Arable soil nitrogen dynamics reflect organic inputs via the extended composite phenotype.

Achieving food security requires resilient agricultural systems with improved nutrient-use efficiency, optimized water and nutrient storage in soils, and reduced gaseous emissions. Success relies on understanding coupled nitrogen and carbon metabolism in soils, their associated influences on soil structure and the processes controlling nitrogen transformations at scales relevant to microbial activity. Here we show that the influence of organic matter on arable soil nitrogen transformations can be decoded by integrating metagenomic data with soil structural parameters. Our approach provides a mechanistic explanation of why organic matter is effective in reducing nitrous oxide losses while supporting system resilience. The relationship between organic carbon, soil-connected porosity and flow rates at scales relevant to microbes suggests that important increases in nutrient-use efficiency could be achieved at lower organic carbon stocks than currently envisaged.

Scalable in-memory processing of omics workflows.

We present a proof of concept implementation of the in-memory computing paradigm that we use to facilitate the analysis of metagenomic sequencing reads. In doing so we compare the performance of POSIX™file systems and key-value storage for omics data, and we show the potential for integrating high-performance computing (HPC) and cloud native technologies. We show that in-memory key-value storage offers possibilities for improved handling of omics data through more flexible and faster data processing. We envision fully containerized workflows and their deployment in portable micro-pipelines with multiple instances working concurrently with the same distributed in-memory storage. To highlight the potential usage of this technology for event driven and real-time data processing, we use a biological case study focused on the growing threat of antimicrobial resistance (AMR). We develop a workflow encompassing bioinformatics and explainable machine learning (ML) to predict life expectancy of a population based on the microbiome of its sewage while providing a description of AMR contribution to the prediction. We propose that in future, performing such analyses in 'real-time' would allow us to assess the potential risk to the population based on changes in the AMR profile of the community.

Combining explainable machine learning, demographic and multi-omic data to inform precision medicine strategies for inflammatory bowel disease.

Inflammatory bowel diseases (IBDs), including ulcerative colitis and Crohn's disease, affect several million individuals worldwide. These diseases are heterogeneous at the clinical, immunological and genetic levels and result from complex host and environmental interactions. Investigating drug efficacy for IBD can improve our understanding of why treatment response can vary between patients. We propose an explainable machine learning (ML) approach that combines bioinformatics and domain insight, to integrate multi-modal data and predict inter-patient variation in drug response. Using explanation of our models, we interpret the ML models' predictions to infer unique combinations of important features associated with pharmacological responses obtained during preclinical testing of drug candidates in ex vivo patient-derived fresh tissues. Our inferred multi-modal features that are predictive of drug efficacy include multi-omic data (genomic and transcriptomic), demographic, medicinal and pharmacological data. Our aim is to understand variation in patient responses before a drug candidate moves forward to clinical trials. As a pharmacological measure of drug efficacy, we measured the reduction in the release of the inflammatory cytokine TNFα from the fresh IBD tissues in the presence/absence of test drugs. We initially explored the effects of a mitogen-activated protein kinase (MAPK) inhibitor; however, we later showed our approach can be applied to other targets, test drugs or mechanisms of interest. Our best model predicted TNFα levels from demographic, medicinal and genomic features with an error of only 4.98% on unseen patients. We incorporated transcriptomic data to validate insights from genomic features. Our results showed variations in drug effectiveness (measured by ex vivo assays) between patients that differed in gender, age or condition and linked new genetic polymorphisms to patient response variation to the anti-inflammatory treatment BIRB796 (Doramapimod). Our approach models IBD drug response while also identifying its most predictive features as part of a transparent ML precision medicine strategy.

Bluster or Lustre: Can AI Improve Crops and Plant Health?

