Biology subjects

Preska Steinberg, A.

Publications and source records attributed to Preska Steinberg, A..

6 recordsLinked to original sources

bioRxiv2026

Recurrent non-canonical proteoforms in acute myeloid leukemia identified by integrative proteogenomics

Reference proteomes incompletely represent proteins translated in cancer, leaving tumor-specific proteoforms outside the search space of conventional mass spectrometry (MS). Such "dark proteome" products may arise from genomic variation, aberrant transcription or splicing, and non-canonical translation, including microproteins encoded by small open reading frames (ORFs). To define this landscape in acute myeloid leukemia (AML), we developed a cohort-informed proteogenomic strategy using paired RNA-sequencing and MS analysis of 123 human patient AML specimens and 13 healthy CD34+ controls. ProteomeGenerator2 was used for de novo transcriptome assembly and ORF prediction, and candidate cancer-specific unannotated sequences were prioritized by unique high-quality mass spectral support, absence from CD34+ controls, recurrence across individual AML patients, and lack of close homology to annotated proteins. We identified 5,849 Swiss-Prot-unannotated proteoforms, including 1,987 without homology to annotated human proteins. Thirty-nine candidates, most encoding microproteins, were recurrently detected in more than 10% of patients, and 14 were independently validated by deep, fractionated, multi-protease data-independent acquisition (DIA) proteomics of human AML cell lines. Structural modeling predicted several functional classes, including intrinsically disordered, alpha-helical microproteins, and membrane- or secretory-pathway-associated proteoforms. These findings define a recurrent AML dark proteome and establish a framework for the discovery of tumor-specific non-canonical proteins for mechanistic and therapeutic studies.

cancer biology↗

bioRxiv2026

Evolution of oncogene amplification across 86,000 cancer cell genomes

High-level copy-number (CN) amplification (HLAMP) is a major mechanism of oncogene activation in human cancer. Despite progress in therapeutically targeting amplified oncogenes, the processes underlying amplicon evolution remain incompletely understood, leaving critical knowledge gaps in their etiology and mechanisms of therapeutic response. To address this, we analyzed the evolutionary trajectories of HLAMPs using single-cell whole-genome sequencing data from 86,239 cancer cells across 93 patients and 9 experimental systems. We found that cell-to-cell CN variability provides a quantifiable readout of HLAMP mechanism, clearly distinguishing extrachromosomal circular DNA (ecDNA) from intrachromosomal amplification (ICamp) through characteristic CN distributions that reflect distinct modes of segregation and correspond to clonal architecture. Notably, ICamp events frequently showed multiple amplitude peaks specific to subclones, indicating punctuated shifts in oncogene dosage through numeric or structural modulatory mechanisms with transcriptional impact. In contrast, ecDNAs exhibited broad, continuous CN distribution with extreme high-copy outliers, consistent with asymmetric segregation. The CN and structural diversity of ecDNA regions enabled systematic deconvolution of ecDNA subspecies and estimation of their per-cell abundance, revealing the history of ecDNA-mediated oncogenesis at single-nucleotide resolution. We observed ecDNA diversification through internal rearrangements across cases and, notably, convergent evolution in glioblastoma cases marked by multiple, recurrent acquisition of EGFR-targeting ecDNAs. Finally, single-cell genome-based identification of ecDNAs showed substantial discrepancy with bulk genome graph-based predictions and reliably distinguished actively maintained ecDNAs from historical genomic footprints after chromosomal re-integration. These findings reveal marked tissue-type specificity of ecDNAs, suggesting that ecDNA-mediated oncogenesis may depend on a permissive tissue context.

genomics↗

bioRxiv2025

End-to-end proteogenomics for discovery of cryptic and non-canonical cancer proteoforms using long-read transcriptomics and multi-dimensional proteomics

Modern mass spectrometry now permits genome-scale quantitative measurements of biological proteomes. However, current approaches remain hindered by incomplete representation of biological variability of protein sequences in canonical reference proteomes, limiting studies to already annotated proteoforms. This, combined with reliance on tryptic peptides and limited sample complexity reduction techniques render detection of variant proteoforms missing from current reference annotations virtually impossible. Here, we report a method for end-to-end proteogenomics which integrates (i) sample-specific transcriptome assembly using long-read sequencing with (ii) multi-protease, multi-dimensional (MPMD) trapped ion mobility spectrometry (TIMS) parallel accumulation-serial fragmentation (PASEF) mass spectrometry. Termed ProteomeGenerator3, we benchmark its sensitivity and specificity for unbiased human cancer proteome discovery. Using long-read transcriptome sequencing of human Ewing sarcoma cancer cells, we detected over 40,000 full-length transcript isoforms, including aberrantly expressed neogenes, splice isoforms, and gene fusions. Combined with MPMD-TIMS-PASEF mass spectrometry, our proteogenomics workflow enabled the detection of 12,189 unique proteoforms with an average sequence coverage exceeding 50%, including 3,216 putative, non-canonical proteoforms, constituting one of the most complete human proteomes to date. Likewise, we report the proteogenomic performance metrics for current state-of-the-art implementations of DIA-NN, Spectronaut and diaTracer-MSFragger algorithms for data-independent acquisition (DIA) and PASEF mass spectral data, including optimal Double Rainbow-diaPASEF parameters for non-tryptic proteomes. In all, end-to-end integrative ProteomeGenerator3 provides a versatile platform for deep sample-specific proteogenomic discovery, suitable for the identification of disease biomarkers and therapeutic targets, and is available open-source from https://github.com/kentsislab/proteomegenerator3.

