Andreas Gnirke

DNA methylation is a fundamental epigenetic modification that regulates gene expression, maintains genomic integrity and defines cellular identity. Conventional bulk sequencing methods, such as reduced-representation bisulfite sequencing (RRBS), have elucidated genome-wide methylation landscapes but do not reveal cell-to-cell variation, limiting insights into methylation heterogeneity during development, differentiation and disease. Here, we present multiplexed single-cell RRBS (Msc-RRBS), a streamlined and scalable protocol for parallel DNA methylation profiling of hundreds of individual cells. All enzymatic reactions, including cell lysis, restriction digestion, end repair and adapter ligation, are carried out in a single microwell, which eliminates the need for buffer exchange and minimizes DNA loss. Short, indexed adapters are used to efficiently remove adapter dimers and enable effective purification of library DNA by magnetic bead clean-up. The workflow supports the generation of sequencing libraries from single cells, pooled cells or sub-nanogram amounts of purified genomic DNA. The full workflow, from cell sorting to sequencing-ready libraries and data analysis, requires ~6 d for one or more 96-well plates. Msc-RRBS thus provides an efficient, reproducible and high-throughput approach for dissecting cell-to-cell DNA methylation heterogeneity across diverse biological and disease contexts. The experiments require basic molecular biology skills, and primary data processing can be performed by someone with limited bioinformatics training. More advanced analyses need additional computational expertise.

Highly Pathogenic Avian Influenza strain H5N1 has caused a multi-state outbreak among US dairy cattle, spreading across 15 states and infecting hundreds of herds since its onset. We rapidly developed and optimized PCR-based detection assays and sequencing protocols to support H5N1 molecular surveillance. Using 214 retail milk from 20 states for methods development, we found that H5N1 concentrations by digital PCR strongly correlated with qPCR cycle threshold (Ct) values, with dPCR exhibiting greater sensitivity. We also found that metagenomic sequencing after hybrid selection was best for higher concentration samples while amplicon sequencing performs best for lower concentrations. By establishing these methods, we were able to support the creation of a statewide surveillance program to test bulk milk samples monthly from all cattle dairy farms within Massachusetts, which remain negative to date. The methods, workflow, and recommendations described here provide a framework for others aiming to conduct H5N1 surveillance efforts.

Low-abundance members of microbial communities are difficult to study in their native habitats, including Escherichia coli, a minor but common inhabitant of the gastrointestinal tract, and key opportunistic pathogen of the urinary tract. While multi-omic analyses have detailed interactions between uropathogenic Escherichia coli (UPEC) and the bladder mediating urinary tract infection (UTI), little is known about UPEC in its pre-infection reservoir, the gastrointestinal tract, partly due to its low relative abundance (E. coli, we develop E. coli PanSelect, which uses probes designed to specifically capture E. coli’s broad pangenome. We demonstrate its ability to enrich diverse E. coli by orders of magnitude, in a mock community and in human stool from a study investigating recurrent UTI (rUTI). Comparisons of transcriptomes between gut E. coli of women with and without history of rUTI suggest rUTI gut E. coli are responding to increased oxygen and nitrate, suggestive of mucosal inflammation, which may have implications for recurrent disease. E. coli PanSelect is well suited for investigations of in vivo E. coli biology in other low-abundance environments, and the framework described here has broad applicability to other diverse, low-abundance organisms.

