Manijeh Mohammadi-Dehcheshmeh

Soybean SAT1 (Symbiotic Ammonium Transporter 1) encodes a bHLH transcription factor involved in nodule growth and NH4+ transport

Significance The legume/rhizobia symbiosis involves a root-based exchange of bacterial fixed nitrogen for plant-derived photosynthetic carbon. The exchange takes place within the legume root nodule, which is a specialized root tissue that develops in response to plant and bacterial signal exchange. The bacteria reside within plant cells inside the nodule. In this study, we explore the activity of a membrane-bound soybean transcription factor, Glycine max basic–helix-loop–helix membrane 1, which is important for soybean nodule growth and is linked to the activity of a unique class of ammonium channels and to signaling cascades influencing a nodule circadian clock. Glycine max symbiotic ammonium transporter 1 was first documented as a putative ammonium (NH4+) 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 NH4+ 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 NH4+ transport common to both yeast and plants.

Comparative study of ammonium transporters in different organisms by study of a large number of structural protein features via data mining algorithms

Ammonium is an excellent nitrogen source, and ammonium transfer is a fundamental process in most organisms. Membrane transport of ammonium is the key component of nitrogen metabolism mediated by Ammonium Transporter/Methylamine Permease/Rhesus (AMT/MEP/Rh) protein family. Ammonium transporters play different physiological roles in various organisms. Here, we looked at the protein characteristics of ammonium transporters in different organisms to create a link between protein characteristics and the organism. In order to increase the accuracy and precision of the employed models, for the first time, an attempt was made to cover all structural aspects of ammonium transporters in animals, bacteria, fungi, plants, and human by extracting and calculating 874 protein attributes of primary, secondary, and tertiary structures for each ammonium transporter. Then, various weighting and modeling algorithms were applied to determine how structural protein features change between organisms. Considering a large number of protein attributes made it possible to detect key protein characteristics in the structure of ammonium transporters. The results, for the first time, indicated that His-based features including count/frequency of His and frequency/count of Ile-His were the most significant features generating different types of ammonium transporters within organisms. Within different tested models, the C5.0 model was the most efficient and precise model for discrimination of organism type, based on ammonium transporter sequence, with the precision of 94.85%. The determination of protein characteristics of ammonium transporters in different organisms provides a new vista for understanding the evolution of transporters based on the modulation of protein characteristics and facilitates engineering of new transporters. In our point of view, dissecting a large number of structural protein characteristics through data mining algorithms provides a novel functional strategy for studying evolution and phylogeny. This research will serve as a basis for future studies on engineering novel ammonium transporters.

Novel approach for identification of influenza virus host range and zoonotic transmissible sequences by determination of host-related associative positions in viral genome segments

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.

Computational systems biology analysis of biomarkers in lung cancer; unravelling genomic regions which frequently encode biomarkers, enriched pathways, and new candidates

Exponentially growing scientific knowledge in scientific publications has resulted in the emergence of a new interdisciplinary science of literature mining. In text mining, the machine reads the published literature and transfers the discovered knowledge to mathematical-like formulas. In an integrative approach in this study, we used text mining in combination with network discovery, pathway analysis, and enrichment analysis of genomic regions for better understanding of biomarkers in lung cancer. Particular attention was paid to non-coding biomarkers. In total, 60 MicroRNA biomarkers were reported for lung cancer, including some prognostic biomarkers. MIR21, MIR155, MALAT1, and MIR31 were the top non-coding RNA biomarkers of lung cancer. Text mining identified 447 proteins which have been studied as biomarkers in lung cancer. EGFR (receptor), TP53 (transcription factor), KRAS, CDKN2A, ENO2, KRT19, RASSF1, GRP (ligand), SHOX2 (transcription factor), and ERBB2 (receptor) were the most studied proteins. Within small molecules, thymosin-a1, oestrogen, and 8-OHdG have received more attention. We found some chromosomal bands, such as 7q32.2, 18q12.1, 6p12, 11p15.5, and 3p21.3 that are highly involved in deriving lung cancer biomarkers.

Root and shoot parts of strawberry: factories for production of functional human pro-insulin

Abstract Diabetes, a disease caused by excessive blood sugar, is caused by the lack of insulin. For commercial production, insulin is made in bacteria or yeast by protein recombinant technology. The focus of this research is evaluating another resource and producing of recombinant insulin protein in as strawberry as this plant has high potential in production of pharmaceutical proteins. Strawberry is a suitable bioreactor for production of recombinant proteins especially edible vaccines. In this research, human pro-insulin gene was cloned in pCAMBIA1304 vector under CaMV35S promoter and NOS terminator. Agrobacterium tumefaciens LBA4404, AGL1, EHA105, EHA101, C58, C58 (pGV2260) and C58 (pGV3101) strains were used for transformation of pro-insulin gene into strawberry cv. Camarosa, Selva, Sarian Hybrid, Pajaro, Paros, Gaviota, Alpine. Additionally, Agrobacterium rhizogenes K599, R1000, A4 and MSU440 strains were utilized for gene transformation into hairy roots. PCR analysis indicated the presence of transformed human pro-insulin gene in the strawberry and hairy roots. Also, its transcription was confirmed using RT-PCR. Furthermore, the analysis of plants, fruits and hairy roots at the level of proteins using dot blot, ELISA, SDS-PAGE and ECL tests re-confirmed the expression of this protein in the transgenic plants as well as hairy roots. Protein purification of human pro-insulin from transgenic tissues was performed using affinity chromatography. Finally, the bioassay of recombinant pro-insulin was performed. The analysis of second generations of transgenic plants (T1) at DNA and protein levels was also performed as a complementary experiment. This study opens a new avenue in molecular farming of human pro-insulin through its mass production in roots and shoots of strawberry.