bioinformatics↗

bioRxiv2022

Correlated substitutions reveal SARS-like coronaviruses recombine frequently with a diverse set of structured gene pools

Quantifying SARS-like coronavirus (SL-CoV) evolution is critical to understanding the origins of SARS-CoV-2 and the molecular processes that could underlie future epidemic viruses. While genomic evidence implicates recombination as a factor in the emergence of SARS-CoV-2, few studies have quantified recombination rates among SL-CoVs. Here, we infer recombination rates of SL-CoVs from correlated substitutions in sequencing data using a coalescent model with recombination. Our computationally-efficient, non-phylogenetic method infers recombination parameters of both sampled sequences and the unsampled gene pools with which they recombine. We apply this approach to infer recombination parameters for a range of positive-sense RNA viruses. We then analyze a set of 191 SL-CoV sequences (including SARS-CoV-2) and find that ORF1ab and S genes frequently undergo recombination. We identify which SL-CoV sequence clusters have recombined with shared gene pools, and show that these pools have distinct structures and high recombination rates, with multiple recombination events occurring per synonymous substitution. We find that individual genes have recombined with different viral reservoirs. By decoupling contributions from mutation and recombination, we recover the phylogeny of non-recombined portions for many of these SL-CoVs, including the position of SARS-CoV-2 in this clonal phylogeny. Lastly, by analyzing 444,145 SARS-CoV-2 whole genome sequences, we show current diversity levels are insufficient to infer the within-population recombination rate of the virus since the pandemic began. Our work offers new methods for inferring recombination rates in RNA viruses with implications for understanding recombination in SARS-CoV-2 evolution and the structure of clonal relationships and gene pools shaping its origins. Significance StatementQuantifying the population genetics of SARS-like coronavirus (SL-CoV) evolution is vital to deciphering the origins of SARS-CoV-2 and pinpointing viruses with epidemic potential. While some Bayesian approaches can quantify recombination for these pathogens, the required simulations of recombination networks do not scale well with the massive amounts of sequences available in the genomics era. Our approach circumvents this by measuring correlated substitutions in sequences and fitting these data to a coalescent model with recombination. This allows us to analyze hundreds of thousands of sample sequences, and infer recombination rates for unsampled viral reservoirs. Our results provide insights into both the clonal relationships of sampled SL-CoV sequence clusters and the evolutionary dynamics of the gene pools with which they recombine.

evolutionary biology↗

bioRxiv2021

Core genes can have higher recombination rates than accessory genes within global microbial populations

Recombination is essential to microbial evolution1-4, and is involved in the spread of antibiotic resistance, antigenic variation, and adaptation to the host niche5-8. Yet quantifying the impact of homologous recombination on different gene classes, which is critical to understanding how selection acts on variation to shape species diversity and genome structure, remains challenging. This is largely due to the dynamic nature of bacterial genomes, whose high intraspecies genome content diversity9 and complex phylogenetic relationships10-12 present difficulties for inferring rates of recombination, particularly for rare genes. In this work, we apply a computationally efficient, non-phylogenetic approach13,14 to measure homologous recombination rates in the core and accessory genome (genes present in all strains and only a subset of strains, respectively) using >100,000 whole genome sequences from 12 microbial species. Our analysis suggests that even well-resolved sequence clusters sampled from global populations interact with overlapping gene pools, which has implications for the role of population structure in genome evolution. We show that in a majority of species, core genes have shorter coalescence times and higher recombination rates than accessory genes, and that gene frequency is often positively correlated with increased recombination. Our results provide a new line of population genomic evidence supporting the hypothesis that core genes are under strong, purifying selection15-17, and indicate that homologous recombination may play a key role in increasing the efficiency of selection in those parts of the genome most conserved within each species.

microbiology↗

bioRxiv2018

High-molecular-weight polymers from dietary fiber drive aggregation of particulates in the murine small intestine

The lumen of the small intestine (SI) is filled with particulates: microbes, therapeutic particles, and food granules. The structure of this particulate suspension could impact uptake of drugs and nutrients and the function of microorganisms; however, little is understood about how this suspension is re-structured as it transits the gut. Here, we demonstrate that particles spontaneously aggregate in SI luminal fluid ex vivo. We find that mucins and immunoglobulins are not required for aggregation. Instead, aggregation can be controlled using polymers from dietary fiber in a manner that is qualitatively consistent with polymer-induced depletion interactions, which do not require specific chemical interactions. Furthermore, we find that aggregation is tunable; by feeding mice dietary fibers of different molecular weights, we can control aggregation in SI luminal fluid. This work suggests that the molecular weight and concentration of dietary polymers play an underappreciated role in shaping the physicochemical environment of the gut.

biophysics↗