Carbon-dependent control of electron transfer and central carbon pathway genes for methane biosynthesis in the Archaean, Methanosarcina acetivorans strain C2A

<p>Abstract</p> <p>Background</p> <p>The archaeon, <it>Methanosarcina acetivorans </it>strain C2A forms methane, a potent greenhouse gas, from a variety of one-carbon substrates and acetate. Whereas the biochemical pathways leading to methane formation are well understood, little is known about the expression of the many of the genes that encode proteins needed for carbon flow, electron transfer and/or energy conservation. Quantitative transcript analysis was performed on twenty gene clusters encompassing over one hundred genes in <it>M. acetivorans </it>that encode enzymes/proteins with known or potential roles in substrate conversion to methane.</p> <p>Results</p> <p>The expression of many seemingly "redundant" genes/gene clusters establish substrate dependent control of approximately seventy genes for methane production by the pathways for methanol and acetate utilization. These include genes for soluble-type and membrane-type heterodisulfide reductases (<it>hdr</it>), hydrogenases including genes for a <it>vht</it>-type F420 non-reducing hydrogenase, molybdenum-type (<it>fmd</it>) as well as tungsten-type (<it>fwd</it>) formylmethanofuran dehydrogenases, genes for <it>rnf </it>and <it>mrp-</it>type electron transfer complexes, for acetate uptake, plus multiple genes for <it>aha- </it>and <it>atp</it>-type ATP synthesis complexes. Analysis of promoters for seven gene clusters reveal UTR leaders of 51-137 nucleotides in length, raising the possibility of both transcriptional and translational levels of control.</p> <p>Conclusions</p> <p>The above findings establish the differential and coordinated expression of two major gene families in <it>M. acetivorans </it>in response to carbon/energy supply. Furthermore, the quantitative mRNA measurements demonstrate the dynamic range for modulating transcript abundance. Since many of these gene clusters in <it>M. acetivorans </it>are also present in other <it>Methanosarcina </it>species including <it>M. mazei</it>, and in <it>M. barkeri</it>, these findings provide a basis for predicting related control in these environmentally significant methanogens.</p

Identification of the Major Expressed S-Layer and Cell Surface-Layer-Related Proteins in the Model Methanogenic Archaea: Methanosarcina barkeri Fusaro and Methanosarcina acetivorans C2A

Many archaeal cell envelopes contain a protein coat or sheath composed of one or more surface exposed proteins. These surface layer (S-layer) proteins contribute structural integrity and protect the lipid membrane from environmental challenges. To explore the species diversity of these layers in the Methanosarcinaceae, the major S-layer protein in Methanosarcina barkeri strain Fusaro was identified using proteomics. The Mbar_A1758 gene product was present in multiple forms with apparent sizes of 130, 120, and 100 kDa, consistent with post-translational modifications including signal peptide excision and protein glycosylation. A protein with features related to the surface layer proteins found in Methanosarcina acetivorans C2A and Methanosarcina mazei Goel was identified in the M. barkeri genome. These data reveal a distinct conserved protein signature with features and implied cell surface architecture in the Methanosarcinaceae that is absent in other archaea. Paralogous gene expression patterns in two Methanosarcina species revealed abundant expression of a single S-layer paralog in each strain. Respective promoter elements were identified and shown to be conserved in mRNA coding and upstream untranslated regions. Prior M. acetivorans genome annotations assigned S-layer or surface layer associated roles of eighty genes: however, of 68 examined none was significantly expressed relative to the experimentally determined S-layer gene

Proteomic analysis reveals metabolic and regulatory systems involved in the syntrophic and axenic lifestyle of Syntrophomonas wolfei

Microbial syntrophy is a vital metabolic interaction necessary for the complete oxidation of organic biomass to methane in all-anaerobic ecosystems. However, this process is thermodynamically constrained and represents an ecosystem-level metabolic bottleneck. To gain insight into the physiology of this process, a shotgun proteomics approach was used to quantify the protein landscape of the model syntrophic metabolizer, Syntrophomonas wolfei, grown axenically and syntrophically with Methanospirillum hungatei. Remarkably, the abundance of most proteins as represented by normalized spectral abundance factor (NSAF) value changed very little between the pure and coculture growth conditions. Among the most abundant proteins detected were GroEL and GroES chaperonins, a small heat shock protein, and proteins involved in electron transfer, beta-oxidation, and ATP synthesis. Several putative energy conservation enzyme systems that utilize NADH and ferredoxin were present. The abundance of an EtfAB2 and the membrane-bound iron-sulfur oxidoreductase (Swol_0698 gene product) delineated a potential conduit for electron transfer between acyl-CoA dehydrogenases and membrane redox carriers. Proteins detected only when S. wolfei was grown with M. hungatei included a zinc-dependent dehydrogenase with a GroES domain, whose gene is present in genomes in many organisms capable of syntrophy, and transcriptional regulators responsive to environmental stimuli or the physiological status of the cell. The proteomic analysis revealed an emphasis on macromolecular stability and energy metabolism by S. wolfei and presence of regulatory mechanisms responsive to external stimuli and cellular physiological status

Complete genome sequence of Syntrophobacter fumaroxidans strain (MPOB(T)).