In exploring the evolutionary trajectories of both pathogenesis and karyotype dynamics in fungi, we conducted a large-scale comparative genomic analysis spanning the Cryptococcus genus, encompassing both global human fungal pathogens and nonpathogenic species, and related species from the sister genus Kwoniella. Chromosome-level genome assemblies were generated for multiple species, covering virtually all known diversity within these genera. Although Cryptococcus and Kwoniella have comparable genome sizes (about 19.2 and 22.9 Mb) and similar gene content, hinting at preadaptive pathogenic potential, our analysis found evidence of gene gain (via horizontal gene transfer) and gene loss in pathogenic Cryptococcus species, which might represent evolutionary signatures of pathogenic development. Genome analysis also revealed a significant variation in chromosome number and structure between the 2 genera. By combining synteny analysis and experimental centromere validation, we found that most Cryptococcus species have 14 chromosomes, whereas most Kwoniella species have fewer (11, 8, 5, or even as few as 3). Reduced chromosome number in Kwoniella is associated with formation of giant chromosomes (up to 18 Mb) through repeated chromosome fusion events, each marked by a pericentric inversion and centromere loss. While similar chromosome inversion–fusion patterns were observed in all Kwoniella species with fewer than 14 chromosomes, no such pattern was detected in Cryptococcus. Instead, Cryptococcus species with less than 14 chromosomes showed reductions primarily through rearrangements associated with the loss of repeat-rich centromeres. Additionally, Cryptococcus genomes exhibited frequent interchromosomal translocations, including intercentromeric recombination facilitated by transposons shared between centromeres. Overall, our findings advance our understanding of genetic changes possibly associated with pathogenicity in Cryptococcus and provide a foundation to elucidate mechanisms of centromere loss and chromosome fusion driving distinct karyotypes in closely related fungal species, including prominent global human pathogens.

Low-abundance members of microbial communities are difficult to study in their native habitats. This includes Escherichia coli , a minor, but common inhabitant of the gastrointestinal tract and opportunistic pathogen, including of the urinary tract, where it is the primary pathogen. While multi-omic analyses have detailed critical interactions between uropathogenic Escherichia coli (UPEC) and the bladder that mediate UTI outcome, comparatively little is known about UPEC in its pre-infection reservoir, partly due to its low abundance there (E. coli in gastrointestinal communities, we developed E. coli PanSelect which uses a set of probes designed to specifically recognize and capture E. coli ’s broad pangenome from sequencing libraries. We demonstrated the ability of E. coli PanSelect to enrich, by orders of magnitude, sequencing data from diverse E. coli using a mock community and a set of human stool samples collected as part of a cohort study investigating drivers of recurrent urinary tract infections (rUTI). Comparisons of genomes and transcriptomes between E. coli residing in the gastrointestinal tracts of women with and without a history of rUTI suggest that rUTI gut E. coli are responding to increased levels of oxygen and nitrate, suggestive of mucosal inflammation, which may have implications for recurrent disease. E. coli PanSelect is well suited for investigations of native in vivo biology of E. coli in other environments where it is at low relative abundance, and the framework described here has broad applicability to other highly diverse, low abundance organisms.

Epigenetic alterations, such as promoter hypermethylation, may drive cancer through tumor suppressor gene inactivation. However, we have limited ability to differentiate driver DNA methylation (DNAme) changes from passenger events. We developed DNAme driver inference–MethSig–accounting for the varying stochastic hypermethylation rate across the genome and between samples. We applied MethSig to bisulfite sequencing data of chronic lymphocytic leukemia (CLL), multiple myeloma, ductal carcinoma in situ, glioblastoma, and to methylation array data across 18 tumor types in TCGA. MethSig resulted in well-calibrated quantile–quantile plots and reproducible inference of likely DNAme drivers with increased sensitivity/specificity compared with benchmarked methods. CRISPR/Cas9 knockout of selected candidate CLL DNAme drivers provided a fitness advantage with and without therapeutic intervention. Notably, DNAme driver risk score was closely associated with adverse outcome in independent CLL cohorts. Collectively, MethSig represents a novel inference framework for DNAme driver discovery to chart the role of aberrant DNAme in cancer.Significance:MethSig provides a novel statistical framework for the analysis of DNA methylation changes in cancer, to specifically identify candidate DNA methylation driver genes of cancer progression and relapse, empowering the discovery of epigenetic mechanisms that enhance cancer cell fitness.This article is highlighted in the In This Issue feature, p. 2113

Description of analyzed TCGA data.