Cooperative Binding of Transcription Factors Orchestrates Reprogramming.

Oct4, Sox2, Klf4, and cMyc (OSKM) reprogram somatic cells to pluripotency. To gain a mechanistic understanding of their function, we mapped OSKM-binding, stage-specific transcription factors (TFs), and chromatin states in discrete reprogramming stages and performed loss- and gain-of-function experiments. We found that OSK predominantly bind active somatic enhancers early in reprogramming and immediately initiate their inactivation genome-wide by inducing the redistribution of somatic TFs away from somatic enhancers to sites elsewhere engaged by OSK, recruiting Hdac1, and repressing the somatic TF Fra1. Pluripotency enhancer selection is a stepwise process that also begins early in reprogramming through collaborative binding of OSK at sites with high OSK-motif density. Most pluripotency enhancers are selected later in the process and require OS and other pluripotency TFs. Somatic and pluripotency TFs modulate reprogramming efficiency when overexpressed by altering OSK targeting, somatic-enhancer inactivation, and pluripotency enhancer selection. Together, our data indicate that collaborative interactions among OSK and with stage-specific TFs direct both somatic-enhancer inactivation and pluripotency-enhancer selection to drive reprogramming.

Standing on the shoulders of mice.

While inbred mice have informed most of what we know about the immune system in the modern era, they have clear limitations with respect to their ability to be informative regarding genetic heterogeneity or microbial influences. They have also not been very predictive as models of human disease or vaccination results. Although there are concerted attempts to compensate for these flaws, the rapid rise of human studies, driven by both technical and conceptual advances, promises to fill in these gaps, as well as provide direct information about human diseases and vaccination responses. Work on human immunity has already provided important additional perspectives on basic immunology such as the importance of clonal deletion to self-tolerance, and while many challenges remain, it seems inevitable that "the human model" will continue to inform us about the immune system and even allow for the discovery of new mechanisms.

Collaborative imagination synchronizes representations of the future and fosters social connection in the present.

From close friends to people on a first date, imagining a shared future appears fundamental to relationships. Yet, no previous research has conceptualized the act of imagination as a socially constructed process that affects how connected we feel to others. The present studies provide a framework for investigating imagination as a collaborative process in which individuals cocreate shared representations of hypothetical events-what we call collaborative imagination. Across two preregistered studies (N = 244), we provide evidence that collaborative imagination of a shared future fosters social connection in novel dyads-beyond imagining a shared future individually or shared experience in general. Subjective ratings and natural language processing of participants' imagined narratives illuminate the representational features of imagined events shaped by collaborative imagination. Together, the present findings have the potential to shift how we view the structure and function of imagination with implications for better understanding interpersonal relationships and collective cognition.

A Perspective on Neuroscience Data Standardization with Neurodata Without Borders.

Neuroscience research has evolved to generate increasingly large and complex experimental data sets, and advanced data science tools are taking on central roles in neuroscience research. Neurodata Without Borders (NWB), a standard language for neurophysiology data, has recently emerged as a powerful solution for data management, analysis, and sharing. We here discuss our labs' efforts to implement NWB data science pipelines. We describe general principles and specific use cases that illustrate successes, challenges, and non-trivial decisions in software engineering. We hope that our experience can provide guidance for the neuroscience community and help bridge the gap between experimental neuroscience and data science. Key takeaways from this article are that (1) standardization with NWB requires non-trivial design choices; (2) the general practice of standardization in the lab promotes data awareness and literacy, and improves transparency, rigor, and reproducibility in our science; (3) we offer several feature suggestions to ease the extensibility, publishing/sharing, and usability for NWB standard and users of NWB data.

GCFMCL: predicting miRNA-drug sensitivity using graph collaborative filtering and multi-view contrastive learning.