In a changing climate where future food security is a growing concern, researchers are exploring new methods and technologies in the effort to meet ambitious crop yield targets. The application of Artificial Intelligence (AI) including Machine Learning (ML) methods in this area has been proposed as a potential mechanism to support this. This review explores current research in the area to convey the state-of-the-art as to how AI/ML have been used to advance research, gain insights, and generally enable progress in this area. We address the question-Can AI improve crops and plant health? We further discriminate the bluster from the lustre by identifying the key challenges that AI has been shown to address, balanced with the potential issues with its usage, and the key requisites for its success. Overall, we hope to raise awareness and, as a result, promote usage, of AI related approaches where they can have appropriate impact to improve practices in agricultural and plant sciences.

Interpreting machine learning models to investigate circadian regulation and facilitate exploration of clock function.

The circadian clock is an important adaptation to life on Earth. Here, we use machine learning to predict complex, temporal, and circadian gene expression patterns in Arabidopsis Most significantly, we classify circadian genes using DNA sequence features generated de novo from public, genomic resources, facilitating downstream application of our methods with no experimental work or prior knowledge needed. We use local model explanation that is transcript specific to rank DNA sequence features, providing a detailed profile of the potential circadian regulatory mechanisms for each transcript. Furthermore, we can discriminate the temporal phase of transcript expression using the local, explanation-derived, and ranked DNA sequence features, revealing hidden subclasses within the circadian class. Model interpretation/explanation provides the backbone of our methodological advances, giving insight into biological processes and experimental design. Next, we use model interpretation to optimize sampling strategies when we predict circadian transcripts using reduced numbers of transcriptomic timepoints. Finally, we predict the circadian time from a single, transcriptomic timepoint, deriving marker transcripts that are most impactful for accurate prediction; this could facilitate the identification of altered clock function from existing datasets.

Uncovering candidate genes involved in photosynthetic capacity using unexplored genetic variation in Spring Wheat.

To feed an ever-increasing population we must leverage advances in genomics and phenotyping to harness the variation in wheat breeding populations for traits like photosynthetic capacity which remains unoptimized. Here we survey a diverse set of wheat germplasm containing elite, introgression and synthetic derivative lines uncovering previously uncharacterized variation. We demonstrate how strategic integration of exotic material alleviates the D genome genetic bottleneck in wheat, increasing SNP rate by 62% largely due to Ae. tauschii synthetic wheat donors. Across the panel, 67% of the Ae. tauschii donor genome is represented as introgressions in elite backgrounds. We show how observed genetic variation together with hyperspectral reflectance data can be used to identify candidate genes for traits relating to photosynthetic capacity using association analysis. This demonstrates the value of genomic methods in uncovering hidden variation in wheat and how that variation can assist breeding efforts and increase our understanding of complex traits.

Explainable AI reveals changes in skin microbiome composition linked to phenotypic differences.

Alterations in the human microbiome have been observed in a variety of conditions such as asthma, gingivitis, dermatitis and cancer, and much remains to be learned about the links between the microbiome and human health. The fusion of artificial intelligence with rich microbiome datasets can offer an improved understanding of the microbiome's role in human health. To gain actionable insights it is essential to consider both the predictive power and the transparency of the models by providing explanations for the predictions. We combine the collection of leg skin microbiome samples from two healthy cohorts of women with the application of an explainable artificial intelligence (EAI) approach that provides accurate predictions of phenotypes with explanations. The explanations are expressed in terms of variations in the relative abundance of key microbes that drive the predictions. We predict skin hydration, subject's age, pre/post-menopausal status and smoking status from the leg skin microbiome. The changes in microbial composition linked to skin hydration can accelerate the development of personalized treatments for healthy skin, while those associated with age may offer insights into the skin aging process. The leg microbiome signatures associated with smoking and menopausal status are consistent with previous findings from oral/respiratory tract microbiomes and vaginal/gut microbiomes respectively. This suggests that easily accessible microbiome samples could be used to investigate health-related phenotypes, offering potential for non-invasive diagnosis and condition monitoring. Our EAI approach sets the stage for new work focused on understanding the complex relationships between microbial communities and phenotypes. Our approach can be applied to predict any condition from microbiome samples and has the potential to accelerate the development of microbiome-based personalized therapeutics and non-invasive diagnostics.

Re-purposing software for functional characterization of the microbiome.

