scholarly journals Hierarchical Control of Nitrite Respiration by Transcription Factors Encoded within Mobile Gene Clusters of Thermus thermophilus

Genes ◽  
2017 ◽  
Vol 8 (12) ◽  
pp. 361 ◽  
Author(s):  
Laura Alvarez ◽  
Nieves G. Quintáns ◽  
Alba Blesa ◽  
Ignacio Baquedano ◽  
Mario Mencía ◽  
...  
Genes ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 1308
Author(s):  
Mercedes Sánchez-Costa ◽  
Alba Blesa ◽  
José Berenguer

Genes coding for enzymes of the denitrification pathway appear randomly distributed among isolates of the ancestral genus Thermus, but only in few strains of the species Thermus thermophilus has the pathway been studied to a certain detail. Here, we review the enzymes involved in this pathway present in T. thermophilus NAR1, a strain extensively employed as a model for nitrate respiration, in the light of its full sequence recently assembled through a combination of PacBio and Illumina technologies in order to counteract the systematic errors introduced by the former technique. The genome of this strain is divided in four replicons, a chromosome of 2,021,843 bp, two megaplasmids of 370,865 and 77,135 bp and a small plasmid of 9799 pb. Nitrate respiration is encoded in the largest megaplasmid, pTTHNP4, within a region that includes operons for O2 and nitrate sensory systems, a nitrate reductase, nitrate and nitrite transporters and a nitrate specific NADH dehydrogenase, in addition to multiple insertion sequences (IS), suggesting its mobility-prone nature. Despite nitrite is the final product of nitrate respiration in this strain, the megaplasmid encodes two putative nitrite reductases of the cd1 and Cu-containing types, apparently inactivated by IS. No nitric oxide reductase genes have been found within this region, although the NorR sensory gene, needed for its expression, is found near the inactive nitrite respiration system. These data clearly support that partial denitrification in this strain is the consequence of recent deletions and IS insertions in genes involved in nitrite respiration. Based on these data, the capability of this strain to transfer or acquire denitrification clusters by horizontal gene transfer is discussed.


RSC Advances ◽  
2019 ◽  
Vol 9 (61) ◽  
pp. 35797-35802 ◽  
Author(s):  
Verena Hantke ◽  
Chongqing Wang ◽  
Elizabeth J. Skellam ◽  
Russell J. Cox

Transcription factors from the biosynthesis of fungal metabolites were investigated by ectopic expression: pyiR from the pyrichalasin cluster enhanced titres of pyrichalasin H 1; but BC1 from the ACE1 cluster unexpectedly induced hinnulin A 4.


2015 ◽  
Vol 11 (4) ◽  
pp. 1012-1028
Author(s):  
Igor F. Tsigelny ◽  
Valentina L. Kouznetsova ◽  
Pengfei Jiang ◽  
Sandeep C. Pingle ◽  
Santosh Kesari

We report an integrative networks-based analysis to identify a system of coherent gene modules in primary and secondary glioblastoma.


2021 ◽  
Vol 49 (2) ◽  
pp. 12323
Author(s):  
Yuyang HU ◽  
Bo SHU

Crown rot is one of the most destructive diseases of cultivated strawberry. The correlation between Whirly family transcription factors, the one class of known resistance genes, and strawberry crown rot resistance has not been studied. In this study, the Whirlys of Fragaria × ananassa, F. iinumae, F. vesca, F. viridis and F. nilgerrensis were characterized by searching the strawberry genome database and analyzing the presence of Whirly domains. Five FaWHYs, two FiWHYs, three FnWHYs, two FviWHYs and four FvWHYs were identified from their respective genome. Two gene clusters with segmental duplications were obtained from the gene cluster analysis with two and three FaWHYs, and three FaWHYs showed syntenic relationships with AtWHYs of Arabidopsis thaliana. FiWHY1, FvWHY2 and FviWHY1 showed syntenic relationships with FaWHY1 and FaWHY2. At the same time, FiWHY2, FvWHY3, FviWHY2 and FnWHY3 exhibited similar syntenic relationships with FaWHY4 and FaWHY5. In addition, FnWHY1 and FnWHY2 corresponded to both FaWHY1 and FaWHY2. Gene expression analysis revealed that five FaWHYs were expressed in crowns, and the regulation of FaWHYs was always consistent with the cis-elements in their promoters. All of them were downregulated by crown rot infected. Together, these results provided a basis for further functional studies of the FaWHYs proteins and their responses to crown rot.