Studies have shown that the mechanism of action of many drugs is related to miRNA. In-depth research on the relationship between miRNA and drugs can provide theoretical foundations and practical approaches for various areas, such as drug target discovery, drug repositioning and biomarker research. Traditional biological experiments to test miRNA-drug susceptibility are costly and time-consuming. Thus, sequence- or topology-based deep learning methods are recognized in this field for their efficiency and accuracy. However, these methods have limitations in dealing with sparse topologies and higher-order information of miRNA (drug) feature. In this work, we propose GCFMCL, a model for multi-view contrastive learning based on graph collaborative filtering. To the best of our knowledge, this is the first attempt that incorporates contrastive learning strategy into the graph collaborative filtering framework to predict the sensitivity relationships between miRNA and drug. The proposed multi-view contrastive learning method is divided into topological contrastive objective and feature contrastive objective: (1) For the homogeneous neighbors of the topological graph, we propose a novel topological contrastive learning method via constructing the contrastive target through the topological neighborhood information of nodes. (2) The proposed model obtains feature contrastive targets from high-order feature information according to the correlation of node features, and mines potential neighborhood relationships in the feature space. The proposed multi-view comparative learning effectively alleviates the impact of heterogeneous node noise and graph data sparsity in graph collaborative filtering, and significantly enhances the performance of the model. Our study employs a dataset derived from the NoncoRNA and ncDR databases, encompassing 2049 experimentally validated miRNA-drug sensitivity associations. Five-fold cross-validation shows that the Area Under the Curve (AUC), Area Under the Precision-Recall Curve (AUPR) and F1-score (F1) of GCFMCL reach 95.28%, 95.66% and 89.77%, which outperforms the state-of-the-art (SOTA) method by the margin of 2.73%, 3.42% and 4.96%, respectively. Our code and data can be accessed at https://github.com/kkkayle/GCFMCL.

Collaborative deep learning improves disease-related circRNA prediction based on multi-source functional information.

Emerging studies have shown that circular RNAs (circRNAs) are involved in a variety of biological processes and play a key role in disease diagnosing, treating and inferring. Although many methods, including traditional machine learning and deep learning, have been developed to predict associations between circRNAs and diseases, the biological function of circRNAs has not been fully exploited. Some methods have explored disease-related circRNAs based on different views, but how to efficiently use the multi-view data about circRNA is still not well studied. Therefore, we propose a computational model to predict potential circRNA-disease associations based on collaborative learning with circRNA multi-view functional annotations. First, we extract circRNA multi-view functional annotations and build circRNA association networks, respectively, to enable effective network fusion. Then, a collaborative deep learning framework for multi-view information is designed to get circRNA multi-source information features, which can make full use of the internal relationship among circRNA multi-view information. We build a network consisting of circRNAs and diseases by their functional similarity and extract the consistency description information of circRNAs and diseases. Last, we predict potential associations between circRNAs and diseases based on graph auto encoder. Our computational model has better performance in predicting candidate disease-related circRNAs than the existing ones. Furthermore, it shows the high practicability of the method that we use several common diseases as case studies to find some unknown circRNAs related to them. The experiments show that CLCDA can efficiently predict disease-related circRNAs and are helpful for the diagnosis and treatment of human disease.

A mutation in Themis contributes to anaphylaxis severity following oral peanut challenge in CC027 mice.

BACKGROUND: The development of peanut allergy is due to a combination of genetic and environmental factors, although specific genes have proven difficult to identify. Previously, we reported that peanut-sensitized Collaborative Cross strain CC027/GeniUnc (CC027) mice develop anaphylaxis upon oral challenge to peanut, in contrast to C3H/HeJ (C3H) mice. OBJECTIVE: This study aimed to determine the genetic basis of orally induced anaphylaxis to peanut in CC027 mice. METHODS: A genetic mapping population between CC027 and C3H mice was designed to identify the genetic factors that drive oral anaphylaxis. A total of 356 CC027xC3H backcrossed mice were generated, sensitized to peanut, then challenged to peanut by oral gavage. Anaphylaxis and peanut-specific IgE were quantified for all mice. T-cell phenotyping was conducted on CC027 mice and 5 additional Collaborative Cross strains. RESULTS: Anaphylaxis to peanut was absent in 77% of backcrossed mice, with 19% showing moderate anaphylaxis and 4% having severe anaphylaxis. There were 8 genetic loci associated with variation in response to peanut challenge-6 associated with anaphylaxis (temperature decrease) and 2 associated with peanut-specific IgE levels. There were 2 major loci that impacted multiple aspects of the severity of acute anaphylaxis, at which the CC027 allele was associated with worse outcome. At one of these loci, CC027 has a private genetic variant in the Themis gene. Consistent with described functions of Themis, we found that CC027 mice have more immature T cells with fewer CD8+, CD4+, and CD4+CD25+CD127- regulatory T cells. CONCLUSIONS: Our results demonstrate a key role for Themis in the orally reactive CC027 mouse model of peanut allergy.

