Multiplexing mutation rate assessment: determining pathogenicity of Msh2 variants in Saccharomyces cerevisiae

A series of reviews in familial cancer: genetic cancer risk in context variants of uncertain significance in MMR genes: which procedures should be followed?

Interpreting variants of uncertain significance (VUS) in mismatch repair (MMR) genes remains a major challenge in managing Lynch syndrome and other hereditary cancer syndromes. This review outlines recommended VUS classification procedures, encompassing foundational and specialized methodologies tailored for MMR genes by expert organizations, including InSiGHT and ClinGen's Hereditary Colorectal Cancer/Polyposis Variant Curation Expert Panel (VCEP). Key approaches include: (1) functional data, encompassing direct assays measuring MMR proficiency such as in vitro MMR assays, deep mutational scanning, and MMR cell-based assays, as well as techniques like methylation-tolerant assays, proteomic-based approaches, and RNA sequencing, all of which provide critical functional evidence supporting variant pathogenicity; (2) computational data/tools, including in silico meta-predictors and models, which contribute to robust VUS classification when integrated with experimental evidence; and (3) enhanced variant detection to identify the actual causal variant through whole-genome sequencing and long-read sequencing to detect pathogenic variants missed by traditional methods. These strategies improve diagnostic precision, support clinical decision-making for Lynch syndrome, and establish a flexible framework that can be applied to other OMIM-listed genes.

The rate of spontaneous mutations in yeast deficient for MutSβ function

Mutations in simple sequence repeat loci underlie many inherited disorders in humans, and are increasingly recognized as important determinants of natural phenotypic variation. In eukaryotes, mutations in these sequences are primarily repaired by the MutSβ mismatch repair complex. To better understand the role of this complex in mismatch repair and the determinants of simple sequence repeat mutation predisposition, we performed mutation accumulation in yeast strains with abrogated MutSβ function. We demonstrate that mutations in simple sequence repeat loci in the absence of mismatch repair are primarily deletions. We also show that mutations accumulate at drastically different rates in short (<8 bp) and longer repeat loci. These data lend support to a model in which the mismatch repair complex is responsible for repair primarily in longer simple sequence repeats.

Lynch syndrome, molecular mechanisms and variant classification

Patients with the heritable cancer disease, Lynch syndrome, carry germline variants in the MLH1, MSH2, MSH6 and PMS2 genes, encoding the central components of the DNA mismatch repair system. Loss-of-function variants disrupt the DNA mismatch repair system and give rise to a detrimental increase in the cellular mutational burden and cancer development. The treatment prospects for Lynch syndrome rely heavily on early diagnosis; however, accurate diagnosis is inextricably linked to correct clinical interpretation of individual variants. Protein variant classification traditionally relies on cumulative information from occurrence in patients, as well as experimental testing of the individual variants. The complexity of variant classification is due to (1) that variants of unknown significance are rare in the population and phenotypic information on the specific variants is missing, and (2) that individual variant testing is challenging, costly and slow. Here, we summarise recent developments in high-throughput technologies and computational prediction tools for the assessment of variants of unknown significance in Lynch syndrome. These approaches may vastly increase the number of interpretable variants and could also provide important mechanistic insights into the disease. These insights may in turn pave the road towards developing personalised treatment approaches for Lynch syndrome.

A universal sequencing read interpreter

Massively parallel DNA sequencing has led to the rapid growth of highly multiplexed experiments in biology. These experiments produce unique sequencing results that require specific analysis pipelines to decode highly structured reads. However, no versatile framework that interprets sequencing reads to extract their encoded information for downstream biological analysis has been developed. Here, we report INTERSTELLAR (interpretation, scalable transformation, and emulation of large-scale sequencing reads) that decodes data values encoded in theoretically any type of sequencing read and translates them into sequencing reads of another structure of choice. We demonstrated that INTERSTELLAR successfully extracted information from a range of short- and long-read sequencing reads and translated those of single-cell (sc)RNA-seq, scATAC-seq, and spatial transcriptomics to be analyzed by different software tools that have been developed for conceptually the same types of experiments. INTERSTELLAR will greatly facilitate the development of sequencing-based experiments and sharing of data analysis pipelines.

RosettaDDGPrediction for high-throughput mutational scans: From stability to binding

Reliable prediction of free energy changes upon amino acid substitutions (ΔΔGs) is crucial to investigate their impact on protein stability and protein-protein interaction. Advances in experimental mutational scans allow high-throughput studies thanks to multiplex techniques. On the other hand, genomics initiatives provide a large amount of data on disease-related variants that can benefit from analyses with structure-based methods. Therefore, the computational field should keep the same pace and provide new tools for fast and accurate high-throughput ΔΔG calculations. In this context, the Rosetta modeling suite implements effective approaches to predict folding/unfolding ΔΔGs in a protein monomer upon amino acid substitutions and calculate the changes in binding free energy in protein complexes. However, their application can be challenging to users without extensive experience with Rosetta. Furthermore, Rosetta protocols for ΔΔG prediction are designed considering one variant at a time, making the setup of high-throughput screenings cumbersome. For these reasons, we devised RosettaDDGPrediction, a customizable Python wrapper designed to run free energy calculations on a set of amino acid substitutions using Rosetta protocols with little intervention from the user. Moreover, RosettaDDGPrediction assists with checking completed runs and aggregates raw data for multiple variants, as well as generates publication-ready graphics. We showed the potential of the tool in four case studies, including variants of uncertain significance in childhood cancer, proteins with known experimental unfolding ΔΔGs values, interactions between target proteins and disordered motifs, and phosphomimetics. RosettaDDGPrediction is available, free of charge and under GNU General Public License v3.0, at https://github.com/ELELAB/RosettaDDGPrediction.

High-throughput approaches to functional characterization of genetic variation in yeast

Expansion of sequencing efforts to include thousands of genomes is providing a fundamental resource for determining the genetic diversity that exists in a population. Now, high-throughput approaches are necessary to begin to understand the role these genotypic changes play in affecting phenotypic variation. Saccharomyces cerevisiae maintains its position as an excellent model system to determine the function of unknown variants with its exceptional genetic diversity, phenotypic diversity, and reliable genetic manipulation tools. Here, we review strategies and techniques developed in yeast that scale classic approaches of assessing variant function. These approaches improve our ability to better map quantitative trait loci at a higher resolution, even for rare variants, and are already providing greater insight into the role that different types of mutations play in phenotypic variation and evolution not just in yeast but across taxa.

PacRAT: a program to improve barcode-variant mapping from PacBio long reads using multiple sequence alignment

SUMMARY

Use of PacBio sequencing for characterizing barcoded libraries of genetic variants is on the rise. However, current approaches in resolving PacBio sequencing artifacts can result in a high number of incorrectly identified or unusable reads. Here, we developed a PacBio Read Alignment Tool (PacRAT) that improves the accuracy of barcode-variant mapping through several steps of read alignment and consensus calling. To quantify the performance of our approach, we simulated PacBio reads from eight variant libraries of various lengths and showed that PacRAT improves the accuracy in pairing barcodes and variants across these libraries. Analysis of real (non-simulated) libraries also showed an increase in the number of reads that can be used for downstream analyses when using PacRAT.

AVAILABILITY AND IMPLEMENTATION

PacRAT is written in Python and is freely available (https://github.com/dunhamlab/PacRAT).

SUPPLEMENTARY INFORMATION

Supplemental data are available at Bioinformatics online.