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Plant organs move throughout the diurnal cycle, changing leaf and petiole positions to balance light capture, leaf temperature, and water loss under dynamic environmental conditions. Upward movement of the petiole, called hyponasty, is one of several traits of the shade avoidance syndrome (SAS). SAS traits are elicited upon perception of vegetation shade signals such as far-red light (FR) and improve light capture in dense vegetation. Monitoring plant movement at a high temporal resolution allows studying functionality and molecular regulation of hyponasty. However, high temporal resolution imaging solutions are often very expensive, making this unavailable to many researchers. Here, we present a modular and low-cost imaging set-up, based on small Raspberry Pi computers, that can track leaf movements and elongation growth with high temporal resolution. We also developed an open-source, semi-automated image analysis pipeline. Using this setup we followed responses to FR enrichment, light intensity, and their interactions. Tracking both elongation and the angle of the petiole, lamina, and entire leaf in Arabidopsis (Arabidopsis thaliana) revealed insight into R:FR sensitivities of leaf growth and movement dynamics and the interactions of R:FR with background light intensity. The detailed imaging options of this system allowed us to identify spatially separate bending points for petiole and lamina positioning of the leaf.

Cover crops (CC) can promote nutrient retention and recycling for main crops yet may also promote soilborne pathogens or suppress beneficial root symbionts such as arbuscular mycorrhizal fungi (AMF). We investigated how root fungal communities of main crop are affected by preceding CC monocultures and mixtures and by main crop identity. We expected that AMF abundance and diversity in main crops are promoted by AM-host CC, and suppressed by non-AM-host CC, and that mixtures of CC species can promote beneficial and suppress pathogenic root fungi. Our full-factorial field experiment comprised crop rotation in sand soil with different CC treatments (monocultures of radish [AM non-host], ryegrass, clover, vetch [AM hosts], mixtures of radish + vetch, ryegrass + clover and fallow) and two main crops (oat and endive). At peak crop growth, we investigated the root fungal communities in the main crops using microscopy and high throughput sequencing (Illumina MiSeq). Cover crop identity was of prime importance and CC legacy overruled main crop identity in determining root fungal communities in main crops. Compared with fallow, CC with ryegrass increased AMF colonization and richness in both main crops and of non-AMF in oat. Legacies of ryegrass, ryegrass + clover and vetch resulted in distinct root fungal communities in the main crops, while the legacy of CC with radish were similar to the legacy of fallow. Root fungal community in crops after clover had highest abundance of representative fungal pathogens in contrast with the other CC treatments that resulted in fungal communities where pathogens were scarce. Oppositely to expected, CC mixtures did not enhance fungal symbionts or suppressed pathogens. Overall, fungal communities in roots of the main crops in our field experiment were determined by the preceding CC species in monoculture, rather than by the CC AMF preference or functional group. This research highlights that the choice of CC determines the root fungal communi

1. Plant species that expand their range in response to current climate change will encounter soil communities that may hinder, allow, or even facilitate plant performance. It has been shown repeatedly for plant species originating from other continents that these plants are less hampered by soil communities from the new than from the original range. However, information about the interactions between intra-continental range expanders and soil communities is sparse, especially at community level. 2. Here we used a plant-soil feedback experiment approach to examine if the interactions between range expanders and soil communities change during range expansion. We grew communities of range-expanding and native plant species with soil communities originating from the original and new range of range expanders. We determined the composition of fungi, bacteria and nematodes in these conditioned soils by high-throughput amplicon sequencing of the ITS region and the 16S rRNA gene and using morphological methods, respectively. Then we tested how these soil communities influence the growth of subsequent communities of range expanders and natives. 3. We found that after the conditioning phase soil bacterial, fungal and nematode communities differed by origin and by conditioning plant communities. Despite differences in bacterial, fungal and nematode communities between original and new range, soil origin did not influence the biomass production of plant communities. Both native and range expanding plant communities produced most aboveground biomass in soils that were conditioned by plant communities distantly related to them. 4. Synthesis. We conclude that communities of range-expanding plant species shape specific soil communities in both original and new range soil. Plant-soil interactions of range expanders in communities can be similar to the ones of their closely related native plant species.

