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Genetic manipulation was undertaken in order to understand the mechanism involved in the heterologous synthesis of lycopene in Escherichia coli. Knockout of the central carbon metabolic gene zwf (glucose-6-phosphate dehydrogenase) resulted in the enhancement of lycopene production (above 130 % relative to control). The amplification and overexpression of rate-limiting steps encoded by idi (isopentenyl diphosphate isomerase), dxs (1-deoxyxylulose-5-phosphate synthase) and ispDF (4-diphosphocytidyl-2C-methyl-D-erythritol synthase and 2C-methyl-D-erythritol 2,4-cyclodiphosphate synthase) genes improved lycopene synthesis from 0.89 to 5.39 mg g(-1) DCW. The combination of central metabolic genes knockout with the amplification of MEP pathway genes yielded best amounts of lycopene (6.85-7.55 mg g(-1) DCW). Transcript profiling revealed that idi and dxs were up-regulated in the zwf knock-out strain, providing a plausible explanation for the increase in lycopene yield observed in this strain. An increase in precursor availability might also have contributed to the improved lycopene production.

Coronavirus disease 2019 (COVID-19) has caused thousands of deaths worldwide and has become an urgent public health concern. The extraordinary interhuman transmission of this disease has urged scientists to examine the various facets of its pathogenic agent, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Herein, based on publicly available genomic data, we analyzed the codon usage co-adaptation profiles of SARS-CoV-2 and other respiratory coronaviruses (CoVs) with their human host, identified CoV-responsive human genes and their functional roles on the basis of both the relative synonymous codon usage (RSCU)-based correlation of viral genes with human genes and differential gene expression analysis, and predicted potential drugs for COVID-19 treatment based on these genes. The relatively high codon adaptation index (CAI) values (> 0.70) signposted the gene expressivity efficiency of CoVs in human. The ENc-GC3 plot indicated that SARS-CoV-2 genome was under strict selection pressure while SARS-CoV and MERS-CoV were under selection and mutational pressures. The RSCU-based correlation analysis indicated that the viral genomes shared similar codons with a panoply of human genes. The merging of RSCU-based correlation data and SARS-CoV-2-responsive differentially expressed genes allowed the identification of human genes potentially affected by SARS-CoV-2 infection. Functional enrichment analysis indicated that these genes were enriched in biological processes and pathways related to host response to viral infection and immune response. Using the drug-gene interaction database, we screened a list of drugs that could target these genes as potential COVID-19 therapeutics. Our findings not only will contribute in vaccine development but also provide a useful set of drugs that could guide practitioners in strategical monitoring of COVID-19. We recommend practitioners to scrupulously screen this list of predicted drugs in order to authenticate those qualified for treating COVID-19 symptoms.

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