Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.

Seed development is important for agriculture productivity. We demonstrate that brassinosteroid (BR) plays crucial roles in determining the size, mass, and shape of Arabidopsis (Arabidopsis thaliana) seeds. The seeds of the BR-deficient mutant de-etiolated2 (det2) are smaller and less elongated than those of wild-type plants due to a decreased seed cavity, reduced endosperm volume, and integument cell length. The det2 mutant also showed delay in embryo development, with reduction in both the size and number of embryo cells. Pollination of det2 flowers with wild-type pollen yielded seeds of normal size but still shortened shape, indicating that the BR produced by the zygotic embryo and endosperm is sufficient for increasing seed volume but not for seed elongation, which apparently requires BR produced from maternal tissues. BR activates expression of SHORT HYPOCOTYL UNDER BLUE1, MINISEED3, and HAIKU2, which are known positive regulators of seed size, but represses APETALA2 and AUXIN RESPONSE FACTOR2, which are negative regulators of seed size. These genes are bound in vivo by the BR-activated transcription factor BRASSINAZOLE-RESISTANT1 (BZR1), and they are known to influence specific processes of integument, endosperm, and embryo development. Our results demonstrate that BR regulates seed size and seed shape by transcriptionally modulating specific seed developmental pathways.

BackgroundThe fungus Marssonina brunnea is a causal pathogen of Marssonina leaf spot that devastates poplar plantations by defoliating susceptible trees before normal fall leaf drop.ResultsWe sequence the genome of M. brunnea with a size of 52 Mb assembled into 89 scaffolds, representing the first sequenced Dermateaceae genome. By inoculating this fungus onto a poplar hybrid clone, we investigate how M. brunnea interacts and co-evolves with its host to colonize poplar leaves. While a handful of virulence genes in M. brunnea, mostly from the LysM family, are detected to up-regulate during infection, the poplar down-regulates its resistance genes, such as nucleotide binding site domains and leucine rich repeats, in response to infection. From 10,027 predicted proteins of M. brunnea in a comparison with those from poplar, we identify four poplar transferases that stimulate the host to resist M. brunnea. These transferas-encoding genes may have driven the co-evolution of M. brunnea and Populus during the process of infection and anti-infection.ConclusionsOur results from the draft sequence of the M. brunnea genome provide evidence for genome-genome interactions that play an important role in poplar-pathogen co-evolution. This knowledge could help to design effective strategies for controlling Marssonina leaf spot in poplar.

Background Liriodendron chinense (Lchi) is a tree species within the Magnoliaceae family and is considered a basal angiosperm. The too low or high temperature or soil drought will restrict its growth as the adverse environmental conditions, thus improving L. chinense abiotic tolerance was the key issues to study. WRKYs are a major family of plant transcription factors known to often be involved in biotic and abiotic stress responses. So far, it is still largely unknown if and how the LchiWRKY gene family is tied to regulating L. chinense stress responses. Therefore, studying the involvement of the WRKY gene family in abiotic stress regulation in L. chinense could be very informative in showing how this tree deals with such stressful conditions. Results In this research, we performed a genome-wide analysis of the Liriodendron chinense (Lchi) WRKY gene family, studying their classification relationships, gene structure, chromosomal locations, gene duplication, cis-element, and response to abiotic stress. The 44 members of the LchiWRKY gene family contain a significant amount of sequence diversity, with their lengths ranging from 525 bp to 40,981 bp. Using classification analysis, we divided the 44 LchiWRKY genes into three phylogenetic groups (I, II, II), with group II then being further divided into five subgroups (IIa, IIb, IIc, IId, IIe). Comparative phylogenetic analysis including the WRKY families from 17 plant species suggested that LchiWRKYs are closely related to the Magnolia Cinnamomum kanehirae WRKY family, and has fewer family members than higher plants. We found the LchiWRKYs to be evenly distributed across 15 chromosomes, with their duplication events suggesting that tandem duplication may have played a major role in LchiWRKY gene expansion model. A Ka/Ks analysis indicated that they mainly underwent purifying selection and distributed in the group IId. Motif analysis showed that LchiWRKYs contained 20 motifs, and different phylogenetic groups contained conserved motif. Gene ontology (GO) analysis showed that LchiWRKYs were mainly enriched in two categories, i.e., biological process and molecular function. Two group IIc members (LchiWRKY10 and LchiWRKY37) contain unique WRKY element sequence variants (WRKYGKK and WRKYGKS). Gene structure analysis showed that most LchiWRKYs possess 3 exons and two different types of introns: the R- and V-type which are both contained within the WRKY domain (WD). Additional promoter cis-element analysis indicated that 12 cis-elements that play different functions in environmental adaptability occur across all LchiWRKY groups. Heat, cold, and drought stress mainly induced the expression of group II and I LchiWRKYs, some of which had undergone gene duplication during evolution, and more than half of which had three exons. LchiWRKY33 mainly responded to cold stress and LchiWRKY25 mainly responded to heat stress, and LchiWRKY18 mainly responded to drought stress, which was almost 4-fold highly expressed, while 5 LchiWRKYs (LchiWRKY5, LchiWRKY23, LchiWRKY14, LchiWRKY27, and LchiWRKY36) responded equally three stresses with more than 6-fold expression. Subcellular localization analysis showed that all LchiWRKYs were localized in the nucleus, and subcellular localization experiments of LchiWRKY18 and 36 also showed that these two transcription factors were expressed in the nucleus. Conclusions This study shows that in Liriodendron chinense, several WRKY genes like LchiWRKY33, LchiWRKY25, and LchiWRKY18, respond to cold or heat or drought stress, suggesting that they may indeed play a role in regulating the tree’s response to such conditions. This information will prove a pivotal role in directing further studies on the function of the LchiWRKY gene family in abiotic stress response and provides a theoretical basis for popularizing afforestation in different regions of China.