BACKGROUND: Widespread bioinformatic resource development generates a constantly evolving and abundant landscape of workflows and software. For analysis of the microbiome, workflows typically begin with taxonomic classification of the microorganisms that are present in a given environment. Additional investigation is then required to uncover the functionality of the microbial community, in order to characterize its currently or potentially active biological processes. Such functional analysis of metagenomic data can be computationally demanding for high-throughput sequencing experiments. Instead, we can directly compare sequencing reads to a functionally annotated database. However, since reads frequently match multiple sequences equally well, analyses benefit from a hierarchical annotation tree, e.g. for taxonomic classification where reads are assigned to the lowest taxonomic unit. RESULTS: To facilitate functional microbiome analysis, we re-purpose well-known taxonomic classification tools to allow us to perform direct functional sequencing read classification with the added benefit of a functional hierarchy. To enable this, we develop and present a tree-shaped functional hierarchy representing the molecular function subset of the Gene Ontology annotation structure. We use this functional hierarchy to replace the standard phylogenetic taxonomy used by the classification tools and assign query sequences accurately to the lowest possible molecular function in the tree. We demonstrate this with simulated and experimental datasets, where we reveal new biological insights. CONCLUSIONS: We demonstrate that improved functional classification of metagenomic sequencing reads is possible by re-purposing a range of taxonomic classification tools that are already well-established, in conjunction with either protein or nucleotide reference databases. We leverage the advances in speed, accuracy and efficiency that have been made for taxonomic classification and translate these benefits for the rapid functional classification of microbiomes. While we focus on a specific set of commonly used methods, the functional annotation approach has broad applicability across other sequence classification tools. We hope that re-purposing becomes a routine consideration during bioinformatic resource development. Video abstract.

Reduced chromatin accessibility underlies gene expression differences in homologous chromosome arms of diploid Aegilops tauschii and hexaploid wheat.

BACKGROUND: Polyploidy is centrally important in the evolution and domestication of plants because it leads to major genomic changes, such as altered patterns of gene expression, which are thought to underlie the emergence of new traits. Despite the common occurrence of these globally altered patterns of gene expression in polyploids, the mechanisms involved are not well understood. RESULTS: Using a precisely defined framework of highly conserved syntenic genes on hexaploid wheat chromosome 3DL and its progenitor 3 L chromosome arm of diploid Aegilops tauschii, we show that 70% of these gene pairs exhibited proportionately reduced gene expression, in which expression in the hexaploid context of the 3DL genes was ∼40% of the levels observed in diploid Ae tauschii. Several genes showed elevated expression during the later stages of grain development in wheat compared with Ae tauschii. Gene sequence and methylation differences probably accounted for only a few cases of differences in gene expression. In contrast, chromosome-wide patterns of reduced chromatin accessibility of genes in the hexaploid chromosome arm compared with its diploid progenitor were correlated with both reduced gene expression and the imposition of new patterns of gene expression. CONCLUSIONS: Our pilot-scale analyses show that chromatin compaction may orchestrate reduced gene expression levels in the hexaploid chromosome arm of wheat compared to its diploid progenitor chromosome arm.

Using human in vitro transcriptome analysis to build trustworthy machine learning models for prediction of animal drug toxicity.

During the development of new drugs or compounds there is a requirement for preclinical trials, commonly involving animal tests, to ascertain the safety of the compound prior to human trials. Machine learning techniques could provide an in-silico alternative to animal models for assessing drug toxicity, thus reducing expensive and invasive animal testing during clinical trials, for drugs that are most likely to fail safety tests. Here we present a machine learning model to predict kidney dysfunction, as a proxy for drug induced renal toxicity, in rats. To achieve this, we use inexpensive transcriptomic profiles derived from human cell lines after chemical compound treatment to train our models combined with compound chemical structure information. Genomics data due to its sparse, high-dimensional and noisy nature presents significant challenges in building trustworthy and transparent machine learning models. Here we address these issues by judiciously building feature sets from heterogenous sources and coupling them with measures of model uncertainty achieved through Gaussian Process based Bayesian models. We combine the use of insight into the feature-wise contributions to our predictions with the use of predictive uncertainties recovered from the Gaussian Process to improve the transparency and trustworthiness of the model.