Fusing graph transformer with multi-aggregate GCN for enhanced drug-disease associations prediction.

BACKGROUND: Identification of potential drug-disease associations is important for both the discovery of new indications for drugs and for the reduction of unknown adverse drug reactions. Exploring the potential links between drugs and diseases is crucial for advancing biomedical research and improving healthcare. While advanced computational techniques play a vital role in revealing the connections between drugs and diseases, current research still faces challenges in the process of mining potential relationships between drugs and diseases using heterogeneous network data. RESULTS: In this study, we propose a learning framework for fusing Graph Transformer Networks and multi-aggregate graph convolutional network to learn efficient heterogenous information graph representations for drug-disease association prediction, termed WMAGT. This method extensively harnesses the capabilities of a robust graph transformer, effectively modeling the local and global interactions of nodes by integrating a graph convolutional network and a graph transformer with self-attention mechanisms in its encoder. We first integrate drug-drug, drug-disease, and disease-disease networks to construct heterogeneous information graph. Multi-aggregate graph convolutional network and graph transformer are then used in conjunction with neural collaborative filtering module to integrate information from different domains into highly effective feature representation. CONCLUSIONS: Rigorous cross-validation, ablation studies examined the robustness and effectiveness of the proposed method. Experimental results demonstrate that WMAGT outperforms other state-of-the-art methods in accurate drug-disease association prediction, which is beneficial for drug repositioning and drug safety research.

Protein kinase R induced by type I interferons is a main regulator of reactive microglia in Zika virus infection.

Microglial cells are the phagocytic cells of the brain that under physiological conditions participate in brain homeostasis and surveillance. Under pathogenic states, microglia undergoes strong morphological and transcriptional changes potentially leading to sustained neuroinflammation, brain damage, and cognitive disorders. Postnatal and adult Zika virus (ZIKV) brain infection is characterized by the induction of reactive microglia associated with brain inflammation, synapse loss and neuropathogenesis. Contrary to neurons, microglial cells are not infected by ZIKV thus raising the question of the mechanism governing ZIKV-induced microglia's reactivity. In this work, we have questioned the role of exogenous, neuronal type I interferons (IFNs-I) in regulating ZIKV-induced microglia's reactivity. Primary cultured microglial cells were either treated with conditioned media from ZIKV-infected mature neurons or co-cultured with ZIKV-infected neurons. Using either an antibody directed against the IFNAR receptor that neutralizes the IFNs-I response or Ifnar-/-microglial cells, we demonstrate that IFNs-I produced by ZIKV-infected neurons are the main regulators of the phagocytic capacity and the pro-inflammatory gene expression profile of reactive, non-infected microglial cells. We identify protein kinase R (PKR), whose expression is activated by IFNs-I, as a major regulator of the phagocytic capacity, pro-inflammatory response, and morphological changes of microglia induced by IFNs-I while up-regulating STAT1 phosphorylation and IRF1 expression. Results obtained herein in vitro with primary cultured cells and in vivo in ZIKV-infected adult immunocompetent mice, unravel a role for IFNs-I and PKR in directly regulating microglia's reactivity that could be at work in other infectious and non-infectious brain pathologies.

