Granger-Causal Testing for Irregularly Sampled Time Series with Application to Nitrogen Signaling in Arabidopsis

Model-to-crop conserved NUE Regulons enhance machine learning predictions of nitrogen use efficiency

Systems biology aims to uncover gene regulatory networks (GRNs) for agricultural traits, but validating them in crops is challenging. We addressed this challenge by learning and validating model-to-crop transcription factor (TF) regulons governing nitrogen use efficiency (NUE). First, a fine-scale time-course nitrogen (N) response transcriptome analysis revealed a conserved temporal N response cascade in maize (Zea mays) and Arabidopsis (Arabidopsis thaliana). These data were used to infer time-based causal TF target edges in N-regulated GRNs. By validating 23 maize TFs in a cell-based TF-perturbation assay (Transient Assay Reporting Genome-wide Effects of Transcription factors), precision/recall analysis enabled us to prune high-confidence edges between ∼200 TFs/700 maize target genes. We next learned gene-to-NUE trait scores using XGBoost machine learning models trained on conserved N-responsive genes across maize and Arabidopsis accessions. By integrating NUE gene scores within our N-GRN, we ranked maize TFs based on a cumulative NUE Regulon score. NUE Regulons for top-ranked TFs were validated using the cell-based TARGET assay in maize (e.g. ZmMYB34/R3→24 targets) and the Arabidopsis ZmMYB34/R3 ortholog (e.g. AtDIV1→23 targets). The genes in this NUE Regulon significantly enhanced the ability of XGBoost models to predict NUE traits in both maize and Arabidopsis. Thus, our pipeline for identifying TF regulons that combines GRN inference, machine learning, and orthologous network regulons offers a strategic framework for crop trait improvement.

Bipartite networks represent causality better than simple networks: evidence, algorithms, and applications

A network, whose nodes are genes and whose directed edges represent positive or negative influences of a regulatory gene and its targets, is often used as a representation of causality. To infer a network, researchers often develop a machine learning model and then evaluate the model based on its match with experimentally verified "gold standard" edges. The desired result of such a model is a network that may extend the gold standard edges. Since networks are a form of visual representation, one can compare their utility with architectural or machine blueprints. Blueprints are clearly useful because they provide precise guidance to builders in construction. If the primary role of gene regulatory networks is to characterize causality, then such networks should be good tools of prediction because prediction is the actionable benefit of knowing causality. But are they? In this paper, we compare prediction quality based on "gold standard" regulatory edges from previous experimental work with non-linear models inferred from time series data across four different species. We show that the same non-linear machine learning models have better predictive performance, with improvements from 5.3% to 25.3% in terms of the reduction in the root mean square error (RMSE) compared with the same models based on the gold standard edges. Having established that networks fail to characterize causality properly, we suggest that causality research should focus on four goals: (i) predictive accuracy; (ii) a parsimonious enumeration of predictive regulatory genes for each target gene g; (iii) the identification of disjoint sets of predictive regulatory genes for each target g of roughly equal accuracy; and (iv) the construction of a bipartite network (whose node types are genes and models) representation of causality. We provide algorithms for all goals.

Nitrogen sensing and regulatory networks: it's about time and space

A plant's response to external and internal nitrogen signals/status relies on sensing and signaling mechanisms that operate across spatial and temporal dimensions. From a comprehensive systems biology perspective, this involves integrating nitrogen responses in different cell types and over long distances to ensure organ coordination in real time and yield practical applications. In this prospective review, we focus on novel aspects of nitrogen (N) sensing/signaling uncovered using temporal and spatial systems biology approaches, largely in the model Arabidopsis. The temporal aspects span: transcriptional responses to N-dose mediated by Michaelis-Menten kinetics, the role of the master NLP7 transcription factor as a nitrate sensor, its nitrate-dependent TF nuclear retention, its "hit-and-run" mode of target gene regulation, and temporal transcriptional cascade identified by "network walking." Spatial aspects of N-sensing/signaling have been uncovered in cell type-specific studies in roots and in root-to-shoot communication. We explore new approaches using single-cell sequencing data, trajectory inference, and pseudotime analysis as well as machine learning and artificial intelligence approaches. Finally, unveiling the mechanisms underlying the spatial dynamics of nitrogen sensing/signaling networks across species from model to crop could pave the way for translational studies to improve nitrogen-use efficiency in crops. Such outcomes could potentially reduce the detrimental effects of excessive fertilizer usage on groundwater pollution and greenhouse gas emissions.

CAT Bridge: an efficient toolkit for gene-metabolite association mining from multiomics data

BACKGROUND

With advancements in sequencing and mass spectrometry technologies, multiomics data can now be easily acquired for understanding complex biological systems. Nevertheless, substantial challenges remain in determining the association between gene-metabolite pairs due to the nonlinear and multifactorial interactions within cellular networks. The complexity arises from the interplay of multiple genes and metabolites, often involving feedback loops and time-dependent regulatory mechanisms that are not easily captured by traditional analysis methods.

FINDINGS

Here, we introduce Compounds And Transcripts Bridge (abbreviated as CAT Bridge, available at https://catbridge.work), a free user-friendly platform for longitudinal multiomics analysis to efficiently identify transcripts associated with metabolites using time-series omics data. To evaluate the association of gene-metabolite pairs, CAT Bridge is a pioneering work benchmarking a set of statistical methods spanning causality estimation and correlation coefficient calculation for multiomics analysis. Additionally, CAT Bridge features an artificial intelligence agent to assist users interpreting the association results.

