Abstract The model organism Arabidopsis thaliana is readily used in basic research due to resource availability and relative speed of data acquisition. A major goal is to transfer acquired knowledge from Arabidopsis to crop species. However, the identification of functional equivalents of well-characterized Arabidopsis genes in other plants is a nontrivial task. It is well documented that transcriptionally coordinated genes tend to be functionally related and that such relationships may be conserved across different species and even kingdoms. To exploit such relationships, we constructed whole-genome coexpression networks for Arabidopsis and six important plant crop species. The interactive networks, clustered using the HCCA algorithm, are provided under the banner PlaNet (http://aranet.mpimp-golm.mpg.de). We implemented a comparative network algorithm that estimates similarities between network structures. Thus, the platform can be used to swiftly infer similar coexpressed network vicinities within and across species and can predict the identity of functional homologs. We exemplify this using the PSA-D and chalcone synthase-related gene networks. Finally, we assessed how ontology terms are transcriptionally connected in the seven species and provide the corresponding MapMan term coexpression networks. The data support the contention that this platform will considerably improve transfer of knowledge generated in Arabidopsis to valuable crop species.
Abstract Introduction HIV rebounds after cessation of antiretroviral therapy, representing a barrier to cure. To better understand the virus reservoir, analysis pipelines have been developed that categorize proviral sequences as intact or defective, and further determine the precise nature of the sequence defects that may be present. We investigated the effects that different analysis pipelines had on the characterization of HIV‐1 proviral sequences. Methods We used single genome amplification to generate near full‐length (NFL) HIV‐1 proviral DNA sequences, defined as amplicons greater than 8000 base pairs in length, isolated from peripheral blood mononuclear cells (PBMC) of treated suppressed participants with HIV‐1. Amplicons underwent direct next‐generation single genome sequencing and were analysed using four HIV‐1 proviral characterization pipelines. Sequences were characterized as intact or defective; defective sequences were assessed for the number and types of defects present. To confirm and extend our findings, 691 proviruses from the Proviral Sequence Database (PSD) were analysed and the ProSeq‐IT tool of the PSD was used to characterize both the participant and PSD proviruses. Results and discussion Virus sequences derived from thirteen ART‐treated virologically suppressed participants with HIV were studied. A total of 693 HIV‐1 proviral sequences were generated, 282 of which were NFL. An average of 53 sequences per participant was analysed. We found that proviruses often harbour multiple sequence defect types (mean 2.7, 95% confidence interval [CI] 2.5, 3.0); the elimination order used by each pipeline affected the percentage of proviruses allotted into each defect category. These differences varied between participants, depending on the number of defect categories present in a given provirus sequence. Pipeline‐specific differences in characterizing the HIV‐1 5′ untranslated region (5′ UTR) led to an overestimation of the number of intact NFL proviral sequences, a finding corroborated in the independent PSD analysis. A comparison of the four published pipelines to ProSeq‐IT found that ProSeq IT was more likely to characterize proviruses as intact. Conclusions The choice of pipeline used for HIV‐1 provirus landscape analysis may bias the classification of defective sequences. To improve the comparison of provirus characterizations across research groups, the development of a consensus elimination pipeline should be prioritized.
Under natural conditions, it is common for plants to experience water deprivation (drought) for periods of days or longer. Plants respond to drought stress by reconfiguring their transcriptome activity. Transcriptome changes in response to drought are dynamic, and are shaped by mitigating factors like time during the diurnal cycle. To date, analyses of drought-induced transcriptome remodelling have concentrated on dynamic changes induced by rapid desiccation, or changes at a single time point following gradual water stress. To gain insights into the dynamics of transcriptome reconfiguration in response to gradual drying of the soil, the drought-induced transcriptomes of Arabidopsis thaliana were examined at four time points over a single diel period – midday, late day, midnight, and pre-dawn. Transcriptome reconfigurations were induced by drought in advance of changes to relative water content, leaf water loss, and chlorophyll content. Comparative analyses support the hypothesis that the drought-responsive transcriptomes were shaped by invocation of distinct hormonal and stress response pathways at different times of the day. While a core set of genes were drought responsive at multiple time points throughout the day, the magnitude of the response varied in a manner dependent on the time of day. Moreover, analysis of a single time point would fail to identify suites of drought-responsive genes that can only be detected through assessment of the dynamics of diurnal changes, emphasising the value of characterising multiple time-of-day-specific drought transcriptomes.
It has been more than 50 years since Arabidopsis (Arabidopsis thaliana) was first introduced as a model organism to understand basic processes in plant biology. A well-organized scientific community has used this small reference plant species to make numerous fundamental plant biology discoveries (Provart et al., 2016). Due to an extremely well-annotated genome and advances in high-throughput sequencing, our understanding of this organism and other plant species has become even more intricate and complex. Computational resources, including CyVerse,3 Araport,4 The Arabidopsis Information Resource (TAIR),5 and BAR,6 have further facilitated novel findings with just the click of a mouse. As we move toward understanding biological systems, Arabidopsis researchers will need to use more quantitative and computational approaches to extract novel biological findings from these data. Here, we discuss guidelines, skill sets, and core competencies that should be considered when developing curricula or training undergraduate or graduate students, postdoctoral researchers, and faculty. A selected case study provides more specificity as to the concrete issues plant biologists face and how best to address such challenges.