Influence of interpersonal distance on collaborative performance in the joint Simon task-An fNIRS-based hyperscanning study.

Collaboration is a critical skill in everyday life. It has been suggested that collaborative performance may be influenced by social factors such as interpersonal distance, which is defined as the perceived psychological distance between individuals. Previous literature has reported that close interpersonal distance may promote the level of self-other integration between interacting members, and in turn, enhance collaborative performance. These studies mainly focused on interdependent collaboration, which requires high levels of shared representations and self-other integration. However, little is known about the effect of interpersonal distance on independent collaboration (e.g., the joint Simon task), in which individuals perform the task independently while the final outcome is determined by the parties. To address this issue, we simultaneously measured the frontal activations of ninety-four pairs of participants using a functional near-infrared spectroscopy (fNIRS)-based hyperscanning technique while they performed a joint Simon task. Behavioral results showed that the Joint Simon Effect (JSE), defined as the RT difference between incongruent and congruent conditions indicating the level of self-other integration between collaborators, was larger in the friend group than in the stranger group. Consistently, the inter-brain neural synchronization (INS) across the dorsolateral and medial parts of the prefrontal cortex was also stronger in the friend group. In addition, INS in the left dorsolateral prefrontal cortex negatively predicted JSE only in the friend group. These results suggest that close interpersonal distance may enhance the shared mental representation among collaborators, which in turn influences their collaborative performance.

Neural mechanisms distinguishing two types of cooperative problem-solving approaches: An fNIRS hyperscanning study.

Collaborative cooperation (CC) and division of labor cooperation (DLC) are two prevalent forms of cooperative problem-solving approaches in daily life. Despite extensive research on the neural mechanisms underlying cooperative problem-solving approaches, a notable gap exists between the neural processes that support CC and DLC. The present study utilized a functional near-infrared spectroscopy (fNIRS) hyperscanning technique along with a classic cooperative tangram puzzle task to investigate the neural mechanisms engaged by both friends and stranger dyads during CC versus DLC. The key findings of this study were as follows: (1) Dyads exhibited superior behavioral performance in the DLC task than in the CC task. The CC task bolstered intra-brain functional connectivity and inter-brain synchrony (IBS) in regions linked to the mirror neuron system (MNS), spatial perception (SP) and cognitive control. (2) Friend dyads showed stronger IBS in brain regions associated with the MNS than stranger dyads. (3) Perspective-taking predicted not only dyads' behavioral performance in the CC task but also their IBS in brain regions associated with SP during the DLC task. Taken together, these findings elucidate the divergent behavioral performance and neural connection patterns between the two cooperative problem-solving approaches. This study provides novel insights into the various neurocognitive processes underlying flexible coordination strategies in real-world cooperative contexts.

Analysis of immune-related adverse events in gastrointestinal malignancy patients treated with immune checkpoint inhibitors.

Immune checkpoint inhibitors are becoming an increasingly common treatment for advanced gastrointestinal cancer, but the possibility of immune-related adverse events has raised concerns. This study aimed to evaluate the risks of immune-related adverse events between patients who received immune checkpoint inhibitors and those who received chemotherapy among different types of gastrointestinal cancer. The study utilized data from the multicenter TriNetX database in the United States covering the period between 2015 and 2022. Hazard ratios and 95% confidence intervals were used to describe the relative hazard of immune-related adverse events based on comparing time-to-event rates. Our study revealed that the incidence of immune-related adverse events was significantly higher in patients who received immune checkpoint inhibitors and chemotherapy compared to those who received chemotherapy only in treating gastrointestinal cancer. CTLA-4 inhibitors tended to have a higher rate of immune-related adverse events compared to PD-1/PD-L1 inhibitors. Our study found a lower mortality rate among patients who developed immune-related adverse events compared to those who did not after propensity score matching (HR, 0.661; 95% CI 0.620-0.704; p < .01). We provide important real-world data on the incidence and impact of immune-related adverse events in patients with advanced gastrointestinal cancer treated with immune checkpoint inhibitors. Our study's results support clinicians in making informed decisions about the potential benefits and risks of immune checkpoint inhibitor therapy for patients with gastrointestinal cancer.