2021 ◽  
Vol 17 (3) ◽  
pp. e1009235
Author(s):  
Hong Liu ◽  
Wenjie Xu ◽  
Vincent M. Bruno ◽  
Quynh T. Phan ◽  
Norma V. Solis ◽  
...  

To gain a better understanding of the transcriptional response of Aspergillus fumigatus during invasive pulmonary infection, we used a NanoString nCounter to assess the transcript levels of 467 A. fumigatus genes during growth in the lungs of immunosuppressed mice. These genes included ones known to respond to diverse environmental conditions and those encoding most transcription factors in the A. fumigatus genome. We found that invasive growth in vivo induces a unique transcriptional profile as the organism responds to nutrient limitation and attack by host phagocytes. This in vivo transcriptional response is largely mimicked by in vitro growth in Aspergillus minimal medium that is deficient in nitrogen, iron, and/or zinc. From the transcriptional profiling data, we selected 9 transcription factor genes that were either highly expressed or strongly up-regulated during in vivo growth. Deletion mutants were constructed for each of these genes and assessed for virulence in mice. Two transcription factor genes were found to be required for maximal virulence. One was rlmA, which is required for the organism to achieve maximal fungal burden in the lung. The other was sltA, which regulates of the expression of multiple secondary metabolite gene clusters and mycotoxin genes independently of laeA. Using deletion and overexpression mutants, we determined that the attenuated virulence of the ΔsltA mutant is due in part to decreased expression aspf1, which specifies a ribotoxin, but is not mediated by reduced expression of the fumigaclavine gene cluster or the fumagillin-pseruotin supercluster. Thus, in vivo transcriptional profiling focused on transcription factors genes provides a facile approach to identifying novel virulence regulators.


2018 ◽  
Vol 11 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Seyed Mehdi Jazayeri ◽  
Mahtab Pooralinaghi ◽  
Ronald Oswaldo Villamar Torres

Transcription factors (TF) are the elements, which regulate gene expression. Regulatory function of TFs play an important role in plant biological processes and mechanisms. They may interconnect with other transcription factors or functional genes to modulate their expression in response to an internal/external factor like life cycle stage, growth, development and stress. Arabidopsis is the well-known and the most used model organism. Transcription factors of three Arabidopsis species including A. halleri, A. lyrata and A. thaliana, were compared. basic/helix-loop-helix (bHLH) with 220 TFs was the most abundant family among three Arabidopsis species while MYB and MYB related families considering as a whole group were more than bHLH with 308 TFs. No STERILE APETALA (SAP) TF homolog was found for A.halleri.  The common transcription factors among three species were 4,172 grouped in 1,212 clusters. The species-specific clustered TFs were 12, 30 and 58 for A. halleri, A. lyrata and A. thaliana respectively. Eight hundred ninety two single-copy gene clusters those have one gene copy from each species, i.e. 2,676 genes, were listed. Four hundred forty five TF singletons were not clustered and are unique among three species. For clustered TF belonging to each species, GO terms and SwissProt hits showed that A. halleri has two species-specific TFs involved in heavy metal response including Zinc finger protein AZF2 and two-component response regulator ARR11 while for A. lyrata specific TFs are involved in stress response and plant development. A. thaliana specific clustered TFs work on plant flower development and acclimation.


