactions appear to be enriched for promoter markers. CONCLUSIONS: In the context of gene expression and its regulation, our analysis indicates that the study of cis-cis or local epistatic interactions may have a more important role than interchromosomal interactions.

Adrenergic regulation of HCN4 channel requires protein association with ß2-adrenergic receptor.

ß(1)- and ß(2)-adrenergic receptors utilize different signaling mechanisms to control cardiac function. Recent studies demonstrated that ß(2)-adrenergic receptors (ß(2)ARs) colocalize with some ion channels that are critical for proper cardiac function. Here, we demonstrate that ß(2)ARs form protein complexes with the pacemaker HCN4 channel, as well as with other subtypes of HCN channels. The adrenergic receptor-binding site was identified at a proximal region of the N-terminal tail of the HCN4 channel. A synthetic peptide derived from the ß(2)AR-binding domain of the HCN4 channel disrupted interaction between HCN4 and ß(2)AR. In addition, treatment with this peptide prevented adrenergic augmentation of pacemaker currents and spontaneous contraction rates but did not affect adrenergic regulation of voltage-gated calcium currents. These results suggest that the ion channel-receptor complex is a critical mechanism in ion channel regulation.

Surrogate explanations for role discovery on graphs.

Role discovery is the task of dividing the set of nodes on a graph into classes of structurally similar roles. Modern strategies for role discovery typically rely on graph embedding techniques, which are capable of recognising complex graph structures when reducing nodes to dense vector representations. However, when working with large, real-world networks, it is difficult to interpret or validate a set of roles identified according to these methods. In this work, motivated by advancements in the field of explainable artificial intelligence, we propose surrogate explanation for role discovery, a new framework for interpreting role assignments on large graphs using small subgraph structures known as graphlets. We demonstrate our framework on a small synthetic graph with prescribed structure, before applying them to a larger real-world network. In the second case, a large, multidisciplinary citation network, we successfully identify a number of important citation patterns or structures which reflect interdisciplinary research.

CowMesh: a data-mesh architecture to unify dairy industry data for prediction and monitoring.

Dairy is an economically significant industry that caters to the huge demand for food products in people's lives. To remain profitable, farmers need to manage their farms and the health of the dairy cows in their herds. There are, however, many risks to cow health that can lead to significant challenges to dairy farm management and have the potential to lead to significant losses. Such risks include cow udder infections (i.e., mastitis) and cow lameness. As automation and data recording become more common in the agricultural sector, dairy farms are generating increasing amounts of data. Recently, these data are being used to generate insights into farm and cow health, where the objective is to help farmers manage the health and welfare of dairy cows and reduce losses from cow health issues. Despite the level of data generation on dairy farms, this information is often difficult to access due to a lack of a single, central organization to collect data from individual farms. The prospect of such an organization, however, raises questions about data ownership, with some farmers reluctant to share their farm data for privacy reasons. In this study, we describe a new data mesh architecture designed for the dairy industry that focuses on facilitating access to data from farms in a decentralized fashion. This has the benefit of keeping the ownership of data with dairy farmers while bringing data together by providing a common and uniform set of protocols. Furthermore, this architecture will allow secure access to the data by research groups and product development groups, who can plug in new projects and applications built across the data. No similar framework currently exists in the dairy industry, and such a data mesh can help industry stakeholders by bringing the dairy farms of a country together in a decentralized fashion. This not only helps farmers, dairy researchers, and product builders but also facilitates an overview of all dairy farms which can help governments to decide on regulations to improve the dairy industry at a national level.

Towards explainable community finding.

The detection of communities of nodes is an important task in understanding the structure of networks. Multiple approaches have been developed to tackle this problem, many of which are in common usage in real-world applications, such as in public health networks. However, clear insight into the reasoning behind the community labels produced by these algorithms is rarely provided. Drawing inspiration from the machine learning literature, we aim to provide post-hoc explanations for the outputs of these algorithms using interpretable features of the network. In this paper, we propose a model-agnostic methodology that identifies a set of informative features to help explain the output of a community finding algorithm. We apply it to three well-known algorithms, though the methodology is designed to generalise to new approaches. As well as identifying important features for a post-hoc explanation system, we report on the common features found made by the different algorithms and the differences between the approaches. Supplementary Information: The online version contains supplementary material available at 10.1007/s41109-022-00515-6.

Author multidisciplinarity and disciplinary roles in field of study networks.

When studying large research corpora, "distant reading" methods are vital to understand the topics and trends in the corresponding research space. In particular, given the recognised benefits of multidisciplinary research, it may be important to map schools or communities of diverse research topics, and to understand the multidisciplinary role that topics play within and between these communities. This work proposes Field of Study (FoS) networks as a novel network representation for use in scientometric analysis. We describe the formation of FoS networks, which relate research topics according to the authors who publish in them, from corpora of articles in which fields of study can be identified. FoS networks are particularly useful for the distant reading of large datasets of research papers when analysed through the lens of exploring multidisciplinary science. In an evolving scientific landscape, modular communities in FoS networks offer an alternative categorisation strategy for research topics and sub-disciplines, when compared to traditional prescribed discipline classification schemes. Furthermore, structural role analysis of FoS networks can highlight important characteristics of topics in such communities. To support this, we present two case studies which explore multidisciplinary research in corpora of varying size and scope; namely, 6323 articles relating to network science research and 4,184,011 articles relating to research on the COVID-19-pandemic.