Microbiome research aims to understand the composition, diversity, and function of microbial communities and their interactions with their host organisms. As only a fraction of microbial species can be traditionally isolated and cultivated, advancements in high-throughput technologies have made it possible to generate large-scale microbiome datasets. The computational strength of artificial intelligence has helped in analysing these large sums of data. In particular, machine learning is a subfield of AI that has been widely utilized in microbiome studies. In this review, we provide an overview of machine learning and how it is utilized in microbiome research. We discuss ideas, new insights, open challenges, and future perspectives of machine learning in microbiome research. We suggest that collaborative efforts between microbiologists, bioinformaticians, and data scientists will be crucial to leveraging machine learning effectively for microbiome research. Despite its current drawbacks, machine learning shows tremendous potential for advancements in fields such as medicine, agriculture, and ecology.

Dendritic signal integration does not only happen linearly across the dendrite, but also supralinearly. Locally, active dendritic spikes can be generated as a result of clustered synaptic activity. Thus, a single neuron functions as multiple computational subunits, that allow for parallel processing and substantially boost the brain’s computational power. In addition, co-active synapses are clustered together on the dendrite, both during development and in mature neurons. This structure likely already arises in the mouse primary visual cortex during the spontaneous network events that occur during development, before its eyes open. In the two weeks between birth and eye-opening, many new synaptic connections are being formed, based on chemical cues and activity patterns. However, the timepoint and manner in which the clustered synaptic structure arises, remains to be elucidated. Calcium imaging was performed on L2/3 pyramidal neuron dendrites of the primary visual cortex in the second postnatal week (postnatal day 5 + 3-7 days-in-vitro). Here, for the first time, spontaneously induced synaptic inputs were analysed in organotypic slice cultures of the mouse primary visual cortex at the subcellular scale. Synapse density and the event frequency increase rapidly before eye opening. In addition, the organization of synapses along the dendrite is likely already spatially and functionally structured during the second postnatal week. This model allows for extensive further assays to unravel the underlying physiological mechanisms.

Genetic perturbation in different genetic backgrounds can cause a range of phenotypes within a species. These phenotypic differences can be the result of the interaction between the genetic background and the perturbation. Previously, we reported that perturbation of gld-1, an important player in the developmental control of Caenorhabditis elegans, released cryptic genetic variation (CGV) affecting fitness in different genetic backgrounds. Here, we investigated the change in transcriptional architecture. We found 414 genes with a cis-expression quantitative trait locus (eQTL) and 991 genes with a trans-eQTL that were specifically found in the gld-1 RNAi treatment. In total, we detected 16 eQTL hotspots, of which 7 were only found in the gld-1 RNAi treatment. Enrichment analysis of those 7 hotspots showed that the regulated genes were associated with neurons and the pharynx. Furthermore, we found evidence of accelerated transcriptional aging in the gld-1 RNAi–treated nematodes. Overall, our results illustrate that studying CGV leads to the discovery of hidden polymorphic regulators.

Information processing is an essential part of biology, enabling coordination of intra-organismal processes such as development, environmental adaptation and inter-organismal communication. Whilst in animals with specialised brain tissue a substantial amount of information processing occurs in a centralised manner, most biological computing is distributed across multiple entities, such as cells in a tissue, roots in a root system or ants in a colony. Physical context, called embodiment, also affects the nature of biological computing. While plants and ant colonies both perform distributed computing, in plants the units occupy fixed positions while individual ants move around. This distinction, solid versus liquid brain computing, shapes the nature of computations. Here we compare information processing in plants and ant colonies, highlighting how similarities and differences originate in, as well as make use of, the differences in embodiment. We end with a discussion on how this embodiment perspective may inform the debate on plant cognition.

