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Linking Plant Nutritional Status to Plant-Microbe Interactions
Plants have developed a wide-range of adaptations to overcome nutrient limitation, including changes to the quantity and composition of carbon-containing compounds released by roots. Root-associated bacteria are largely influenced by these compounds which can be perceived as signals or substrates. Here, we evaluate the effect of root exudates collected from maize plants grown under nitrogen (N), phosphate (P), iron (Fe) and potassium (K) deficiencies on the transcriptome of the plant growth promoting rhizobacterium (PGPR) Bacillus amyloliquefaciens FZB42. The largest shifts in gene expression patterns were observed in cells exposed to exudates from N-, followed by P-deficient plants. Exudates from N-deprived maize triggered a general stress response in FZB42 in the exponential growth phase, which was evidenced by the suppression of numerous genes involved in protein synthesis. Exudates from P-deficient plants induced bacterial genes involved in chemotaxis and motility whilst exudates released by Fe and K deficient plants did not cause dramatic changes in the bacterial transcriptome during exponential growth phase. Global transcriptional changes in bacteria elicited by nutrient deficient maize exudates were significantly correlated with concentrations of the amino acids aspartate, valine and glutamate in root exudates suggesting that transcriptional profiling of FZB42 associated with metabolomics of N, P, Fe and K-deficient maize root exudates is a powerful approach to better understand plant-microbe interactions under conditions of nutritional stress.
Journal Article
Are root exudates more important than other sources of rhizodeposits in structuring rhizosphere bacterial communities
This review evaluates the importance of root exudates in determining rhizosphere bacterial community structure. We present evidence that indicates that: (1) the direct influence of root exudates on rhizosphere bacterial communities is limited to small spatiotemporal windows related to root apices; (2) upon rapid assimilation by microorganisms, root exudates are modified, independent of plant influences, before rerelease into the rhizosphere by the microorganisms themselves - thus, at short distances from root apices, rhizosphere carbon pools are unlikely to be dominated by root exudates; and (3) many of the major compounds found in root exudates are ubiquitous in the rhizosphere as they are found in other pools of rhizodeposits and in microbial exudates. Following this argument, we suggest that the importance of root exudates in structuring rhizosphere bacterial communities needs to be considered in the context of the wider contribution of other rhizosphere carbon pools. Finally, we discuss the implications of rhizosphere bacterial distribution trends for the development of effective strategies to manage beneficial plant-microorganism interactions.
Journal Article
Evolutionary conservation of a core root microbiome across plant phyla along a tropical soil chronosequence
Culture-independent molecular surveys of plant root microbiomes indicate that soil type generally has a stronger influence on microbial communities than host phylogeny. However, these studies have mostly focussed on model plants and crops. Here, we examine the root microbiomes of multiple plant phyla including lycopods, ferns, gymnosperms, and angiosperms across a soil chronosequence using 16S rRNA gene amplicon profiling. We confirm that soil type is the primary determinant of root-associated bacterial community composition, but also observe a significant correlation with plant phylogeny. A total of 47 bacterial genera are associated with roots relative to bulk soil microbial communities, including well-recognized plant-associated genera such as
Bradyrhizobium, Rhizobium
, and
Burkholderia
, and major uncharacterized lineages such as WPS-2, Ellin329, and FW68. We suggest that these taxa collectively constitute an evolutionarily conserved core root microbiome at this site. This lends support to the inference that a core root microbiome has evolved with terrestrial plants over their 400 million year history.
Yeoh et al. study root microbiomes of different plant phyla across a tropical soil chronosequence. They confirm that soil type is the primary determinant of root-associated bacterial communities, but also observe a clear correlation with plant phylogeny and define a core root microbiome at this site.
Journal Article
Therapeutics and pharmacology for medical students
Systems-based to enable easy reference for the busy medical student on their clinical attachment, this guide deals with the aspects of pharmacology, with outlines of appropriate usage and recommended dosage.