A framework for gene mapping in wheat demonstrated using the Yr7 yellow rust resistance gene.

We used three approaches to map the yellow rust resistance gene Yr7 and identify associated SNPs in wheat. First, we used a traditional QTL mapping approach using a double haploid (DH) population and mapped Yr7 to a low-recombination region of chromosome 2B. To fine map the QTL, we then used an association mapping panel. Both populations were SNP array genotyped allowing alignment of QTL and genome-wide association scans based on common segregating SNPs. Analysis of the association panel spanning the QTL interval, narrowed the interval down to a single haplotype block. Finally, we used mapping-by-sequencing of resistant and susceptible DH bulks to identify a candidate gene in the interval showing high homology to a previously suggested Yr7 candidate and to populate the Yr7 interval with a higher density of polymorphisms. We highlight the power of combining mapping-by-sequencing, delivering a complete list of gene-based segregating polymorphisms in the interval with the high recombination, low LD precision of the association mapping panel. Our mapping-by-sequencing methodology is applicable to any trait and our results validate the approach in wheat, where with a near complete reference genome sequence, we are able to define a small interval containing the causative gene.

Hierarchically Labeled Database Indexing Allows Scalable Characterization of Microbiomes.

Increasingly available microbial reference data allow interpreting the composition and function of previously uncharacterized microbial communities in detail, via high-throughput sequencing analysis. However, efficient methods for read classification are required when the best database matches for short sequence reads are often shared among multiple reference sequences. Here, we take advantage of the fact that microbial sequences can be annotated relative to established tree structures, and we develop a highly scalable read classifier, PRROMenade, by enhancing the generalized Burrows-Wheeler transform with a labeling step to directly assign reads to the corresponding lowest taxonomic unit in an annotation tree. PRROMenade solves the multi-matching problem while allowing fast variable-size sequence classification for phylogenetic or functional annotation. Our simulations with 5% added differences from reference indicated only 1.5% error rate for PRROMenade functional classification. On metatranscriptomic data PRROMenade highlighted biologically relevant functional pathways related to diet-induced changes in the human gut microbiome.

Understanding DNA Methylation Patterns in Wheat.

The bread wheat genome is large (17 Gb), allohexaploid, and highly repetitive (80-90% of the genome), which makes genomic and epigenomic analyses expensive to conduct and a challenge to analyze. Here we provide an overview of recent bioinformatic and experimental methods that have been developed to understand DNA methylation patterns in the complex polyploid genome of wheat.

Analysis of the recombination landscape of hexaploid bread wheat reveals genes controlling recombination and gene conversion frequency.

BACKGROUND: Sequence exchange between homologous chromosomes through crossing over and gene conversion is highly conserved among eukaryotes, contributing to genome stability and genetic diversity. A lack of recombination limits breeding efforts in crops; therefore, increasing recombination rates can reduce linkage drag and generate new genetic combinations. RESULTS: We use computational analysis of 13 recombinant inbred mapping populations to assess crossover and gene conversion frequency in the hexaploid genome of wheat (Triticum aestivum). We observe that high-frequency crossover sites are shared between populations and that closely related parents lead to populations with more similar crossover patterns. We demonstrate that gene conversion is more prevalent and covers more of the genome in wheat than in other plants, making it a critical process in the generation of new haplotypes, particularly in centromeric regions where crossovers are rare. We identify quantitative trait loci for altered gene conversion and crossover frequency and confirm functionality for a novel RecQ helicase gene that belongs to an ancient clade that is missing in some plant lineages including Arabidopsis. CONCLUSIONS: This is the first gene to be demonstrated to be involved in gene conversion in wheat. Harnessing the RecQ helicase has the potential to break linkage drag utilizing widespread gene conversions.

Integrating genomic resources to present full gene and putative promoter capture probe sets for bread wheat.

