Engineering cell metabolism for bioproduction not only consumes building blocks and energy molecules (e.g., ATP) but also triggers energetic inefficiency inside the cell. The metabolic burdens on microbial workhorses lead to undesirable physiological changes, placing hidden constraints on host productivity. We discuss cell physiological responses to metabolic burdens, as well as strategies to identify and resolve the carbon and energy burden problems, including metabolic balancing, enhancing respiration, dynamic regulatory systems, chromosomal engineering, decoupling cell growth with production phases, and co-utilization of nutrient resources. To design robust strains with high chances of success in industrial settings, novel genome-scale models (GSMs), (13)C-metabolic flux analysis (MFA), and machine-learning approaches are needed for weighting, standardizing, and predicting metabolic costs.

The Streptococcus thermophilus CRISPR3-Cas (StCas9) system has been shown to mediate DNA cleavage in its original host and in E. coli as well as in vitro. Here, we have reconstituted the StCas9 system in yeast and conducted a systematic optimization of the sgRNA structure, including the minimal length of tracrRNA, loop structure, Match II region, Bulge motif, the minimal length of guide sequence within the crRNA, tolerance of mismatches and target sequence preference. The optimal sgRNA design for the StCas9 system achieved up to 12 and 40 % targeting efficiencies in yeast and human cells, respectively. This study provides important insight into the sequence and structural requirements necessary to develop a targeted and highly efficient eukaryotic gene editing platform using CRISPR-Cas systems.

The cytochrome P450 monooxygenases (P450s) represent a large and important enzyme superfamily in plants. They catalyze numerous monooxygenation/hydroxylation reactions in biochemical pathways, P450s are involved in a variety of metabolic pathways and participate in the homeostasis of phytohormones. The CYP82 family genes specifically reside in dicots and are usually induced by distinct environmental stresses. However, their functions are largely unknown, especially in soybean (Glycine max L.). Here, we report the function of GmCYP82A3, a gene from soybean CYP82 family. Its expression was induced by Phytophthora sojae infection, salinity and drought stresses, and treatment with methyl jasmonate (MeJA) or ethephon (ETH). Its expression levels were consistently high in resistant cultivars. Transgenic Nicotiana benthamiana plants overexpressing GmCYP82A3 exhibited strong resistance to Botrytis cinerea and Phytophthora parasitica, and enhanced tolerance to salinity and drought stresses. Furthermore, transgenic plants were less sensitive to jasmonic acid (JA), and the enhanced resistance was accompanied with increased expression of the JA/ET signaling pathway-related genes.

Metabolic diversity in microorganisms can provide the basis for creating novel biochemical products. However, most metabolic engineering projects utilize a handful of established model organisms and thus, a challenge for harnessing the potential of novel microbial functions is the ability to either heterologously express novel genes or directly utilize non-model organisms. Genetic manipulation of non-model microorganisms is still challenging due to organism-specific nuances that hinder universal molecular genetic tools and translatable knowledge of intracellular biochemical pathways and regulatory mechanisms. However, in the past several years, unprecedented progress has been made in synthetic biology, molecular genetics tools development, applications of omics data techniques, and computational tools that can aid in developing non-model hosts in a systematic manner. In this review, we focus on concerns and approaches related to working with non-model microorganisms including developing molecular genetics tools such as shuttle vectors, selectable markers, and expression systems. In addition, we will discuss: (1) current techniques in controlling gene expression (transcriptional/translational level), (2) advances in site-specific genome engineering tools [homologous recombination (HR) and clustered regularly interspaced short palindromic repeats (CRISPR)], and (3) advances in genome-scale metabolic models (GSMMs) in guiding design of non-model species. Application of these principles to metabolic engineering strategies for consolidated bioprocessing (CBP) will be discussed along with some brief comments on foreseeable future prospects.

We finely map a novel resistance gene ( RpsJS ) to Phytophthora sojae in soybean. RpsJS was mapped in 138.9 kb region, including three NBS-LRR type predicted genes, on chromosome 18. Phytophthora root rot (PRR) caused by Phytophthora sojae (P. sojae) has been reported in most soybean-growing regions throughout the world. Development of PRR resistance varieties is the most economical and environmentally safe method for controlling this disease. Chinese soybean line Nannong 10-1 is resistant to many P. sojae isolates, and shows different reaction types to P. sojae isolates as compared with those with known Rps (Resistance to P. sojae) genes, which suggests that the line may carry novel Rps genes or alleles. A mapping population of 231 F(2) individuals from the cross of Nannong 10-1 (Resistant, R) and 06-070583 (Susceptible, S) was used to map the Rps gene. The segregation fits a ratio of 3R:1S within F(2) plants, indicating that resistance in Nannong 10-1 is controlled by a single dominant gene (designated as RpsJS). The results showed that RpsJS was mapped on soybean chromosome 18 (molecular linkage group G, MLG G) flanked by SSR (simple repeat sequences) markers BARCSOYSSR_18_1859 and SSRG60752K at a distance of 0.9 and 0.4 cm, respectively. Among the 14 genes annotated in this 138.9 kb region between the two markers, three genes (Glyma18g51930, Glyma18g51950 and Glyma18g51960) are the nucleotide-binding site and a leucine-rich repeat (NBS-LRR) type gene, which may be involved in recognizing the presence of pathogens and ultimately conferring resistance. Based on marker-assisted resistance spectrum analyses of RpsJS and the mapping results, we inferred that RpsJS was a novel gene or a new allele at the Rps4, Rps5 or Rps6 loci.