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Glycine max symbiotic ammonium transporter 1 was first documented as a putative ammonium (NH 4 + ) channel localized to the symbiosome membrane of soybean root nodules. We show that Glycine max symbiotic ammonium transporter 1 is actually a membrane-localized basic helix-loop-helix (bHLH) DNA-binding transcription factor now renamed Glycine max bHLH membrane 1 (GmbHLHm1). In yeast, GmbHLHm1 enters the nucleus and transcriptionally activates a unique plasma membrane NH 4 + channel Saccharomyces cerevisiae ammonium facilitator 1. Ammonium facilitator 1 homologs are present in soybean and other plant species, where they often share chromosomal microsynteny with bHLHm1 loci. GmbHLHm1 is important to the soybean rhizobium symbiosis because loss of activity results in a reduction of nodule fitness and growth. Transcriptional changes in nodules highlight downstream signaling pathways involving circadian clock regulation, nutrient transport, hormone signaling, and cell wall modification. Collectively, these results show that GmbHLHm1 influences nodule development and activity and is linked to a novel mechanism for NH 4 + transport common to both yeast and plants.nitrogen fixation | legume | nitrogen transport

Our knowledge of the evolution and the role of untranslated region (UTR) in SARS-CoV-2 pathogenicity is very limited. Leader sequence, originated from UTR, is found at the 5′ ends of all encoded SARS-CoV-2 transcripts, highlighting its importance. Here, evolution of leader sequence was compared between human pathogenic and non-pathogenic coronaviruses. Then, profiling of microRNAs that can inactivate the key UTR regions of coronaviruses was carried out. A distinguished pattern of evolution in leader sequence of SARS-CoV-2 was found. Mining all available microRNA families against leader sequences of coronaviruses resulted in discovery of 39 microRNAs with a stable thermodynamic binding energy. Notably, SARS-CoV-2 had a lower binding stability against microRNAs. hsa-MIR-5004-3p was the only human microRNA able to target the leader sequence of SARS and to a lesser extent, also SARS-CoV-2. However, its binding stability decreased remarkably in SARS-COV-2. We found some plant microRNAs with low and stable binding energy against SARS-COV-2. Meta-analysis documented a significant (p < 0.01) decline in the expression of MIR-5004-3p after SARS-COV-2 infection in trachea, lung biopsy, and bronchial organoids as well as lung-derived Calu-3 and A549 cells. The paucity of the innate human inhibitory microRNAs to bind to leader sequence of SARS-CoV-2 can contribute to its high replication in infected human cells.

BackgroundRecent (2013 and 2009) zoonotic transmission of avian or porcine influenza to humans highlights an increase in host range by evading species barriers. Gene reassortment or antigenic shift between viruses from two or more hosts can generate a new life-threatening virus when the new shuffled virus is no longer recognized by antibodies existing within human populations. There is no large scale study to help understand the underlying mechanisms of host transmission. Furthermore, there is no clear understanding of how different segments of the influenza genome contribute in the final determination of host range.MethodsTo obtain insight into the rules underpinning host range determination, various supervised machine learning algorithms were employed to mine reassortment changes in different viral segments in a range of hosts. Our multi-host dataset contained whole segments of 674 influenza strains organized into three host categories: avian, human, and swine. Some of the sequences were assigned to multiple hosts. In point of fact, the datasets are a form of multi-labeled dataset and we utilized a multi-label learning method to identify discriminative sequence sites. Then algorithms such as CBA, Ripper, and decision tree were applied to extract informative and descriptive association rules for each viral protein segment.ResultWe found informative rules in all segments that are common within the same host class but varied between different hosts. For example, for infection of an avian host, HA14V and NS1230S were the most important discriminative and combinatorial positions.ConclusionHost range identification is facilitated by high support combined rules in this study. Our major goal was to detect discriminative genomic positions that were able to identify multi host viruses, because such viruses are likely to cause pandemic or disastrous epidemics.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3250-9) contains supplementary material, which is available to authorized users.