BACKGROUND: Whole-genome shotgun resequencing of wheat is expensive because of its large, repetitive genome. Moreover, sequence data can fail to map uniquely to the reference genome, making it difficult to unambiguously assign variation. Resequencing using target capture enables sequencing of large numbers of individuals at high coverage to reliably identify variants associated with important agronomic traits. Previous studies have implemented complementary DNA/exon or gene-based probe sets in which the promoter and intron sequence is largely missing alongside newly characterized genes from the recent improved reference sequences. RESULTS: We present and validate 2 gold standard capture probe sets for hexaploid bread wheat, a gene and a putative promoter capture, which are designed using recently developed genome sequence and annotation resources. The captures can be combined or used independently. We demonstrate that the capture probe sets effectively enrich the high-confidence genes and putative promoter regions that were identified in the genome alongside a large proportion of the low-confidence genes and associated promoters. Finally, we demonstrate successful sample multiplexing that allows generation of adequate sequence coverage for single-nucleotide polymorphism calling while significantly reducing cost per sample for gene and putative promoter capture. CONCLUSIONS: We show that a capture design employing an "island strategy" can enable analysis of the large gene/putative promoter space of wheat with only 2 × 160 Mbp probe sets. Furthermore, these assays extend the regions of the wheat genome that are amenable to analyses beyond its exome, providing tools for detailed characterization of these regulatory regions in large populations.

Elucidating the genetic basis of biomass accumulation and radiation use efficiency in spring wheat and its role in yield potential.

One of the major challenges for plant scientists is increasing wheat (Triticum aestivum) yield potential (YP). A significant bottleneck for increasing YP is achieving increased biomass through optimization of radiation use efficiency (RUE) along the crop cycle. Exotic material such as landraces and synthetic wheat has been incorporated into breeding programmes in an attempt to alleviate this; however, their contribution to YP is still unclear. To understand the genetic basis of biomass accumulation and RUE, we applied genome-wide association study (GWAS) to a panel of 150 elite spring wheat genotypes including many landrace and synthetically derived lines. The panel was evaluated for 31 traits over 2 years under optimal growing conditions and genotyped using the 35K wheat breeders array. Marker-trait association identified 94 SNPs significantly associated with yield, agronomic and phenology-related traits along with RUE and final biomass (BM_PM) at various growth stages that explained 7%-17% of phenotypic variation. Common SNP markers were identified for grain yield, BM_PM and RUE on chromosomes 5A and 7A. Additionally, landrace and synthetic derivative lines showed higher thousand grain weight (TGW), BM_PM and RUE but lower grain number (GM2) and harvest index (HI). Our work demonstrates the use of exotic material as a valuable resource to increase YP. It also provides markers for use in marker-assisted breeding to systematically increase BM_PM, RUE and TGW and avoid the TGW/GM2 and BM_PM/HI trade-off. Thus, achieving greater genetic gains in elite germplasm while also highlighting genomic regions and candidate genes for further study.

Hidden variation in polyploid wheat drives local adaptation.

Wheat has been domesticated into a large number of agricultural environments and has the ability to adapt to diverse environments. To understand this process, we survey genotype, repeat content, and DNA methylation across a bread wheat landrace collection representing global genetic diversity. We identify independent variation in methylation, genotype, and transposon copy number. We show that these, so far unexploited, sources of variation have had a significant impact on the wheat genome and that ancestral methylation states become preferentially "hard coded" as single nucleotide polymorphisms (SNPs) via 5-methylcytosine deamination. These mechanisms also drive local adaption, impacting important traits such as heading date and salt tolerance. Methylation and transposon diversity could therefore be used alongside SNP-based markers for breeding.

A modified sequence capture approach allowing standard and methylation analyses of the same enriched genomic DNA sample.