CONCLUSIONS

We applied CAT Bridge to experimentally obtained Capsicum chinense (chili pepper) and public human and Escherichia coli time-series transcriptome and metabolome datasets. CAT Bridge successfully identified genes involved in the biosynthesis of capsaicin in C. chinense. Furthermore, case study results showed that the convergent cross-mapping method outperforms traditional approaches in longitudinal multiomics analyses. CAT Bridge simplifies access to various established methods for longitudinal multiomics analysis and enables researchers to swiftly identify associated gene-metabolite pairs for further validation.

Dynamic growth QTL action in diverse light environments: characterization of light regime-specific and stable QTL in Arabidopsis

Plant growth is a complex process affected by a multitude of genetic and environmental factors and their interactions. To identify genetic factors influencing plant performance under different environmental conditions, vegetative growth was assessed in Arabidopsis thaliana cultivated under constant or fluctuating light intensities, using high-throughput phenotyping and genome-wide association studies. Daily automated non-invasive phenotyping of a collection of 382 Arabidopsis accessions provided growth data during developmental progression under different light regimes at high temporal resolution. Quantitative trait loci (QTL) for projected leaf area, relative growth rate, and PSII operating efficiency detected under the two light regimes were predominantly condition-specific and displayed distinct temporal activity patterns, with active phases ranging from 2 d to 9 d. Eighteen protein-coding genes and one miRNA gene were identified as potential candidate genes at 10 QTL regions consistently found under both light regimes. Expression patterns of three candidate genes affecting projected leaf area were analysed in time-series experiments in accessions with contrasting vegetative leaf growth. These observations highlight the importance of considering both environmental and temporal patterns of QTL/allele actions and emphasize the need for detailed time-resolved analyses under diverse well-defined environmental conditions to effectively unravel the complex and stage-specific contributions of genes affecting plant growth processes.

A review of causal discovery methods for molecular network analysis

BACKGROUND

With the increasing availability and size of multi-omics datasets, investigating the casual relationships between molecular phenotypes has become an important aspect of exploring underlying biology andgenetics. There are an increasing number of methodlogies that have been developed and applied to moleular networks to investigate these causal interactions.

METHODS

We have introduced and reviewed the available methods for building large-scale causal molecular networks that have been developed and applied in the past decade.

RESULTS

In this review we have identified and summarized the existing methods for infering causality in large-scale causal molecular networks, and discussed important factors that will need to be considered in future research in this area.

CONCLUSION

Existing methods to infering causal molecular networks have their own strengths and limitations so there is no one best approach, and it is instead down to the discretion of the researcher. This review also to discusses some of the current limitations to biological interpretation of these networks, and important factors to consider for future studies on molecular networks.

Histidine kinase inhibitors impair shoot regeneration in Arabidopsis thaliana via cytokinin signaling and SAM patterning determinants

Reversible protein phosphorylation is a post-translational modification involved in virtually all plant processes, as it mediates protein activity and signal transduction. Here, we probe dynamic protein phosphorylation during de novo shoot organogenesis in Arabidopsis thaliana. We find that application of three kinase inhibitors in various time intervals has different effects on root explants. Short exposures to the putative histidine (His) kinase inhibitor TCSA during the initial days on shoot induction medium (SIM) are detrimental for regeneration in seven natural accessions. Investigation of cytokinin signaling mutants, as well as reporter lines for hormone responses and shoot markers, suggests that TCSA impedes cytokinin signal transduction via AHK3, AHK4, AHP3, and AHP5. A mass spectrometry-based phosphoproteome analysis further reveals profound deregulation of Ser/Thr/Tyr phosphoproteins regulating protein modification, transcription, vesicle trafficking, organ morphogenesis, and cation transport. Among TCSA-responsive factors are prior candidates with a role in shoot apical meristem patterning, such as AGO1, BAM1, PLL5, FIP37, TOP1ALPHA, and RBR1, as well as proteins involved in polar auxin transport (e.g., PIN1) and brassinosteroid signaling (e.g., BIN2). Putative novel regeneration determinants regulated by TCSA include RD2, AT1G52780, PVA11, and AVT1C, while NAIP2, OPS, ARR1, QKY, and aquaporins exhibit differential phospholevels on control SIM. LC-MS/MS data are available via ProteomeXchange with identifier PXD030754.

Network inference with Granger causality ensembles on single-cell transcriptomics

Cellular gene expression changes throughout a dynamic biological process, such as differentiation. Pseudotimes estimate cells' progress along a dynamic process based on their individual gene expression states. Ordering the expression data by pseudotime provides information about the underlying regulator-gene interactions. Because the pseudotime distribution is not uniform, many standard mathematical methods are inapplicable for analyzing the ordered gene expression states. Here we present single-cell inference of networks using Granger ensembles (SINGE), an algorithm for gene regulatory network inference from ordered single-cell gene expression data. SINGE uses kernel-based Granger causality regression to smooth irregular pseudotimes and missing expression values. It aggregates predictions from an ensemble of regression analyses to compile a ranked list of candidate interactions between transcriptional regulators and target genes. In two mouse embryonic stem cell differentiation datasets, SINGE outperforms other contemporary algorithms. However, a more detailed examination reveals caveats about poor performance for individual regulators and uninformative pseudotimes.