Over the past 3 months, coronavirus disease 2019 (COVID-19) has emerged across China and developed into a worldwide outbreak [1]. The disease has caused varying degrees of illness. The proportion of patients with COVID-19 with non-severe illness was 84.3% on admission, and severe cases accounted for 15.7% [2]. Most of the non-severe pneumonia patients would gradually alleviate and be cured with treatment, while others would rapidly progress to severe illness, which has a poor prognosis [3, 4]. As recently reported, the cumulative risk of the composite end-point was 3.6% in all COVID-19 patients, and the cumulative risk was 20.6% for severe illness [2]. However, it is still unknown whether early identification and intervention for non-severe patients with COVID-19 could prevent progression into severe disease. According to the experience of treating other diseases, there might be a large promoting effect of treatment. In this paper, we aim to build a predictive model for identifying high-risk non-severe pneumonia patients at an early stage. 86 patients with COVID-19 and non-severe pneumonia on admission were recruited as the training cohort at Renmin Hospital of Wuhan University from 2 to 20 January, 2020, and another 62 patients were prospectively enrolled as the validation cohort from 28 January to 9 February, 2020. COVID-19 was confirmed by real-time PCR. Disease severities of COVID-19 were defined as severe and non-severe pneumonia based on the criteria of American Thoracic Society guidelines for community-acquired pneumonia [2, 5]. The exclusion criteria included: 1) degrees of severity were not available on admission or during follow-up; 2) diagnosed with severe illness at the time of admission; 3) confirmed with COVID-19 and treated at other hospitals; 4) medication was administered within 15 days before admission; 5) received oxygen support during follow-up. Patients were divided into "progressed" or "non-progressed" groups, based on whether they progressed to severe illness or not during the 14-day follow-up period. Comorbidity included diabetes, hypertension, cardiovascular and cerebrovascular diseases, COPD, malignant tumour, chronic liver disease, chronic kidney disease, tuberculosis and immunodeficiency diseases, etc. Clinical characteristics and laboratory findings were extracted from electronic medical records. Radiological features were extracted from chest computed tomography (CT) imaging using a double-blind method [6]. To evaluate the lesion size accurately, a diagnosis system for COVID-19 based on artificial intelligence (AI) was employed to measure volume ratio of pneumonia automatically by analysing CT values [7, 8]. Logistic regression was used as the classifier to build the predictive model. The discriminative performance of the predictive model was quantified by the value of the area under the receiver operating characteristic curve (AUC) in the cross-validation of the training and validation datasets. Risk index calculated with the weight of each variable in the model was used to identify...