BACKGROUND: Bread wheat has a large complex genome that makes whole genome resequencing costly. Therefore, genome complexity reduction techniques such as sequence capture make re-sequencing cost effective. With a high-quality draft wheat genome now available it is possible to design capture probe sets and to use them to accurately genotype and anchor SNPs to the genome. Furthermore, in addition to genetic variation, epigenetic variation provides a source of natural variation contributing to changes in gene expression and phenotype that can be profiled at the base pair level using sequence capture coupled with bisulphite treatment. Here, we present a new 12 Mbp wheat capture probe set, that allows both the profiling of genotype and methylation from the same DNA sample. Furthermore, we present a method, based on Agilent SureSelect Methyl-Seq, that will use a single capture assay as a starting point to allow both DNA sequencing and methyl-seq. RESULTS: Our method uses a single capture assay that is sequentially split and used for both DNA sequencing and methyl-seq. The resultant genotype and epi-type data is highly comparable in terms of coverage and SNP/methylation site identification to that generated from separate captures for DNA sequencing and methyl-seq. Furthermore, by defining SNP frequencies in a diverse landrace from the Watkins collection we highlight the importance of having genotype data to prevent false positive methylation calls. Finally, we present the design of a new 12 Mbp wheat capture and demonstrate its successful application to re-sequence wheat. CONCLUSIONS: We present a cost-effective method for performing both DNA sequencing and methyl-seq from a single capture reaction thus reducing reagent costs, sample preparation time and DNA requirements for these complementary analyses.

Comparative Mapping and Targeted-Capture Sequencing of the Gametocidal Loci in Aegilops sharonensis.

Gametocidal (Gc) chromosomes or elements in species such as Eig are preferentially transmitted to the next generation through both the male and female gametes when introduced into wheat ( L.). Furthermore, any genes, such as genes that control agronomically important traits, showing complete linkage with Gc elements, are also transmitted preferentially to the next generation without the need for selection. The mechanism for the preferential transmission of the Gc elements appears to occur by the induction of extensive chromosome damage in any gametes that lack the Gc chromosome in question. Previous studies on the mechanism of the Gc action in indicates that at least two linked elements are involved. The first, the element, induces chromosome breakage in gametes, which have lost the Gc elements while the second, the element, prevents the chromosome breakage action of the breaker element in gametes which carry the Gc elements. In this study, we have used comparative genomic studies to map 54 single nucleotide polymorphism (SNP) markers in an 4S introgression segment in wheat and have also identified 18 candidate genes in for the breaker element through targeted sequencing of this 4S introgression segment. This valuable genomic resource will aide in further mapping the Gc locus that could be exploited in wheat breeding to produce new, superior varieties of wheat.

Mapping-by-sequencing in complex polyploid genomes using genic sequence capture: a case study to map yellow rust resistance in hexaploid wheat.

Previously we extended the utility of mapping-by-sequencing by combining it with sequence capture and mapping sequence data to pseudo-chromosomes that were organized using wheat-Brachypodium synteny. This, with a bespoke haplotyping algorithm, enabled us to map the flowering time locus in the diploid wheat Triticum monococcum L. identifying a set of deleted genes (Gardiner et al., 2014). Here, we develop this combination of gene enrichment and sliding window mapping-by-synteny analysis to map the Yr6 locus for yellow stripe rust resistance in hexaploid wheat. A 110 MB NimbleGen capture probe set was used to enrich and sequence a doubled haploid mapping population of hexaploid wheat derived from an Avalon and Cadenza cross. The Yr6 locus was identified by mapping to the POPSEQ chromosomal pseudomolecules using a bespoke pipeline and algorithm (Chapman et al., 2015). Furthermore the same locus was identified using newly developed pseudo-chromosome sequences as a mapping reference that are based on the genic sequence used for sequence enrichment. The pseudo-chromosomes allow us to demonstrate the application of mapping-by-sequencing to even poorly defined polyploidy genomes where chromosomes are incomplete and sub-genome assemblies are collapsed. This analysis uniquely enabled us to: compare wheat genome annotations; identify the Yr6 locus - defining a smaller genic region than was previously possible; associate the interval with one wheat sub-genome and increase the density of SNP markers associated. Finally, we built the pipeline in iPlant, making it a user-friendly community resource for phenotype mapping.