A historical cohort study with 27,754 individuals on the association between meat consumption and gastrointestinal tract and colorectal cancer incidence.

In order to explore the association between meat consumption and gastrointestinal/colorectal cancer (CRC) risk and to estimate the Israeli population attributable fraction (PAF), we conducted a collaborative historical cohort study using the individual participant data of seven nutritional studies from the past 6 decades. We included healthy adult men and women who underwent a nutritional interview. Dietary assessment data, using food-frequency or 24-h recall questionnaires, were harmonized. The study file was linked to the National Cancer and death registries. Among 27,754 participants, 1216 (4.4%) were diagnosed with gastrointestinal cancers and 839 (3.0%) with CRC by the end of 2016. Using meta-analysis methods applied to Cox proportional hazard models (adjusted for daily energy intake, sex, age, ethnic origin, education and smoking),100 g/day increments in beef, red meat and poultry consumption, and 50 g/day increment in processed meat consumption were associated with hazard ratios (HRs) and 95% confidence intervals of 1.46 (1.06-2.02), 1.15 (0.87-1.52), 1.06 (0.89-1.26), and 0.93 (0.76-1.12), respectively, for CRC. Similar results were obtained for gastrointestinal cancer, although red meat consumption reached statistical significance (HR = 1.27; 95%CI: 1.02-1.58). The PAFs associated with a reduction to a maximum of 50 g/day in the consumption of red meat were 2.7% (95%CI: -1.9 to 12.0) and 5.2% (0.3-13.9) for CRC and gastrointestinal cancers, respectively. Reduction of beef consumption to a maximum of 50 g/day will result in a CRC PAF reduction of 7.5% (0.7%-24.3%). While beef consumption was associated with gastrointestinal/CRC excess risk, poultry consumption was not. A substantial part of processed meat consumption in Israel is processed poultry, perhaps explaining the lack of association with CRC.

Mouse Genetic Reference Populations: Cellular Platforms for Integrative Systems Genetics.

Interrogation of disease-relevant cellular and molecular traits exhibited by genetically diverse cell populations enables in vitro systems genetics approaches for uncovering the basic properties of cellular function and identity. Primary cells, stem cells, and organoids derived from genetically diverse mouse strains, such as Collaborative Cross and Diversity Outbred populations, offer the opportunity for parallel in vitro/in vivo screening. These panels provide genetic resolution for variant discovery and functional characterization, as well as disease modeling and in vivo validation capabilities. Here we review mouse cellular systems genetics approaches for characterizing the influence of genetic variation on signaling networks and phenotypic diversity, and we discuss approaches for data integration and cross-species validation.

Variations in the germinal center response revealed by genetically diverse mouse strains.

The humoral response is complex and involves multiple cellular populations and signaling pathways. Bacterial and viral infections, as well as immunization regimens, can trigger this type of response, promoting the formation of microanatomical cellular structures called germinal centers (GCs). GCs formed in secondary lymphoid organs support the differentiation of high-affinity plasma cells and memory B cells. There is growing evidence that the quality of the humoral response is influenced by genetic variants. Using 12 genetically divergent mouse strains, we assessed the impact of genetics on GC cellular traits. At steady state, in the spleen, lymph nodes and Peyer's patches, we quantified GC B cells, plasma cells and follicular helper T cells. These traits were also quantified in the spleen of mice following immunization with a foreign antigen, namely, sheep red blood cells, in addition to the number and size of GCs. We observed both strain- and organ-specific variations in cell type abundance, as well as for GC number and size. Moreover, we find that some of these traits are highly heritable. Importantly, the results of this study inform on the impact of genetic diversity in shaping the GC response and identify the traits that are the most impacted by genetic background.

One-Step Cooperative Growth of High Reaction Kinetics Composite Homogeneous Core-Shell Heterostructure.

