Project description:Developmental language disorder (DLD), previously known as specific language impairment, is a neurodevelopmental disorder. It affects approximately 7% of school-age children. The affected children fail to develop normal speech and language skills. This is a major public health concern as it adversely impacts the communication, academic, and social skills of the affected individual. The human brain development is a complex process that involves the accurate orchestration of the expression of multiple genes. Precise temporal and spatial regulation of gene expression is essential for proper brain development. Epigenetic factors such as DNA methylation can modulate gene expression without altering the DNA sequence. They are, therefore, considered as key regulators of the expression of genes involved in neurodevelopment. In this study, we examined any altered DNA methylation between children affected with DLD and healthy control subjects. We looked into genome-wide methylation differences between the DLD and control groups using Infinium HumanMethylation850 (EPICarray). Twelve children with DLD and 12 healthy controls were recruited for the study. Five milliliters of peripheral blood samples were collected from the study subjects in EDTA vials. Genomic DNA (gDNA) was extracted from the blood samples using the standard salting out protocol. Five micrograms of each DNA at 50 ng/µl concentration were used for genome-wide methylation analysis. The gDNA samples were bisulfite-treated with EpiTect Bisulfite Kit. Further to this, the DNA samples were subjected to whole genome amplification and enzymatic fragmentation. Human Infinium Methylation EPIC BeadChip (Illumina), which covers more than 850,000 genome-wide methylation sites, was used for genome-wide methylation analysis. The DNA methylation profiles of each sample were visualized at the single-CpG level and for the genomic regions of interest.
Project description:Chemical communication is crucial in ecosystems with complex microbial assemblages. However, due to archaeal cultivation challenges, our understanding of the structure diversity and function of secondary metabolites (SMs) within archaeal communities is limited compared to the extensively studied and well-documented bacterial counterparts. Our comprehensive investigation into the biosynthetic potential of archaea, combined with metabolic analyses and the first report of heterologous expression in archaea, has unveiled the previously unexplored biosynthetic capabilities and chemical diversity of archaeal ribosomally synthesized and post-translationally modified peptide (RiPP). We have identified twenty-four new lanthipeptides of RiPPs exhibiting unique chemical characteristics, including a novel subfamily featuring an unexplored type with diamino-dicarboxylic (DADC) termini, largely expanding the chemical landscape of archaeal SMs. This sheds light on the chemical novelty of archaeal metabolites and emphasizes their potential as an untapped resource for natural product discovery. Additionally, archaeal lanthipeptides demonstrate specific antagonistic activity against haloarchaea, mediating the unique biotic interaction in the halophilic niche. Furthermore, they showcased a unique ecological role in enhancing the host's motility by inducing the rod-shaped cell morphology and upregulating the archaellum gene flgA1, facilitating the archaeal interaction with abiotic environments. These discoveries broaden our understanding of archaeal chemical language and provide promising prospects for future exploration of SM-mediated interaction.
Project description:Traditional protein engineering methods, such as directed evolution, while effective, are often slow and labor-intensive. Advances in machine learning and automated biofoundry present new opportunities for optimizing these processes. This study devises a protein language model-enabled automatic evolution platform, a closed-loop system for automated protein engineering within the Design-Build-Test-Learn cycle. The protein language model ESM-2 makes zero-shot prediction of 96 variants to initiate the cycle. The biofoundry constructs and evaluates these variants, and feeds the results back to a multi-layer perceptron to train a fitness predictor, which then makes prediction of second round of 96 variants with improved fitness. With the tRNA synthetase as a model enzyme, four-rounds of evolution carried out within 10 days lead to mutants with enzyme activity improved by up to 2.4-fold. Our system significantly enhances the speed and accuracy of protein evolution, driving faster advancements in protein engineering for industrial applications.
Project description:Current base editors use DNA deaminases, including cytidine deaminase in cytidine base editor (CBE) or adenine deaminase in adenine base editor (ABE), to facilitate transition nucleotide substitutions. Combining CBE or ABE with glycosylase enzymes can induce limited transversion mutations. Nonetheless, a critical demand remains for base editors capable of generating alternative mutation types, such as T>G corrections. In this study, we leveraged pre-trained protein language models to optimize a uracil-N-glycosylase (UNG) variant with altered specificity for thymines (eTDG). Notably, after two rounds of testing fewer than 50 top-ranking variants, more than 50% exhibited over 1.5-fold enhancement in enzymatic activities. When eTDG was fused with nCas9, it induced programmable T-to-S (G/C) substitutions and corrected db/db diabetic mutation in mice (up to 55%). Our findings not only establish orthogonal strategies for developing novel base editors, but also demonstrate the capacities of protein language models for optimizing enzymes without extensive task-specific training data.