Bone segmentation in contrast enhanced whole-body computed tomography.

Segmentation of bone regions allows for enhanced diagnostics, disease characterisation and treatment monitoring in CT imaging. In contrast enhanced whole-body scans accurate automatic segmentation is particularly difficult as low dose whole body protocols reduce image quality and make contrast enhanced regions more difficult to separate when relying on differences in pixel intensities. This paper outlines a U-net architecture with novel preprocessing techniques, based on the windowing of training data and the modification of sigmoid activation threshold selection to successfully segment bone-bone marrow regions from low dose contrast enhanced whole-body CT scans. The proposed method achieved mean Dice coefficients of 0.979 ± 0.02, 0.965 ± 0.03, and 0.934 ± 0.06 on two internal datasets and one external test dataset respectively. We have demonstrated that appropriate preprocessing is important for differentiating between bone and contrast dye, and that excellent results can be achieved with limited data.

Spectroscopic technologies and data fusion: Applications for the dairy industry.

Increasing consumer awareness, scale of manufacture, and demand to ensure safety, quality and sustainability have accelerated the need for rapid, reliable, and accurate analytical techniques for food products. Spectroscopy, coupled with Artificial Intelligence-enabled sensors and chemometric techniques, has led to the fusion of data sources for dairy analytical applications. This article provides an overview of the current spectroscopic technologies used in the dairy industry, with an introduction to data fusion and the associated methodologies used in spectroscopy-based data fusion. The relevance of data fusion in the dairy industry is considered, focusing on its potential to improve predictions for processing traits by chemometric techniques, such as principal component analysis (PCA), partial least squares regression (PLS), and other machine learning algorithms.

Correction: Collaboration in the time of COVID: a scientometric analysis of multidisciplinary SARS-CoV-2 research.

[This corrects the article DOI: 10.1057/s41599-021-00922-7.].

Missing value imputation for epistatic MAPs.

BACKGROUND: Epistatic miniarray profiling (E-MAPs) is a high-throughput approach capable of quantifying aggravating or alleviating genetic interactions between gene pairs. The datasets resulting from E-MAP experiments typically take the form of a symmetric pairwise matrix of interaction scores. These datasets have a significant number of missing values - up to 35% - that can reduce the effectiveness of some data analysis techniques and prevent the use of others. An effective method for imputing interactions would therefore increase the types of possible analysis, as well as increase the potential to identify novel functional interactions between gene pairs. Several methods have been developed to handle missing values in microarray data, but it is unclear how applicable these methods are to E-MAP data because of their pairwise nature and the significantly larger number of missing values. Here we evaluate four alternative imputation strategies, three local (Nearest neighbor-based) and one global (PCA-based), that have been modified to work with symmetric pairwise data. RESULTS: We identify different categories for the missing data based on their underlying cause, and show that values from the largest category can be imputed effectively. We compare local and global imputation approaches across a variety of distinct E-MAP datasets, showing that both are competitive and preferable to filling in with zeros. In addition we show that these methods are effective in an E-MAP from a different species, suggesting that pairwise imputation techniques will be increasingly useful as analogous epistasis mapping techniques are developed in different species. We show that strongly alleviating interactions are significantly more difficult to predict than strongly aggravating interactions. Finally we show that imputed interactions, generated using nearest neighbor methods, are enriched for annotations in the same manner as measured interactions. Therefore our method potentially expands the number of mapped epistatic interactions. In addition we make implementations of our algorithms available for use by other researchers. CONCLUSIONS: We address the problem of missing value imputation for E-MAPs, and suggest the use of symmetric nearest neighbor based approaches as they offer consistently accurate imputations across multiple datasets in a tractable manner.

Overlapping community finding with noisy pairwise constraints.

In many real applications of semi-supervised learning, the guidance provided by a human oracle might be "noisy" or inaccurate. Human annotators will often be imperfect, in the sense that they can make subjective decisions, they might only have partial knowledge of the task at hand, or they may simply complete a labeling task incorrectly due to the burden of annotation. Similarly, in the context of semi-supervised community finding in complex networks, information encoded as pairwise constraints may be unreliable or conflicting due to the human element in the annotation process. This study aims to address the challenge of handling noisy pairwise constraints in overlapping semi-supervised community detection, by framing the task as an outlier detection problem. We propose a general architecture which includes a process to "clean" or filter noisy constraints. Furthermore, we introduce multiple designs for the cleaning process which use different type of outlier detection models, including autoencoders. A comprehensive evaluation is conducted for each proposed methodology, which demonstrates the potential of the proposed architecture for reducing the impact of noisy supervision in the context of overlapping community detection.