Abstract Drought stress negatively impacts the health of long-lived trees. Understanding the genetic mechanisms that underpin response to drought stress is requisite for selecting or enhancing climate change resilience. We aimed to determine how hybrid poplars respond to prolonged and uniform exposure to drought; how responses to moderate and more severe growth-limiting drought stresses differed; and, how drought responses change throughout the day. We established hybrid poplar trees ( Populus x ‘Okanese’) from unrooted stem cutting with abundant soil moisture for six weeks. We then withheld water to establish well-watered, moderate, and severe growth-limiting drought conditions. These conditions were maintained for three weeks during which growth was monitored. We then measured photosynthetic rates and transcriptomes of leaves that had developed during the drought treatments at two times of day. The moderate and severe drought treatments elicited distinct changes in growth and development, photosynthetic rates, and global transcriptome profiles. Notably, the time of day of sampling produced the strongest signal in the transcriptome data. The moderate drought treatment elicited global transcriptome changes that were intermediate to the severe and well-watered treatments in the early evening, but did not elicit a strong drought response in the morning, emphasizing the complex nature of drought regulation in long-lived trees. Highlight Poplar drought transcriptome is defined by the time of day of sampling and by the extent of water deficit.
Plants rely on transcriptional dynamics to respond to multiple climatic fluctuations and contexts in nature. We analyzed the genome-wide gene expression patterns of rice (Oryza sativa) growing in rainfed and irrigated fields during two distinct tropical seasons and determined simple linear models that relate transcriptomic variation to climatic fluctuations. These models combine multiple environmental parameters to account for patterns of expression in the field of co-expressed gene clusters. We examined the similarities of our environmental models between tropical and temperate field conditions, using previously published data. We found that field type and macroclimate had broad impacts on transcriptional responses to environmental fluctuations, especially for genes involved in photosynthesis and development. Nevertheless, variation in solar radiation and temperature at the timescale of hours had reproducible effects across environmental contexts. These results provide a basis for broad-based predictive modeling of plant gene expression in the field.
IntroductionThe Bone and Joint Monitor Project was developed to quantify the global burden of musculoskeletal conditions and develop strategies for their prevention.Experts within the Monitor Project have worked previously with officers at the World Health Organization (WHO) to estimate morbidity and mortality associated with rheumatic conditions.The present collaboration seeks means of providing additional and more current burden data.Objective To develop recommendations for performing epidemiological studies in sample populations with musculoskeletal conditions and problems, accounting for determinants and consequences to the individual and society.Methods Recommendations have been developed identifying the most relevant domains for measuring and monitoring the various musculoskeletal conditions by review of epidemiological data on occurrence, determinants and outcomes, and by expert opinion.Instruments that measure these domains were reviewed. ResultsThe domains recommended follow the principles of the WHO International Classification of Functioning, Disability and Health [1,2], and consider: health condition; body function and structure; activity limitation; participation restriction; personal and environmental contextual factors; and, in addition, the resource utilisation and social consequences.The musculoskeletal conditions and problems considered were osteoarthitis, inflammatory arthritis, osteoporosis, spinal problems, musculoskeletal trauma and injuries, and musculoskeletal pain with restricted activity.The selection of indicators for each domain considered the feasibility of their use in a health interview survey (HIS), a health examination survey (HES), a register or a clinical study.Consensus on case definition was reached depending on the study methodology.For example, osteoporosis defined by bone densitometry cannot be ascertained in an HIS, whereas the outcome of osteoporosis (i.e.fragility fracture) can be.Osteoarthitis can be identified as joint pain in an HIS but the preferred definition is pain with X-ray changes and can only be ascertained in an HES.Previously validated generic and disease-specific instruments have been identified that include indicators for all or most of the recommended domains for the consequences of the different conditions and problems.The indicators of the domains for resource utilisation and social consequences and feasibility for col-lection will vary in different socioeconomic and geographic areas.Guidance on sampling methods is also being developed.Conclusions The comparability of data collected across the globe will improve by the application of agreed upon indicators that consider key domains for the different musculoskeletal conditions and problems in epidemiological studies conducted in different populations.
ABSTRACT Changes in T-cell function are a hallmark of human immunodeficiency virus type 1 (HIV-1) infection, but the pathogenic mechanisms leading to these changes are unclear. We examined the gene expression profiles in ex vivo human CD4 + and CD8 + T cells from untreated HIV-1-infected individuals at different clinical stages and rates of disease progression. Profiles of pure CD4 + and CD8 + T-cell subsets from HIV-1-infected nonprogressors with controlled viremia were indistinguishable from those of individuals not infected with HIV-1. Similarly, no gene clusters could distinguish T cells from individuals with early infection from those seen in chronic progressive HIV-1 infection, whereas differences were observed between uninfected individuals or nonprogressors versus early or chronic progressors. In early and chronic HIV-1 infection, three characteristic gene expression signatures were observed. (i) CD4 + and CD8 + T cells showed increased expression of interferon-stimulated genes (ISGs). However, some ISGs, including CXCL9, CXCL10, and CXCL11, and the interleukin-15 alpha receptor were not upregulated. (ii) CD4 + and CD8 + T cells showed a cluster similar to that observed in thymocytes. (iii) More genes were differentially regulated in CD8 + T cells than in CD4 + T cells, including a cluster of genes downregulated exclusively in CD8 + T cells. In conclusion, HIV-1 infection induces a persistent T-cell transcriptional profile, early in infection, characterized by a dramatic but potentially aberrant interferon response and a profile suggesting an active thymic output. These findings highlight the complexity of the host-virus relationship in HIV-1 infection.
