Extracytoplasmic function (ECF) sigma factor ^F is involved in Caulobacter crescentus response to heavy metal stress

Background: The -proteobacterium Caulobacter crescentus inhabits low-nutrient environments and can tolerate certain levels of heavy metals in these sites. It has been reported that C. crescentus responds to exposure to various heavy metals by altering the expression of a large number of genes.Results: In this work, we show that the ECF sigma factor F is one of the regulatory proteins involved in the control of the transcriptional response to chromium and cadmium. Microarray experiments indicate that F controls eight genes during chromium stress, most of which were previously described as induced by heavy metals. Surprisingly, F itself is not strongly auto-regulated under metal stress conditions. Interestingly, F-dependent genes are not induced in the presence of agents that generate reactive oxygen species. Promoter analyses revealed that a conserved F-dependent sequence is located upstream of all genes of the F regulon. In addition, we show that the second gene in the sigF operon acts as a negative regulator of F function, and the encoded protein has been named NrsF (Negative regulator of sigma F). Substitution of two conserved cysteine residues (C131 and C181) in NrsF affects its ability to maintain the expression of F-dependent genes at basal levels. Furthermore, we show that F is released into the cytoplasm during chromium stress and in cells carrying point mutations in both conserved cysteines of the protein NrsF.Conclusion: A possible mechanism for induction of the F-dependent genes by chromium and cadmium is the inactivation of the putative anti-sigma factor NrsF, leading to the release of F to bind RNA polymerase core and drive transcription of its regulon.</p

Global transcriptional response of Caulobacter crescentus to iron availability

Abstract\ud \ud \ud \ud Background\ud In the alpha subclass of proteobacteria iron homeostasis is controlled by diverse iron responsive regulators. Caulobacter crescentus, an important freshwater α-proteobacterium, uses the ferric uptake repressor (Fur) for such purpose. However, the impact of the iron availability on the C. crescentus transcriptome and an overall perspective of the regulatory networks involved remain unknown.\ud \ud \ud \ud Results\ud In this work we report the identification of iron-responsive and Fur-regulated genes in C. crescentus using microarray-based global transcriptional analyses. We identified 42 genes that were strongly upregulated both by mutation of fur and by iron limitation condition. Among them, there are genes involved in iron uptake (four TonB-dependent receptor gene clusters, and feoAB), riboflavin biosynthesis and genes encoding hypothetical proteins. Most of these genes are associated with predicted Fur binding sites, implicating them as direct targets of Fur-mediated repression. These data were validated by β-galactosidase and EMSA assays for two operons encoding putative transporters. The role of Fur as a positive regulator is also evident, given that 27 genes were downregulated both by mutation of fur and under low-iron condition. As expected, this group includes many genes involved in energy metabolism, mostly iron-using enzymes. Surprisingly, included in this group are also TonB-dependent receptors genes and the genes fixK, fixT and ftrB encoding an oxygen signaling network required for growth during hypoxia. Bioinformatics analyses suggest that positive regulation by Fur is mainly indirect. In addition to the Fur modulon, iron limitation altered expression of 113 more genes, including induction of genes involved in Fe-S cluster assembly, oxidative stress and heat shock response, as well as repression of genes implicated in amino acid metabolism, chemotaxis and motility.\ud \ud \ud \ud Conclusions\ud Using a global transcriptional approach, we determined the C. crescentus iron stimulon. Many but not all of iron responsive genes were directly or indirectly controlled by Fur. The iron limitation stimulon overlaps with other regulatory systems, such as the RpoH and FixK regulons. Altogether, our results showed that adaptation of C. crescentus to iron limitation not only involves increasing the transcription of iron-acquisition systems and decreasing the production of iron-using proteins, but also includes novel genes and regulatory mechanisms.We are grateful to Michael T. Laub for making the C. crescentus DNA microarray slides available and Carla Rosenberg lab for assistance with the microarray scanning. This work was supported by grant 470663/2011-1 from Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq). During the course of this work, JFSN and RFL were supported by postdoctoral fellowships, grants 2007/56306-0 and 2008/52874-6, from São Paulo Research Foundation (FAPESP). MVM is partly supported by CNPq.We are grateful to Michael T. Laub for making the C. crescentus DNA microarray slides available and Carla Rosenberg lab for assistance with the microarray scanning. This work was supported by grant 470663/20111 from Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq). During the course of this work, JFSN and RFL were supported by postdoctoral fellowships, grants 2007/563060 and 2008/528746, from São Paulo Research Foundation (FAPESP). MVM is partly supported by CNPq

Protein hydrolysates from salmon heads and cape hake by-products: comparing enzymatic method with subcritical water extraction on bioactivity properties

Fish by-products can be converted into high-value-added products like fish protein hydrolysates (FPHs), which have high nutritional value and are rich in bioactive peptides with health benefits. This study aims to characterise FPHs derived from salmon heads (HPSs) and Cape hake trimmings (HPHs) using Alcalase for enzymatic hydrolysis and Subcritical Water Hydrolysis (SWH) as an alternative method. All hydrolysates demonstrated high protein content (70.4–88.7%), with the degree of hydrolysis (DH) ranging from 10.7 to 36.4%. The peptide profile of FPHs indicated the breakdown of proteins into small peptides. HPSs showed higher levels of glycine and proline, while HPHs had higher concentrations of glutamic acid, leucine, threonine, and phenylalanine. Similar elemental profiles were observed in both HPHs and HPSs, and the levels of Cd, Pb, and Hg were well below the legislated limits. Hydrolysates do not have a negative effect on cell metabolism and contribute to cell growth. HPSs and HPHs exhibited high 2,2′–azino-bis(3 ethylbenzthiazoline-6)-sulfonic acid (ABTS) radical scavenging activity, Cu2+ and Fe2+ chelating activities, and angiotensin-converting enzyme (ACE) inhibitory activity, with HPHs generally displaying higher activities. The α-amylase inhibition of both FPHs was relatively low. These results indicate that HPHs are a promising natural source of nutritional compounds and bioactive peptides, making them potential candidates for use as an ingredient in new food products or nutraceuticals. SWH at 250 °C is a viable alternative to enzymatic methods for producing FPHs from salmon heads with high antioxidant and chelating properties.info:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost