No abstract

The thermotolerant yeast Kluyveromyces marxianus displays a potential to be used for ethanol production from both whey and lignocellulosic biomass at elevated temperatures, which is highly alluring to reduce the cost of the bioprocess. Nevertheless, contrary to Saccharomyces cerevisiae, K. marxianus cannot tolerate high ethanol concentrations. We report the transcriptional profile alterations in K. marxianus under ethanol stress in order to gain insights about mechanisms involved with ethanol response. Time-dependent changes have been characterized under the exposure of 6% ethanol and compared with the unstressed cells prior to the ethanol addition. Our results reveal that the metabolic flow through the central metabolic pathways is impaired under the applied ethanol stress. Consistent with these results, we also observe that genes involved with ribosome biogenesis are downregulated and gene-encoding heat shock proteins are upregulated. Remarkably, the expression of some gene-encoding enzymes related to unsaturated fatty acid and ergosterol biosynthesis decreases upon ethanol exposure, and free fatty acid and ergosterol measurements demonstrate that their content in K. marxianus does not change under this stress. These results are in contrast to the increase previously reported with S. cerevisiae subjected to ethanol stress and suggest that the restructuration of K. marxianus membrane composition differs in the two yeasts which gives important clues to understand the low ethanol tolerance of K. marxianus compared to S. cerevisiae.

Gene codon optimization may be impaired by the misinterpretation of frequency and optimality of codons. Although recent studies have revealed the effects of codon usage bias (CUB) on protein biosynthesis, an integrated perspective of the biological role of individual codons remains unknown. Unlike other previous studies, we show, through an integrated framework that attributes of codons such as frequency, optimality and positional dependency should be combined to unveil individual codon contribution for protein biosynthesis. We designed a codon quantification method for assessing CUB as a function of position within genes with a novel constraint: the relativity of position-dependent codon usage shaped by coding sequence length. Thus, we propose a new way of identifying the enrichment, depletion and non-uniform positional distribution of codons in different regions of yeast genes. We clustered codons that shared attributes of frequency and optimality. The cluster of non-optimal codons with rare occurrence displayed two remarkable characteristics: higher codon decoding time than frequent–non-optimal cluster and enrichment at the 5′-end region, where optimal codons with the highest frequency are depleted. Interestingly, frequent codons with non-optimal adaptation to tRNAs are uniformly distributed in the Saccharomyces cerevisiae genes, suggesting their determinant role as a speed regulator in protein elongation.

Methylorubrum extorquens (formerly Methylobacterium extorquens ) AM1 is a methylotrophic bacterium with a versatile lifestyle. Various carbon sources including acetate, succinate and methanol are utilized by M. extorquens AM1 with the latter being a promising inexpensive substrate for use in the biotechnology industry. Itaconic acid (ITA) is a high-value building block widely used in various industries. Given that no wildtype methylotrophic bacteria are able to utilize methanol to produce ITA, we tested the potential of M. extorquens AM1 as an engineered host for this purpose. In this study, we successfully engineered M. extorquens AM1 to express a heterologous codon-optimized gene encoding cis- aconitic acid decarboxylase. The engineered strain produced ITA using acetate, succinate and methanol as the carbon feedstock. The highest ITA titer in batch culture with methanol as the carbon source was 31.6 ± 5.5 mg/L, while the titer and productivity were 5.4 ± 0.2 mg/L and 0.056 ± 0.002 mg/L/h, respectively, in a scaled-up fed-batch bioreactor under 60% dissolved oxygen saturation. We attempted to enhance the carbon flux toward ITA production by impeding poly-β-hydroxybutyrate accumulation, which is used as carbon and energy storage, via mutation of the regulator gene phaR . Unexpectedly, ITA production by the phaR mutant strain was not higher even though poly-β-hydroxybutyrate concentration was lower. Genome-wide transcriptomic analysis revealed that phaR mutation in the ITA-producing strain led to complex rewiring of gene transcription, which might result in a reduced carbon flux toward ITA production. Besides poly-β-hydroxybutyrate metabolism, we found evidence that PhaR might regulate the transcription of many other genes including those encoding other regulatory proteins, methanol dehydrogenases, formate dehydrogenases, malate:quinone oxidoreductase, and those synthesizing pyrroloquinoline quinone and thiamine co-factors. Overall, M. extorquens AM1 was successfully engineered to produce ITA using acetate, succinate and methanol as feedstock, further supporting this bacterium as a feasible host for use in the biotechnology industry. This study showed that PhaR could have a broader regulatory role than previously anticipated, and increased our knowledge of this regulator and its influence on the physiology of M. extorquens AM1.