2018 ◽  
Vol 11 (1) ◽  
pp. 1-9
Author(s):  
Seyed Mehdi Jazayeri ◽  
Mahtab Pooralinaghi ◽  
Ronald Villamar Torres ◽  
Luz García Cruzatty

Transcription factors (TF) are the elements, which regulate gene expression. Regulatory function of TFs play an important role in plant biological processes and mechanisms. They may interconnect with other transcription factors or functional genes to modulate their expression in response to an internal/external factor like life cycle stage, growth, development and stress. Arabidopsis is the well-known and the most used model organism. Transcription factors of three Arabidopsis species including A. halleri, A. lyrata and A. thaliana, were compared. basic/helix-loop-helix (bHLH) with 220 TFs was the most abundant family among three Arabidopsis species while MYB and MYB related families considering as a whole group were more than bHLH with 308 TFs. No STERILE APETALA (SAP) TF homolog was found for A.halleri.  The common transcription factors among three species were 4,172 grouped in 1,212 clusters. The species-specific clustered TFs were 12, 30 and 58 for A. halleri, A. lyrata and A. thaliana respectively. Eight hundred ninety two single-copy gene clusters those have one gene copy from each species, i.e. 2,676 genes, were listed. Four hundred forty five TF singletons were not clustered and are unique among three species. For clustered TF belonging to each species, GO terms and SwissProt hits showed that A. halleri has two species-specific TFs involved in heavy metal response including Zinc finger protein AZF2 and two-component response regulator ARR11 while for A. lyrata specific TFs are involved in stress response and plant development. A. thaliana specific clustered TFs work on plant flower development and acclimation.


2022 ◽  
Author(s):  
Nian Liu ◽  
Manish Pandey ◽  
Bei Wu ◽  
Li Huang ◽  
Huaiyong Luo ◽  
...  

Abstract The wild allotetraploid peanut Arachis monticola contains higher oil content than cultivated allotetraploid Arachis hypogaea. To investigate its molecular mechanism controlling oil accumulation, we performed comparative transcriptomics from developing seeds between three Arachis monticola and five Arachis hypogaea varieties. The analysis not only showed species-specific grouping based on transcriptional profiles but also detected two gene clusters with divergent expression patterns enriched in lipid metabolism. Further, the differential expression gene analysis also indicated expression alteration in wild peanut leading to enhanced activity of oil biogenesis and limiting the rate of lipid degradation. We also constructed a regulatory network of lipid metabolic DEGs with co-expressed transcription factors. In addition, bisulfite sequencing was conducted to characterize the variation of DNA methylation between wild allotetraploid (245, WH 10025) and cultivated allotetraploid (Z16, Zhh 7720) genotypes. Genome-wide DNA methylation was found antagonistically correlated with gene expression during seed development. The results indicated that CG and CHG contexts methylation may negatively regulate specific lipid metabolic genes and transcription factors to subtly affect the difference of oil accumulation. Our work provided the first glimpse on the regulatory mechanism of gene expression altering for oil accumulation in wild peanut and gene resources for future breeding applications.


2006 ◽  
Vol 34 (1) ◽  
pp. 185-187 ◽  
Author(s):  
S. Rinaldo ◽  
G. Giardina ◽  
M. Brunori ◽  
F. Cutruzzolà

All denitrifiers can keep the steady-state concentrations of nitrite and nitric oxide (NO) below cytotoxic levels by controlling the expression of denitrification gene clusters by redox signalling through transcriptional regulators belonging to the CRP (cAMP receptor protein)/FNR (fumarate and nitrate reductase regulator) superfamily.


2011 ◽  
Vol 5 ◽  
pp. BBI.S6362 ◽  
Author(s):  
Wei-Sheng Wu

Transcription factors control gene expression by binding to short specific DNA sequences, called transcription factor binding sites (TFBSs), in the promoter of a gene. Thus, studying the spatial distribution of TFBSs in the promoters may provide insights into the molecular mechanisms of gene regulation. I developed a method to construct the spatial distribution of TFBSs for any set of genes of interest. I found that different functional gene clusters have different spatial distributions of TFBSs, indicating that gene regulation mechanisms may be very different among different functional gene clusters. I also found that the binding sites for different transcription factors (TFs) may have different spatial distributions: a sharp peak, a plateau or no dominant single peak. The spatial distributions of binding sites for many TFs derived from my analyses are valuable prior information for TFBS prediction algorithm because different regions of a promoter can assign different possibilities for TFBS occurrence.


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