High-efficient production of L-homoserine in Escherichia coli through engineering synthetic pathway combined with regulating cell division.

L-Homoserine is an important amino acid as a precursor in synthesizing many valuable products. However, the low productivity caused by slow L-homoserine production during active cell growth in fermentation hinders its potential applications. In this study, strategies of engineering the synthetic pathway combined with regulating cell division were employed in an L-homoserine-producing Escherichia coli strain for efficiently biomanufacturing L-homoserine. First, the flux-control genes in the L-homoserine degradation pathway were omitted to redistribute carbon flux. To drive more carbon flux into L-homoserine production, the phosphoenolpyruvate-pyruvate-oxaloacetate loop was redrawn. Subsequently, the cell division was engineered by using the self-regulated promoters to coordinate cell growth and L-homoserine production. The ultimate strain HOM23 produced 101.31 g/L L-homoserine with a productivity of 1.91 g/L/h, which presented the highest L-homoserine titer and productivity to date from plasmid-free strains. The strategies used in this study could be applied to constructing cell factories for producing other L-aspartate derivatives.

Shared Genetics and Comorbid Genes of Amyotrophic Lateral Sclerosis and Parkinson's Disease.

BACKGROUND: Comorbidity exists between amyotrophic lateral sclerosis (ALS) and Parkinson's disease (PD), but the role of genetic factors is unclear. OBJECTIVE: We aim to investigate genetic correlation, causal relationship, and comorbid genes between ALS and PD. METHODS: Leveraging the largest genome-wide association study data (ALS: 27,205 cases, 110,881 controls; PDG: 33,674 cases, 449,056 controls), we used linkage disequilibrium score regression and Mendelian randomization analysis for genetic correlation and causal inference. We performed genome-wide cross-trait analysis via Multi-Trait Analysis of Genome-Wide Association Studies and Cross-Phenotype Association to identify specific single-nucleotide polymorphisms, followed by functional mapping and annotation. Integrating expression quantitative trait loci data from 13 brain regions, we conducted a transcriptome-wide association study via functional summary-based imputation and joint-tissue imputation to explore comorbid genes, followed by pathway enrichment analysis. RESULTS: We found that PD positively correlates with ALS (rg = 0.144, P = 0.026) and confers a causal effect (odds ratio = 1.09, 95% confidence interval: 1.03-1.15, P = 3.00 × 10-3 ). We identified nine single-nucleotide polymorphisms (eight new), associating with three risk loci (chromosomes 4, 10, and 17) and seven genes (TMEM175, MAPT, NSF, LRRC37A2, ARHGAP27, GAK, and FGFRL1). In transcriptome-wide association study analysis, we showed six previously unreported pleiotropic genes (KANSL1, ARL17B, EFNA1, WNT3, ERCC8, and ADAM15), and we found these candidate genes are mainly enriched in negative regulation of neuron projection development (GO:0010977). CONCLUSIONS: Our work demonstrates shared genetic architecture between ALS and PD, reports new pleiotropic genes, and sheds light on the comorbid mechanism. © 2023 International Parkinson and Movement Disorder Society.

Investigating the shared genetic architecture of post-traumatic stress disorder and gastrointestinal tract disorders: a genome-wide cross-trait analysis.

BACKGROUND: Observational studies suggest a correlation between post-traumatic stress disorder (PTSD) and gastrointestinal tract (GIT) disorders. However, the genetic overlap, causal relationships, and underlining mechanisms between PTSD and GIT disorders were absent. METHODS: We obtained genome-wide association study statistics for PTSD (23 212 cases, 151 447 controls), peptic ulcer disease (PUD; 16 666 cases, 439 661 controls), gastroesophageal reflux disease (GORD; 54 854 cases, 401 473 controls), PUD and/or GORD and/or medications (PGM; 90 175 cases, 366 152 controls), irritable bowel syndrome (IBS; 28 518 cases, 426 803 controls), and inflammatory bowel disease (IBD; 7045 cases, 449 282 controls). We quantified genetic correlations, identified pleiotropic loci, and performed multi-marker analysis of genomic annotation, fast gene-based association analysis, transcriptome-wide association study analysis, and bidirectional Mendelian randomization analysis. RESULTS: PTSD globally correlates with PUD (rg = 0.526, p = 9.355 × 10-7), GORD (rg = 0.398, p = 5.223 × 10-9), PGM (rg = 0.524, p = 1.251 × 10-15), and IBS (rg = 0.419, p = 8.825 × 10-6). Cross-trait meta-analyses identify seven genome-wide significant loci between PTSD and PGM (rs13107325, rs1632855, rs1800628, rs2188100, rs3129953, rs6973700, and rs73154693); three between PTSD and GORD (rs13107325, rs1632855, and rs3132450); one between PTSD and IBS/IBD (rs4937872 and rs114969413, respectively). Proximal pleiotropic genes are mainly enriched in immune response regulatory pathways, and in brain, digestive, and immune systems. Gene-level analyses identify five candidates: ABT1, BTN3A2, HIST1H3J, ZKSCAN4, and ZKSCAN8. We found significant causal effects of GORD, PGM, IBS, and IBD on PTSD. We observed no reverse causality of PTSD with GIT disorders, except for GORD. CONCLUSIONS: PTSD and GIT disorders share common genetic architectures. Our work offers insights into the biological mechanisms, and provides genetic basis for translational research studies.