Reaction kinetics can be improved by the enhanced electrical contact between different components growing symbiotically. But so far, due to the necessity for material synthesis conditions match, the component structures of cooperative growth are similar, and the materials are of the same type. The collaborative growth of high-reaction kinetics composite homogeneous core-shell heterostructure between various materials is innovatively proposed with different structures in one step. The NiCo-LDH and PPy successfully symbiotically grow on activated carbon fiber fabric in one step. The open channel structure of the NiCo-LDH nanosheets is preserved while PPy effectively wrapped around the NiCo-LDH. The well-defined nanostructure with abundant active sites and convenient ion diffusion paths is favorable for electrolyte entry into the entire nanoarrays. In addition, owing to the enhanced electronic interaction between different components through XPS analysis, the NiCo-LDH@PPy electrode shows outstanding reaction kinetics and structural stability. The as-synthesized NiCo-LDH@PPy exhibited excellent super-capacitive storage capabilities, robust capacitive activity, and good rate survival. Furthermore, an asymmetric supercapacitor (ASC) device made of NiCo-LDH@PPy and activated carbon (AC) is able to maintain a long cycle life while achieving high power and energy densities.

Neuroinvasive Flavivirus Pathogenesis Is Restricted by Host Genetic Factors in Collaborative Cross Mice, Independently of Oas1b.

Powassan virus (POWV) is an emerging tick-borne flavivirus that causes neuroinvasive diseases, including encephalitis, meningitis, and paralysis. Similar to other neuroinvasive flaviviruses, such as West Nile virus (WNV) and Japanese encephalitis virus (JEV), POWV disease presentation is heterogeneous, and the factors influencing disease outcome are not fully understood. We used Collaborative Cross (CC) mice to assess the impact of host genetic factors on POWV pathogenesis. We infected a panel of Oas1b-null CC lines with POWV and observed a range of susceptibility, indicating that host factors other than the well-characterized flavivirus restriction factor Oas1b modulate POWV pathogenesis in CC mice. Among the Oas1b-null CC lines, we identified multiple highly susceptible lines (0% survival), including CC071 and CC015, and two resistant lines, CC045 and CC057 (>75% survival). The susceptibility phenotypes generally were concordant among neuroinvasive flaviviruses, although we did identify one line, CC006, that was specifically resistant to JEV, suggesting that both pan-flavivirus and virus-specific mechanisms contribute to susceptibility phenotypes in CC mice. We found that POWV replication was restricted in bone marrow-derived macrophages from CC045 and CC057 mice, suggesting that resistance could result from cell-intrinsic restriction of viral replication. Although serum viral loads at 2 days postinfection were equivalent between resistant and susceptible CC lines, clearance of POWV from the serum was significantly enhanced in CC045 mice. Furthermore, CC045 mice had significantly lower viral loads in the brain at 7 days postinfection than did CC071 mice, suggesting that reduced central nervous system (CNS) infection contributes to the resistant phenotype of CC045 mice. IMPORTANCE Neuroinvasive flaviviruses, such as WNV, JEV, and POWV, are transmitted to humans by mosquitoes or ticks and can cause neurologic diseases, such as encephalitis, meningitis, and paralysis, and they can result in death or long-term sequelae. Although potentially severe, neuroinvasive disease is a rare outcome of flavivirus infection. The factors that determine whether someone develops severe disease after a flavivirus infection are not fully understood, but host genetic differences in polymorphic antiviral response genes likely contribute to the outcome of infection. We evaluated a panel of genetically diverse mice and identified lines with distinct outcomes following infection with POWV. We found that resistance to POWV pathogenesis corresponded to reduced viral replication in macrophages, more rapid clearance of virus in peripheral tissues, and reduced viral infection in the brain. These susceptible and resistant mouse lines will provide a system for investigating the pathogenic mechanisms of POWV and identifying polymorphic host genes that contribute to resistance.

Drug-target interactions prediction via deep collaborative filtering with multiembeddings.