Understanding the interplay between genotype and phenotype is a fundamental goal of functional genomics. Methane oxidation is a microbial phenotype with global-scale significance as part of the carbon biogeochemical cycle and a sink for greenhouse gas. Microorganisms that oxidize methane (methanotrophs) are taxonomically diverse and widespread around the globe. In methanotrophic bacteria, enzymes in the methane oxidation metabolic module (KEGG module M00174, conversion of methane to formaldehyde) are encoded in four operons (pmoCAB, mmoXYZBCD, mxaFI, and xoxF). Recent reports have suggested that methanotrophs in Proteobacteria acquired methane monooxygenases through horizontal gene transfer. Here, we used a genomic meta-analysis to infer the transcriptional and translational advantages of coding sequences from the methane oxidation metabolic modules of different types of methanotrophs. By analyzing isolate and metagenome-assembled genomes from phylogenetically and geographically diverse sources, we detected an anomalous nucleotide composition bias in the coding sequences of particulate methane monooxygenase genes (pmoCAB) from type Ia methanotrophs. We found that this nucleotide bias increases the level of codon bias by decreasing the GC content in the third base of codons, a strategy that contrasts with that of other coding sequences in the module. Further codon usage analyses uncovered that codon variants of the type Ia pmoCAB coding sequences deviate from the genomic signature to match ribosomal protein-coding sequences. Subsequently, computation of transcription and translation metrics revealed that the pmoCAB coding sequences of type Ia methanotrophs optimize the usage of codon variants to maximize translation efficiency and accuracy, while minimizing the synthesis cost of transcripts and proteins. IMPORTANCE Microbial methane oxidation plays a fundamental role in the biogeochemical cycle of Earth’s system. Recent reports have provided evidence for the acquisition of methane monooxygenases by horizontal gene transfer in methane-oxidizing bacteria from different environments, but how evolution has shaped the coding sequences to execute methanotrophy efficiently remains unexplored. In this work, we provide genomic evidence that among the different types of methanotrophs, type Ia methanotrophs possess a unique coding sequence of the pmoCAB operon that is under positive selection for optimal resource allocation and efficient synthesis of transcripts and proteins. This adaptive trait possibly enables type Ia methanotrophs to respond robustly to fluctuating methane availability and explains their global prevalence.

Codon usage bias exerts control over a wide variety of molecular processes. The positioning of synonymous codons within coding sequences (CDSs) dictates protein expression by mechanisms such as local translation efficiency, mRNA Gibbs free energy, and protein cotranslational folding. In this work, we explore how codon usage affects the position-dependent content of hydrogen bonding, which in turn influences energy requirements for unwinding double-stranded DNA (dsDNA). We categorized codons according to their hydrogen bond content and found differential effects on hydrogen bonding encoded by codon variants. The specific positional disposition of codon variants within CDSs creates a ramp of hydrogen bonding at the 5ʹ end of the ORFeome in Escherichia coli. CDSs occupying the first position of operons are subjected to selective pressure that reduces their hydrogen bonding compared to internal CDSs, and highly transcribed CDSs demand a lower maximum capacity of hydrogen bonds per codon, suggesting that the energetic requirement for unwinding the dsDNA in highly transcribed CDSs has evolved to be minimized in E. coli. Subsequent analysis of over 14,000 ORFeomes showed a pervasive ramp of hydrogen bonding at the 5ʹ end in Bacteria and Archaea that positively correlates with the probability of mRNA secondary structure formation. Both the ramp and the correlation were not found in Fungi. The position-dependent hydrogen bonding might be part of the mechanism that contributes to the coordination between transcription and translation in Bacteria and Archaea. A Web-based application to analyze the position-dependent hydrogen bonding of ORFeomes has been developed and is publicly available (https://juanvillada.shinyapps.io/hbonds/). IMPORTANCE Redundancy of the genetic code creates a vast space of alternatives to encode a protein. Synonymous codons exert control over a variety of molecular and physiological processes of cells mainly through influencing protein biosynthesis. Recent findings have shown that synonymous codon choice affects transcription by controlling mRNA abundance, mRNA stability, transcription termination, and transcript biosynthesis cost. In this work, by analyzing thousands of Bacteria, Archaea, and Fungi genomes, we extend recent findings by showing that synonymous codon choice, corresponding to the number of hydrogen bonds in a codon, can also have an effect on the energetic requirements for unwinding double-stranded DNA in a position-dependent fashion. This report offers new perspectives on the mechanism behind the transcription-translation coordination and complements previous hypotheses on the resource allocation strategies used by Bacteria and Archaea to manage energy efficiency in gene expression.

Understanding the interplay between genotype and phenotype is a fundamental goal of functional genomics. Methane oxidation is a microbial phenotype with global-scale significance as part of the carbon biogeochemical cycle, and is a sink for greenhouse gas. Microorganisms that oxidize methane (methanotrophs) are taxonomically diverse and widespread around the globe. Recent reports have suggested that type Ia methanotrophs are the most prevalent methane-oxidizing bacteria in different environments. In methanotrophic bacteria, complete methane oxidation is encoded in four operons (pmoCAB, mmoXYZBCD, mxaFI, and xoxF ), but how evolution has shaped these genes to execute methane oxidation remains poorly understood. Here, we used a genomic meta-analysis to investigate the coding sequences that encode methane oxidation. By analyzing isolate and metagenome-assembled genomes from phylogenetically and geographically diverse sources, we detected an anomalous nucleotide composition bias in the coding sequences of particulate methane monooxygenase genes (pmoCAB) from type Ia methanotrophs around the globe. We found that this was a highly conserved sequence that optimizes codon usage in order to maximize translation efficiency and accuracy, while minimizing the synthesis cost of transcripts and proteins. We show that among the seven types of methanotrophs, only type Ia methanotrophs possess a unique coding sequence of the pmoCAB operon that is under positive selection for optimal resource allocation and efficient synthesis of transcripts and proteins in environmental counter gradients with high oxygen and low methane concentrations. This adaptive trait possibly enables type Ia methanotrophs to respond robustly to fluctuating methane availability and explains their global prevalence.

A genome-scale metabolic model (GEM) is an integrative platform that enables the incorporation of a wide range of experimental data. It is used to reveal system-level metabolism and, thus, clarify the link between the genotype and phenotype.