Identification of 5 Potential Predictive Biomarkers for Alzheimer's Disease by Integrating the Unified Test for Molecular Signatures and Weighted Gene Coexpression Network Analysis.

BACKGROUND: Previous transcriptome-wide association study (TWAS) has documented 21 genes associated with Alzheimer's disease (AD) risk, but the predictive biomarkers remain unexplored. METHODS: TWAS leveraging the unified test for molecular signatures (UTMOST) was performed in 75,000 cases and 420,000 controls with 10 brain tissue gene expression references. Weighted gene coexpression network analysis (WGCNA) was conducted in GSE5281 and GSE48350 data sets containing 167 AD samples and 247 controls. Random forest (RF) analysis was applied to screen the potential predictive biomarkers based on overlapping genes identified by TWAS and WGCNA, followed by comprehensive bioinformatic analyses with differential gene expression, functional enrichment, and correlation with immune cells. A nomogram was established to verify the predictive power of the identified biomarkers. RESULTS: TWAS revealed 78 candidate genes (p < 2.89 × 10-6). In WGCNA turquoise module, 3 718 AD-related genes were screened. RF identified 5 predictive biomarkers (FAM71E1, DDB2, AP4M1, GPR4, DOC2A), which are enriched in the global genome nucleotide excision repair pathway and associated with immune cell designations "Natural.killer.T.cell," "Memory.B.cell," "T.follicular.helper.cell," "Neutrophil," and "MDSC." The nomogram based on the 5 markers showed a high predictive power. CONCLUSION: Five potential predictive biomarkers for AD were identified, providing new insights into the pathogenesis and etiology of AD.

Brain Proteome-Wide and Transcriptome-Wide Asso-ciation Studies, Bayesian Colocalization, and Mendelian Randomization Analyses Reveal Causal Genes of Parkinson's Disease.

How genome-wide associated loci confer risk for Parkinson's disease is unclear. We aim to reveal causal genes through effects on brain proteins to provide new pathogenesis insights for Parkinson's disease. Proteome-wide and transcriptome-wide associations were determined by functional summary-based imputation leveraging data from genome-wide association summary (56 306 Europeans, 1.4 million controls), brain proteomes (528 cases from 2 separate data sets), and transcriptome (452 cases), followed by Mendelian randomization, Bayesian colocalization, cell-type-specific and brain regional expression, and drug-gene interaction analyses. As a result, genetically regulated protein abundances of 11 genes were associated with Parkinson's disease. Five genes (CD38, GPNMB, TMEM175, RAB7L1, and HIP1R) were colocalized. Four genes (GPNMB, SEC23IP, CD38, and DGKQ) demonstrated Mendelian randomized correlations (p < 8.10 × 10-5). Higher GPNMB level (1.47, 1.28-1.68) and lower CD38 level (0.319, 0.24-0.43) were causally associated with higher risk of Parkinson's disease, consistent with transcriptomic evaluations. CD38 and GPNMB were preferentially enriched in astrocytes and oligodendrocyte precursor cells, respectively. And CD38 and GPNMB were suggested to be the targets of many oncological drugs from Drug-Gene Interaction database. In conclusion, utilizing multidimensional data, GPNMB and CD38 were prioritized as the causal genes of Parkinson's disease, crucial for mechanistic and therapeutic investigations.