Ensemble non-negative matrix factorization methods for clustering protein-protein interactions.

MOTIVATION: When working with large-scale protein interaction data, an important analysis task is the assignment of pairs of proteins to groups that correspond to higher order assemblies. Previously a common approach to this problem has been to apply standard hierarchical clustering methods to identify such a groups. Here we propose a new algorithm for aggregating a diverse collection of matrix factorizations to produce a more informative clustering, which takes the form of a 'soft' hierarchy of clusters. RESULTS: We apply the proposed Ensemble non-negative matrix factorization (NMF) algorithm to a high-quality assembly of binary protein interactions derived from two proteome-wide studies in yeast. Our experimental evaluation demonstrates that the algorithm lends itself to discovering small localized structures in this data, which correspond to known functional groupings of complexes. In addition, we show that the algorithm also supports the assignment of putative functions for previously uncharacterized proteins, for instance the protein YNR024W, which may be an uncharacterized component of the exosome.

M-current preservation contributes to anticonvulsant effects of valproic acid.

Valproic acid (VPA) has been widely used for decades to treat epilepsy; however, its mechanism of action remains poorly understood. Here, we report that the anticonvulsant effects of nonacute VPA treatment involve preservation of the M-current, a low-threshold noninactivating potassium current, during seizures. In a wide variety of neurons, activation of Gq-coupled receptors, such as the m1 muscarinic acetylcholine receptor, suppresses the M-current and induces hyperexcitability. We demonstrated that VPA treatment disrupts muscarinic suppression of the M-current and prevents resultant agonist-induced neuronal hyperexcitability. We also determined that VPA treatment interferes with M-channel signaling by inhibiting palmitoylation of a signaling scaffold protein, AKAP79/150, in cultured neurons. In a kainate-induced murine seizure model, administration of a dose of an M-channel inhibitor that did not affect kainate-induced seizure transiently eliminated the anticonvulsant effects of VPA. Retigabine, an M-channel opener that does not open receptor-suppressed M-channels, provided anticonvulsant effects only when administered prior to seizure induction in control animals. In contrast, treatment of VPA-treated mice with retigabine induced anticonvulsant effects even when administered after seizure induction. Together, these results suggest that receptor-induced M-current suppression plays a role in the pathophysiology of seizures and that preservation of the M-current during seizures has potential as an effective therapeutic strategy.

Imputing and predicting quantitative genetic interactions in epistatic MAPs.

Mapping epistatic (or genetic) interactions has emerged as an important network biology approach for establishing functional relationships among genes and proteins. Epistasis networks are complementary to physical protein interaction networks, providing valuable insight into both the function of individual genes and the overall wiring of the cell. A high-throughput method termed "epistatic mini array profiles" (E-MAPs) was recently developed in yeast to quantify alleviating or aggravating interactions between gene pairs. The typical output of an E-MAP experiment is a large symmetric matrix of interaction scores. One problem with this data is the large amount of missing values - interactions that cannot be measured during the high-throughput process or whose measurements were discarded due to quality filtering steps. These missing values can reduce the effectiveness of some data analysis techniques and prevent the use of others. Here, we discuss one solution to this problem, imputation using nearest neighbors, and give practical examples of the use of a freely available implementation of this method.

Improved functional overview of protein complexes using inferred epistatic relationships.

BACKGROUND: Epistatic Miniarray Profiling(E-MAP) quantifies the net effect on growth rate of disrupting pairs of genes, often producing phenotypes that may be more (negative epistasis) or less (positive epistasis) severe than the phenotype predicted based on single gene disruptions. Epistatic interactions are important for understanding cell biology because they define relationships between individual genes, and between sets of genes involved in biochemical pathways and protein complexes. Each E-MAP screen quantifies the interactions between a logically selected subset of genes (e.g. genes whose products share a common function). Interactions that occur between genes involved in different cellular processes are not as frequently measured, yet these interactions are important for providing an overview of cellular organization. RESULTS: We introduce a method for combining overlapping E-MAP screens and inferring new interactions between them. We use this method to infer with high confidence 2,240 new strongly epistatic interactions and 34,469 weakly epistatic or neutral interactions. We show that accuracy of the predicted interactions approaches that of replicate experiments and that, like measured interactions, they are enriched for features such as shared biochemical pathways and knockout phenotypes. We constructed an expanded epistasis map for yeast cell protein complexes and show that our new interactions increase the evidence for previously proposed inter-complex connections, and predict many new links. We validated a number of these in the laboratory, including new interactions linking the SWR-C chromatin modifying complex and the nuclear transport apparatus. CONCLUSION: Overall, our data support a modular model of yeast cell protein network organization and show how prediction methods can considerably extend the information that can be extracted from overlapping E-MAP screens.