Drug-target interactions (DTIs) prediction research presents important significance for promoting the development of modern medicine and pharmacology. Traditional biochemical experiments for DTIs prediction confront the challenges including long time period, high cost and high failure rate, and finally leading to a low-drug productivity. Chemogenomic-based computational methods can realize high-throughput prediction. In this study, we develop a deep collaborative filtering prediction model with multiembeddings, named DCFME (deep collaborative filtering prediction model with multiembeddings), which can jointly utilize multiple feature information from multiembeddings. Two different representation learning algorithms are first employed to extract heterogeneous network features. DCFME uses the generated low-dimensional dense vectors as input, and then simulates the drug-target relationship from the perspective of both couplings and heterogeneity. In addition, the model employs focal loss that concentrates the loss on sparse and hard samples in the training process. Comparative experiments with five baseline methods show that DCFME achieves more significant performance improvement on sparse datasets. Moreover, the model has better robustness and generalization capacity under several harder prediction scenarios.

Dual-Network Collaborative Matrix Factorization for predicting small molecule-miRNA associations.

MicroRNAs (miRNAs) play crucial roles in multiple biological processes and human diseases and can be considered as therapeutic targets of small molecules (SMs). Because biological experiments used to verify SM-miRNA associations are time-consuming and expensive, it is urgent to propose new computational models to predict new SM-miRNA associations. Here, we proposed a novel method called Dual-network Collaborative Matrix Factorization (DCMF) for predicting the potential SM-miRNA associations. Firstly, we utilized the Weighted K Nearest Known Neighbors (WKNKN) method to preprocess SM-miRNA association matrix. Then, we constructed matrix factorization model to obtain two feature matrices containing latent features of SM and miRNA, respectively. Finally, the predicted SM-miRNA association score matrix was obtained by calculating the inner product of two feature matrices. The main innovations of this method were that the use of WKNKN method can preprocess the missing values of association matrix and the introduction of dual network can integrate more diverse similarity information into DCMF. For evaluating the validity of DCMF, we implemented four different cross validations (CVs) based on two distinct datasets and two different case studies. Finally, based on dataset 1 (dataset 2), DCMF achieved Area Under receiver operating characteristic Curves (AUC) of 0.9868 (0.8770), 0.9833 (0.8836), 0.8377 (0.7591) and 0.9836 ± 0.0030 (0.8632 ± 0.0042) in global Leave-One-Out Cross Validation (LOOCV), miRNA-fixed local LOOCV, SM-fixed local LOOCV and 5-fold CV, respectively. For case studies, plenty of predicted associations have been confirmed by published experimental literature. Therefore, DCMF is an effective tool to predict potential SM-miRNA associations.

Integrating perspectives in developing mycorrhizal trait databases: a call for inclusive and collaborative continental efforts.

Global assessments of mycorrhizal symbiosis present large sampling gaps in rich biodiversity regions. Filling these gaps is necessary to build large-scale, unbiased mycorrhizal databases to obtain reliable analyses and prevent misleading generalizations. Underrepresented regions in mycorrhizal research are mainly in Africa, Asia, and South America. Despite the high biodiversity and endemism in these regions, many groups of organisms remain understudied, especially mycorrhizal fungi. In this Viewpoint, we emphasize the importance of inclusive and collaborative continental efforts in integrating perspectives for comprehensive trait database development and propose a conceptual framework that can help build large mycorrhizal databases in underrepresented regions. Based on the four Vs of big data (volume, variety, veracity, and velocity), we identify the main challenges of constructing a large mycorrhizal dataset and propose solutions for each challenge. We share our collaborative methodology, which involves employing open calls and working groups to engage all mycorrhizal researchers in the region to build a South American Mycorrhizal Database. By fostering interdisciplinary collaborations and embracing a continental-scale approach, we can create robust mycorrhizal trait databases that provide valuable insights into the evolution, ecology, and functioning of mycorrhizal associations, reducing the geographical biases that are so common in large-scale ecological studies.