Syntrophobacter fumaroxidans strain MPOB(T) is the best-studied species of the genus Syntrophobacter. The species is of interest because of its anaerobic syntrophic lifestyle, its involvement in the conversion of propionate to acetate, H2 and CO2 during the overall degradation of organic matter, and its release of products that serve as substrates for other microorganisms. The strain is able to ferment fumarate in pure culture to CO2 and succinate, and is also able to grow as a sulfate reducer with propionate as an electron donor. This is the first complete genome sequence of a member of the genus Syntrophobacter and a member genus in the family Syntrophobacteraceae. Here we describe the features of this organism, together with the complete genome sequence and annotation. The 4,990,251 bp long genome with its 4,098 protein-coding and 81 RNA genes is a part of the Microbial Genome Program (MGP) and the Genomes to Life (GTL) Program project

The value of genome-wide analysis in craniosynostosis

Background: This study assessed the diagnostic yield of high-throughput sequencing methods in a cohort of craniosynostosis (CS) patients not presenting causal variants identified through previous targeted analysis.Methods: Whole-genome or whole-exome sequencing (WGS/WES) was performed in a cohort of 59 patients (from 57 families) assessed by retrospective phenotyping as having syndromic or nonsyndromic CS.Results: A syndromic form was identified in 51% of the unrelated cases. A genetic cause was identified in 38% of syndromic cases, with novel variants detected in FGFR2 (a rare Alu insertion), TWIST1, TCF12, KIAA0586, HDAC9, FOXP1, and NSD2. Additionally, we report two patients with rare recurrent variants in KAT6A and YY1 as well as two patients with structural genomic aberrations: one with a 22q13 duplication and one with a complex rearrangement involving chromosome 2 (2p25 duplication including SOX11 and deletion of 2q22). Moreover, we identified potentially relevant variants in 87% of the remaining families with no previously detected causal variants, including novel variants in ADAMTSL4, ASH1L, ATRX, C2CD3, CHD5, ERF, H4C5, IFT122, IFT140, KDM6B, KMT2D, LTBP1, MAP3K7, NOTCH2, NSD1, SOS1, SPRY1, POLR2A, PRRX1, RECQL4, TAB2, TAOK1, TET3, TGFBR1, TCF20, and ZBTB20.Conclusion: These results confirm WGS/WES as a powerful diagnostic tool capable of either targeted in silico or broad genomic analysis depending on phenotypic presentation (e.g., classical or unusual forms of syndromic CS)

Pyrophosphate-Dependent ATP Formation from Acetyl Coenzyme A in Syntrophus aciditrophicus, a New Twist on ATP Formation.

UnlabelledSyntrophus aciditrophicus is a model syntrophic bacterium that degrades key intermediates in anaerobic decomposition, such as benzoate, cyclohexane-1-carboxylate, and certain fatty acids, to acetate when grown with hydrogen-/formate-consuming microorganisms. ATP formation coupled to acetate production is the main source for energy conservation by S.&nbsp;aciditrophicus However, the absence of homologs for phosphate acetyltransferase and acetate kinase in the genome of S.&nbsp;aciditrophicus leaves it unclear as to how ATP is formed, as most fermentative bacteria rely on these two enzymes to synthesize ATP from acetyl coenzyme A (CoA) and phosphate. Here, we combine transcriptomic, proteomic, metabolite, and enzymatic approaches to show that S.&nbsp;aciditrophicus uses AMP-forming, acetyl-CoA synthetase (Acs1) for ATP synthesis from acetyl-CoA. acs1 mRNA and Acs1 were abundant in transcriptomes and proteomes, respectively, of S.&nbsp;aciditrophicus grown in pure culture and coculture. Cell extracts of S.&nbsp;aciditrophicus had low or undetectable acetate kinase and phosphate acetyltransferase activities but had high acetyl-CoA synthetase activity under all growth conditions tested. Both Acs1 purified from S.&nbsp;aciditrophicus and recombinantly produced Acs1 catalyzed ATP and acetate formation from acetyl-CoA, AMP, and pyrophosphate. High pyrophosphate levels and a high AMP-to-ATP ratio (5.9 ± 1.4) in S.&nbsp;aciditrophicus cells support the operation of Acs1 in the acetate-forming direction. Thus, S.&nbsp;aciditrophicus has a unique approach to conserve energy involving pyrophosphate, AMP, acetyl-CoA, and an AMP-forming, acetyl-CoA synthetase.ImportanceBacteria use two enzymes, phosphate acetyltransferase and acetate kinase, to make ATP from acetyl-CoA, while acetate-forming archaea use a single enzyme, an ADP-forming, acetyl-CoA synthetase, to synthesize ATP and acetate from acetyl-CoA. Syntrophus aciditrophicus apparently relies on a different approach to conserve energy during acetyl-CoA metabolism, as its genome does not have homologs to the genes for phosphate acetyltransferase and acetate kinase. Here, we show that S.&nbsp;aciditrophicus uses an alternative approach, an AMP-forming, acetyl-CoA synthetase, to make ATP from acetyl-CoA. AMP-forming, acetyl-CoA synthetases were previously thought to function only in the activation of acetate to acetyl-CoA