Book
Mobile detection of autism through machine learning on home video: A development and prospective validation study
The standard approaches to diagnosing autism spectrum disorder (ASD) evaluate between 20 and 100 behaviors and take several hours to complete. This has in part contributed to long wait times for a diagnosis and subsequent delays in access to therapy. We hypothesize that the use of machine learning analysis on home video can speed the diagnosis without compromising accuracy. We have analyzed item-level records from 2 standard diagnostic instruments to construct machine learning classifiers optimized for sparsity, interpretability, and accuracy. In the present study, we prospectively test whether the features from these optimized models can be extracted by blinded nonexpert raters from 3-minute home videos of children with and without ASD to arrive at a rapid and accurate machine learning autism classification.
We created a mobile web portal for video raters to assess 30 behavioral features (e.g., eye contact, social smile) that are used by 8 independent machine learning models for identifying ASD, each with >94% accuracy in cross-validation testing and subsequent independent validation from previous work. We then collected 116 short home videos of children with autism (mean age = 4 years 10 months, SD = 2 years 3 months) and 46 videos of typically developing children (mean age = 2 years 11 months, SD = 1 year 2 months). Three raters blind to the diagnosis independently measured each of the 30 features from the 8 models, with a median time to completion of 4 minutes. Although several models (consisting of alternating decision trees, support vector machine [SVM], logistic regression (LR), radial kernel, and linear SVM) performed well, a sparse 5-feature LR classifier (LR5) yielded the highest accuracy (area under the curve [AUC]: 92% [95% CI 88%-97%]) across all ages tested. We used a prospectively collected independent validation set of 66 videos (33 ASD and 33 non-ASD) and 3 independent rater measurements to validate the outcome, achieving lower but comparable accuracy (AUC: 89% [95% CI 81%-95%]). Finally, we applied LR to the 162-video-feature matrix to construct an 8-feature model, which achieved 0.93 AUC (95% CI 0.90-0.97) on the held-out test set and 0.86 on the validation set of 66 videos. Validation on children with an existing diagnosis limited the ability to generalize the performance to undiagnosed populations.
These results support the hypothesis that feature tagging of home videos for machine learning classification of autism can yield accurate outcomes in short time frames, using mobile devices. Further work will be needed to confirm that this approach can accelerate autism diagnosis at scale.
Journal Article
Jean Grey
When the teenage Marvel Girl traveled through time and arrived in the present, she learned the terrible fate that befell her adult counterpart. Possessed by a cosmic entity called the Phoenix, that Jean Grey was trapped in an endless cycle of life and death. Now, determined to escape that future, young Jean sets out to write her own destiny. But when she has a premonition that the Phoenix is coming for her, she'll have to fight tooth and nail not to become its next victim! Jean enlists the help of some of its previous hosts - but will the ultimate brain trust of Rachel Grey, Quentin Quire and Hope Summers have the answers she's searching for?
BOOK
Deterministic processes guide long-term synchronised population dynamics in replicate anaerobic digesters
A replicate long-term experiment was conducted using anaerobic digestion (AD) as a model process to determine the relative role of niche and neutral theory on microbial community assembly, and to link community dynamics to system performance. AD is performed by a complex network of microorganisms and process stability relies entirely on the synergistic interactions between populations belonging to different functional guilds. In this study, three independent replicate anaerobic digesters were seeded with the same diverse inoculum, supplied with a model substrate, α-cellulose, and operated for 362 days at a 10-day hydraulic residence time under mesophilic conditions. Selective pressure imposed by the operational conditions and model substrate caused large reproducible changes in community composition including an overall decrease in richness in the first month of operation, followed by synchronised population dynamics that correlated with changes in reactor performance. This included the synchronised emergence and decline of distinct
Ruminococcus
phylotypes at day 148, and emergence of a
Clostridium
and
Methanosaeta
phylotype at day 178, when performance became stable in all reactors. These data suggest that many dynamic functional niches are predictably filled by phylogenetically coherent populations over long time scales. Neutral theory would predict that a complex community with a high degree of recognised functional redundancy would lead to stochastic changes in populations and community divergence over time. We conclude that deterministic processes may play a larger role in microbial community dynamics than currently appreciated, and under controlled conditions it may be possible to reliably predict community structural and functional changes over time.
Journal Article