The change in allele frequencies within a population over time represents a fundamental process of evolution. By monitoring allele frequencies, we can analyze the effects of natural selection and genetic drift on populations. To efficiently track time-resolved genetic change, large experimental or wild populations can be sequenced as pools of individuals sampled over time using high-throughput genome sequencing (called the Evolve & Resequence approach, E&R). Here, we present a set of experiments using hundreds of natural genotypes of the model plant Arabidopsis thaliana to showcase the power of this approach to study rapid evolution at large scale. First, we validate that sequencing DNA directly extracted from pools of flowers from multiple plants -- organs that are relatively consistent in size and easy to sample -- produces comparable results to other, more expensive state-of-the-art approaches such as sampling and sequencing of individual leaves. Sequencing pools of flowers from 25-50 individuals at ∼40X coverage recovers genome-wide frequencies in diverse populations with accuracy r > 0.95. Secondly, to enable analyses of evolutionary adaptation using E&R approaches of plants in highly replicated environments, we provide open source tools that streamline sequencing data curation and calculate various population genetic statistics two orders of magnitude faster than current software. To directly demonstrate the usefulness of our method, we conducted a two-year outdoor evolution experiment with A. thaliana to show signals of rapid evolution in multiple genomic regions. We demonstrate how these laboratory and computational Pool-seq-based methods can be scaled to study hundreds of populations across many climates.

Global warming and precipitation extremes (drought or increased precipitation) strongly affect plant primary production and thereby terrestrial ecosystem functioning. Recent syntheses show that combined effects of warming and precipitation extremes on plant biomass are generally additive, while individual experiments often show interactive effects, indicating that combined effects are more negative or positive than expected based on the effects of single factors. Here, we examined whether variation in biomass responses to single and combined effects of warming and precipitation extremes can be explained by plant growth form and community type. We performed a meta-analysis of 37 studies, which experimentally crossed warming and precipitation treatments, to test whether biomass responses to combined effects of warming and precipitation extremes depended on plant woodiness and community type (monocultures versus mixtures). Our results confirmed that the effects of warming and precipitation extremes were overall additive. However, combined effects of warming and drought on above- and belowground biomass were less negative in woody- than in herbaceous plant systems and more negative in plant mixtures than in monocultures. We further show that drought effects on plant biomass were more negative in greenhouse- than in field studies, suggesting that greenhouse experiments may overstate drought effects in the field. Our results highlight the importance of plant system characteristics to better understand plant responses to climate change.

Global warming and precipitation extremes (drought or increased precipitation) strongly affect plant primary production and thereby terrestrial ecosystem functioning. Recent syntheses show that combined effects of warming and precipitation extremes on plant biomass are generally additive, while individual experiments often show interactive effects, indicating that combined effects are more negative or positive than expected based on the effects of single factors. Here, we examined whether variation in biomass responses to single and combined effects of warming and precipitation extremes can be explained by plant growth form and community type. We performed a meta-analysis of 37 studies, which experimentally crossed warming and precipitation treatments, to test whether biomass responses to combined effects of warming and precipitation extremes depended on plant woodiness and community type (monocultures versus mixtures). Our results confirmed that the effects of warming and precipitation extremes were overall additive. However, combined effects of warming and drought on above- and belowground biomass were less negative in woody- than in herbaceous plant systems and more negative in plant mixtures than in monocultures. We further show that drought effects on plant biomass were more negative in greenhouse- than in field studies, suggesting that greenhouse experiments may overstate drought effects in the field. Our results highlight the importance of plant system characteristics to better understand plant responses to climate change.

Anthocyanins are purplish pigments that are often induced by stress conditions in many plants. They protect plants against various stresses due to their antioxidant properties. These antioxidant properties also cause anthocyanins to possibly be beneficial for human health when consuming anthocyanin-rich plants. For engineering crops to produce more anthocyanins in their tissues, the genetics behind the production of anthocyanins needs to be understood. While the conserved biosynthetic pathway has been characterized in many plants, regulation of anthocyanin production differs between species. Lettuce, one of the most consumed leafy vegetables in the world, is a crop that has red varieties with high amounts of anthocyanins. How the exact underlying genetics behind this increase of anthocyanins work is only partly understood. In this review, the current understandings of the genetics behind anthocyanin production and regulation in lettuce and Arabidopsis thaliana are discussed. As most research on plant genetics is done in Arabidopsis thaliana, the knowledge of anthocyanin production and regulation in this model organism are used as a basis to compare research on lettuce with. This knowledge can be used in the future to cultivate healthier lettuce variants with higher concentrations of anthocyanins.