A Vocabulon Study of E.Coli Regulatory Sites with Feedback to Expression Array Analysis

The identification of binding sites for regulatory proteins in the up-stream region of genes is an important ingredient towards the understanding of transcription regulation. In recent years, novel experimental techniques, as gene expression arrays, and the availability of entire genome sequences have opened the possibility for more detailed investigations in this domain. Traditionally, the reconstruction of the profile of a binding site and the localization of all its occurrences in a sequence are treated as separate problems. The first is tackled using a small group of sequences, known or suspected to contain the binding site, but with neither position or pattern known. One successful approach to such reconstruction problem is based on a probabilistic model of the sequence, represented as concatenation of background and motif stochastic words. Maximum likelihood or maximum a-posteriori estimates are obtained with EM or Gibbs-sampler algorithms [13, 14]. The second problem is approached considering one or multiple sequences of variable length; the pattern characterizing the motif is assumed known. Possible locations are identified on the base of scoring functions that highlight the similarity of the motif with the sequence portions. Cut off values for such similarity scores are hard to determine: ad hoc solutions or estimations on a training set are often adopted [17, 18]. Typically these techniques are used to scan one sequence of interest against a data-base of known binding sites. While there are historical and practical reasons to consider these two problems as separate, the current post-genomic era, where we are confronted with large abundance of sequence, calls for a different approach. Consider the problem, tackled in [18], of identifying all the the binding sites of the known regulatory proteins in the genome of E. Coli. While formally similar to blasting a small sequence of interest against a data-base of known regulatory proteins, there are substantial differences in these genome-wide search. On the one hand, as one scans through the genome for binding sites of LexA—to take one example—and finds a substantial number of them, it seems appropriate one should use the information in the identified locations to update the current pattern description. On the other hand, given that the output is not going to include a small number of sites, that can be further investigated, but a large collection of them, the assessment of significance cut-off should be based on proper probabilistic statements. To address these issues, one would need a probability model for the entire genome sequence, that can lead to evaluation of specific a-posteriori probabilities of appearance of a binding site in any given location, and whose parameters can be estimated on the base of data. At the same time, given the scale of the problem, the model should be suitable for rapid computation. In an attempt to address such need we introduce here the Vocabulon model. Section 2 gives a description of the probability model we employ; its differences from others in the literature; and its current implementation. We then present the results of multiple investigations on E. Coli sequence. Given that genome-wide information on the location of binding sites is not available, we used results of gene expression array experiments to corroborate our results, arguing in favor of a novel perspective in array analysis

A Vocabulon Study of E.Coli Regulatory Sites with Feedback to Expression Array Analysis

The identification of binding sites for regulatory proteins in the up-stream region of genes is an important ingredient towards the understanding of transcription regulation. In recent years, novel experimental techniques, as gene expression arrays, and the availability of entire genome sequences have opened the possibility for more detailed investigations in this domain. Traditionally, the reconstruction of the profile of a binding site and the localization of all its occurrences in a sequence are treated as separate problems. The first is tackled using a small group of sequences, known or suspected to contain the binding site, but with neither position or pattern known. One successful approach to such reconstruction problem is based on a probabilistic model of the sequence, represented as concatenation of background and motif stochastic words. Maximum likelihood or maximum a-posteriori estimates are obtained with EM or Gibbs-sampler algorithms [13, 14]. The second problem is approached considering one or multiple sequences of variable length; the pattern characterizing the motif is assumed known. Possible locations are identified on the base of scoring functions that highlight the similarity of the motif with the sequence portions. Cut off values for such similarity scores are hard to determine: ad hoc solutions or estimations on a training set are often adopted [17, 18]. Typically these techniques are used to scan one sequence of interest against a data-base of known binding sites. While there are historical and practical reasons to consider these two problems as separate, the current post-genomic era, where we are confronted with large abundance of sequence, calls for a different approach. Consider the problem, tackled in [18], of identifying all the the binding sites of the known regulatory proteins in the genome of E. Coli. While formally similar to blasting a small sequence of interest against a data-base of known regulatory proteins, there are substantial differences in these genome-wide search. On the one hand, as one scans through the genome for binding sites of LexA—to take one example—and finds a substantial number of them, it seems appropriate one should use the information in the identified locations to update the current pattern description. On the other hand, given that the output is not going to include a small number of sites, that can be further investigated, but a large collection of them, the assessment of significance cut-off should be based on proper probabilistic statements. To address these issues, one would need a probability model for the entire genome sequence, that can lead to evaluation of specific a-posteriori probabilities of appearance of a binding site in any given location, and whose parameters can be estimated on the base of data. At the same time, given the scale of the problem, the model should be suitable for rapid computation. In an attempt to address such need we introduce here the Vocabulon model. Section 2 gives a description of the probability model we employ; its differences from others in the literature; and its current implementation. We then present the results of multiple investigations on E. Coli sequence. Given that genome-wide information on the location of binding sites is not available, we used results of gene expression array experiments to corroborate our results, arguing in favor of a novel perspective in array analysis