MTHFR Knockdown Assists Cell Defense against Folate Depletion Induced Chromosome Segregation and Uracil Misincorporation in DNA.

Folate depletion causes chromosomal instability by increasing DNA strand breakage, uracil misincorporation, and defective repair. Folate mediated one-carbon metabolism has been suggested to play a key role in the carcinogenesis and progression of hepatocellular carcinoma (HCC) through influencing DNA integrity. Methylenetetrahydrofolate reductase (MTHFR) is the enzyme catalyzing the irreversible conversion of 5,10-methylenetetrahydrofolate to 5-methyltetrahydrofolate that can control folate cofactor distributions and modulate the partitioning of intracellular one-carbon moieties. The association between MTHFR polymorphisms and HCC risk is inconsistent and remains controversial in populational studies. We aimed to establish an in vitro cell model of liver origin to elucidate the interactions between MTHFR function, folate status, and chromosome stability. In the present study, we (1) examined MTHFR expression in HCC patients; (2) established cell models of liver origin with stabilized inhibition of MTHFR using small hairpin RNA delivered by a lentiviral vector, and (3) investigated the impacts of reduced MTHFR and folate status on cell cycle, methyl group homeostasis, nucleotide biosynthesis, and DNA stability, all of which are pathways involved in DNA integrity and repair and are critical in human tumorigenesis. By analyzing the TCGA/GTEx datasets available within GEPIA2, we discovered that HCC cancer patients with higher MTHFR had a worse survival rate. The shRNA of MTHFR (shMTHFR) resulted in decreased MTHFR gene expression, MTHFR protein, and enzymatic activity in human hepatoma cell HepG2. shMTHFR tended to decrease intracellular S-adenosylmethionine (SAM) contents but folate depletion similarly decreased SAM in wildtype (WT), negative control (Neg), and shMTHFR cells, indicating that in cells of liver origin, shMTHFR does not exacerbate the methyl group supply in folate depletion. shMTHFR caused cell accumulations in the G2/M, and cell population in the G2/M was inversely correlated with MTHFR gene level (r = -0.81, p < 0.0001), MTHFR protein expression (r = -0.8; p = 0.01), and MTHFR enzyme activity (r = -0.842; p = 0.005). Folate depletion resulted in G2/M cell cycle arrest in WT and Neg but not in shMTHFR cells, indicating that shMTHFR does not exacerbate folate depletion-induced G2/M cell cycle arrest. In addition, shMTHFR promoted the expression and translocation of nuclei thymidine synthetic enzyme complex SHMT1/DHFR/TYMS and assisted folate-dependent de novo nucleotide biosynthesis under folate restriction. Finally, shMTHFR promoted nuclear MLH1/p53 expression under folate deficiency and further reduced micronuclei formation and DNA uracil misincorporation under folate deficiency. In conclusion, shMTHFR in HepG2 induces cell cycle arrest in G2/M that may promote nucleotide supply and assist cell defense against folate depletion-induced chromosome segregation and uracil misincorporation in the DNA. This study provided insight into the significant impact of MTHFR function on chromosome stability of hepatic tissues. Data from the present study may shed light on the potential regulatory mechanism by which MTHFR modulates the risk for hepatic malignancies.

Cobalt-Catalyzed Intermolecular Hydrofunctionalization of Alkenes: Evidence for a Bimetallic Pathway.

A functional group tolerant cobalt-catalyzed method for the intermolecular hydrofunctionalization of alkenes with oxygen- and nitrogen-based nucleophiles is reported. This protocol features a strategic use of hypervalent iodine(III) reagents that enables a mechanistic shift from conventional cobalt-hydride catalysis. Key evidence was found supporting a unique bimetallic-mediated rate-limiting step involving two distinct cobalt(III) species, from which a new carbon-heteroatom bond is formed.

Transcription between human-readable synthetic descriptions and machine-executable instructions: an application of the latest pre-training technology.

AI has been widely applied in scientific scenarios, such as robots performing chemical synthetic actions to free researchers from monotonous experimental procedures. However, there exists a gap between human-readable natural language descriptions and machine-executable instructions, of which the former are typically in numerous chemical articles, and the latter are currently compiled manually by experts. We apply the latest technology of pre-trained models and achieve automatic transcription between descriptions and instructions. We design a concise and comprehensive schema of instructions and construct an open-source human-annotated dataset consisting of 3950 description-instruction pairs, with 9.2 operations in each instruction on average. We further propose knowledgeable pre-trained transcription models enhanced by multi-grained chemical knowledge. The performance of recent popular models and products showing great capability in automatic writing (e.g., ChatGPT) has also been explored. Experiments prove that our system improves the instruction compilation efficiency of researchers by at least 42%, and can generate fluent academic paragraphs of synthetic descriptions when given instructions, showing the great potential of pre-trained models in improving human productivity.