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Plant root symbiosis with Arbuscular mycorrhizal (AM) fungi improves uptake of water and mineral nutrients, improving plant development under stressful conditions. Unraveling the unified transcriptomic signature of a successful colonization provides a better understanding of symbiosis. We developed a framework for finding the transcriptomic signature of Arbuscular mycorrhiza colonization and its regulating transcription factors in roots of Medicago truncatula. Expression profiles of roots in response to AM species were collected from four separate studies and were combined by direct merging meta-analysis. Batch effect, the major concern in expression meta-analysis, was reduced by three normalization steps: Robust Multi-array Average algorithm, Z-standardization, and quartiling normalization. Then, expression profile of 33685 genes in 18 root samples of Medicago as numerical features, as well as study ID and Arbuscular mycorrhiza type as categorical features, were mined by seven models: RELIEF, UNCERTAINTY, GINI INDEX, Chi Squared, RULE, INFO GAIN, and INFO GAIN RATIO. In total, 73 genes selected by machine learning models were up-regulated in response to AM (Z-value difference > 0.5). Feature weighting models also documented that this signature is independent from study (batch) effect. The AM inoculation signature obtained was able to differentiate efficiently between AM inoculated and non-inoculated samples. The AP2 domain class transcription factor, GRAS family transcription factors, and cyclin-dependent kinase were among the highly expressed meta-genes identified in the signature. We found high correspondence between the AM colonization signature obtained in this study and independent RNA-seq experiments on AM colonization, validating the repeatability of the colonization signature. Promoter analysis of upregulated genes in the transcriptomic signature led to the key regulators of AM colonization, including the essential transcription factors for endosymbiosis establishment and development such as NF-YA factors. The approach developed in this study offers three distinct novel features: (I) it improves direct merging meta-analysis by integrating supervised machine learning models and normalization steps to reduce study-specific batch effects; (II) seven attribute weighting models assessed the suitability of each gene for the transcriptomic signature which contributes to robustness of the signature (III) the approach is justifiable, easy to apply, and useful in practice. Our integrative framework of meta-analysis, promoter analysis, and machine learning provides a foundation to reveal the transcriptomic signature and regulatory circuits governing Arbuscular mycorrhizal symbiosis and is transferable to the other biological settings.

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Background In northern Australian koala populations (Queensland and New South Wales), periodontal disease (gingivitis and periodontitis) is common while koala retrovirus subtype A is endogenous, with other subtypes transmitted exogenously. Koala retrovirus has been hypothesised to cause immune suppression and may predispose koalas to diseases caused by concurrent infections. In southern Australia populations (Victoria and South Australia) periodontal disease has not been investigated, and koala retrovirus is presumably exogenously transmitted. This study described oral health in South Australian koalas and investigated if an association between periodontal disease and koala retrovirus exists. Methods Oral health was examined for wild‐caught koalas from the Mount Lofty Ranges (n = 75). Koala retrovirus provirus was detected in whole blood using nested PCR and proviral load determined with qPCR. Periodontal disease severity was recorded and used to calculate the Final Oral Health Index (0‐normal, 24‐severe).Results Periodontal disease was observed in 84% (63/75) of koalas; 77% had gingivitis (58/75) and 65% (49/75) had periodontitis. The average Final Oral Health Index was 5.47 (s.d 3.13). Most cases of periodontal disease were associated with the incisors. Koala retrovirus‐infected koalas were more likely to present with periodontitis (p = 0.042) and the Final Oral Health Index was negatively correlated with proviral load (ρ = −0.353, p = 0.017). Conclusion South Australian koalas had a high prevalence of gingivitis and periodontitis. Periodontal disease was more prevalent in the incisors. Exogenous koala retrovirus infection may also facilitate the development of periodontitis by modulation of the immune response to concurrent oral bacterial infections.