A top variant identification pipeline for protein engineering.

Understanding the fitness of protein variants with combinatorial mutations is critical for effective protein engineering. In this issue of Cell Systems, Chu et al. present TopVIP, a top variant identification pipeline that enables accurate picking of the greatest number of best-performing protein variants with high-fitness leveraging zero-shot predictor and low-N iterative sampling.

Role of GDF-15 in diabetic nephropathy: mechanisms, diagnosis, and therapeutic potential.

PURPOSE: Growth differentiation factor 15 (GDF-15) is a cytokine involved in regulating homeostasis, and its expression is up-regulated in response to injury, stress, and inflammation. This study explored the role of GDF-15 in diabetic nephropathy (DN), a severe complication of diabetes mellitus, and its potential as a biomarker for disease progression. METHODS: As a member of the transforming growth factor-ß superfamily, GDF-15 exhibits its renal protective functions primarily through its anti-inflammatory effects and the up-regulation of other renal protective factors. This study evaluated the association between circulating GDF-15 levels and DN progression, examining the underlying mechanisms. RESULTS: Circulating GDF-15 levels are closely linked to the development and progression of DN. While existing research has yielded some consistent conclusions, a comprehensive understanding of the role of GDF-15 in DN pathogenesis is needed to identify new therapeutic targets and strategies. CONCLUSION: GDF-15 has the potential to be a prognostic and diagnostic biomarker for DN. It is crucial to establish appropriate reference ranges and explore their clinical utility in routine practice for validating the role of GDF-15 in DN management. Further interventional studies are required to confirm its clinical value in diagnosing and predicting the progression of DN.

The Transpeptidase Sortase A Binds Nucleic Acids and Mediates Mammalian Cell Labeling.

Wild-type sortase A is an important virulence factor displaying a diverse array of proteins on the surface of bacteria. This protein display relies on the transpeptidase activity of sortase A, which is widely engineered to allow protein ligation and protein engineering based on the interaction between sortase A and peptides. Here an unknown interaction is found between sortase A from Staphylococcus aureus and nucleic acids, in which exogenously expressed engineered sortase A binds oligonucleotides in vitro and is independent of its canonical transpeptidase activity. When incubated with mammalian cells, engineered sortase A further mediates oligonucleotide labeling to the cell surface, where sortase A attaches itself and is part of the labeled moiety. The labeling reaction can also be mediated by many classes of wild-type sortases as well. Cell surface GAG appears involved in sortase-mediated oligonucleotide cell labeling, as demonstrated by CRISPR screening. This interaction property is utilized to develop a technique called CellID to facilitate sample multiplexing for scRNA-seq and shows the potential of using sortases to label cells with diverse oligonucleotides. Together, the binding between sortase A and nucleic acids opens a new avenue to understanding the virulence of wild-type sortases and exploring the application of sortases in biotechnology.

Machine learning-based prediction models to guide the selection of Cas9 variants for efficient gene editing.

The increasing emergence of Cas9 variants has attracted broad interest, as these variants were designed to expand CRISPR applications. New Cas9 variants typically feature higher editing efficiency, improved editing specificity, or alternative PAM sequences. To select Cas9 variants and gRNAs for high-fidelity and efficient genome editing, it is crucial to systematically quantify the editing performances of gRNAs and develop prediction models based on high-quality datasets. Using synthetic gRNA-target paired libraries and next-generation sequencing, we compared the activity and specificity of gRNAs of four SpCas9 variants. The nucleotide composition in the PAM-distal region had more influence on the editing efficiency of HiFi Cas9 and LZ3 Cas9. We further developed machine learning models to predict the gRNA efficiency and specificity for the four Cas9 variants. To aid users from broad research areas, the machine learning models for the predictions of gRNA editing efficiency within human genome sites are available on our website.