Biology subjects

Graim, K.

Publications and source records attributed to Graim, K..

3 recordsLinked to original sources

bioRxiv2026

The Paipu framework enables creation of a large-scale mammalian cancer transcriptomics atlas

A plethora of studies have identified shared molecular mechanisms involved in tumor development across humans and other mammalian species. While these two-species analyses advance understanding of human disease, extending them across many species would provide evolutionary insight into molecular mechanisms driving human cancers. However, this expansion requires knowledge transfer and harmonization across species. Genomic differences between species, including variation in genome annotation quality, have historically hindered multi-species large-scale atlas creation. To overcome these challenges, we present Paipu, a comprehensive pipeline designed to streamline querying, preprocessing, harmonization, and retrieval of large-scale RNA-seq data and associated metadata from the NCBI Sequence Read Archive (SRA). Paipu facilitates multi-species analysis by creating a harmonized atlas from user-defined search terms and species. It consists of three components: reference genome preparation, SRA metadata retrieval, and RNA-seq data processing. We apply Paipu to 188 cancer-related terms in 239 non-human mammalian species, creating a harmonized atlas of 3,484 RNA-seq samples spanning 17 species and 35 cancers. This pan-mammalian pan-cancer atlas enables myriad comparative genomics analyses that leverage genetic variation to better understand rare human cancers. As such, Paipu serves as a resource for cross-species cancer genomics and supports atlas creation for any set of species and search terms. Graphical Abstract

bioinformatics↗

bioRxiv2026

Deciphering sepsis molecular subtypes using large-scale data to identify subtype-specific drug repurposing

Sepsis is a life-threatening dysregulated response to infection, the heterogeneity of which precludes effective targeted therapies. To address this, we created a transcriptomic atlas of publicly available adult sepsis data, on which we performed molecular subtyping and identified potential subtype-specific drug repurposing opportunities. In total, we harmonized data from 3,713 samples across 28 datasets, of which 2,251 were from sepsis patients. Using this data, we identified four molecular subtypes of sepsis (C1 - C4) by clustering the sepsis samples based on expression differences in immune-and lipid-related genes. We next identified gene signatures unique to each molecular subtype. Pathway analysis of these signatures revealed patterns of immune exhaustion and metabolic dysregulation in C1, suggesting potential benefit from corticosteroid treatment. C2 had the youngest patient population and the lowest mortality, and C2 expression patterns were often anti-correlated with those of C1. C3 was enriched for inflammatory and cellular stress pathways, while the highest mortality subtype, C4, showed evidence of immunosuppression and metabolic reprogramming. Gene and pathway-level analysis of our molecular subtypes statistically correlated with results from analysis of 28-day mortality, with the best (C2) and worst subtypes (C4) exhibiting similar molecular dysregulation as survivors and non-survivors, respectively. For each subtype, we then evaluated potential targeted therapies. Using a large-scale pharmacogenomics database, we identified drugs targeting the subtype gene signatures and assessed the potential clinical impacts of these drugs. We identified several potential candidate therapies for each molecular subtype, including possible responsiveness to Methylene Blue therapy for patients from our highest mortality subtype, C4. Notably, our drug repurposing analysis revealed a significant representation of anti-inflammatory monoclonal antibody therapies across molecular subtypes. The anti-correlated signatures in C1 and C2 suggest that monoclonal antibody therapies may not be effective for patients in both subtypes, which may explain why prior clinical trials have been unsuccessful. Altogether, our detailed molecular subtyping and analysis identify potential drug targets within each molecular subtype, with implications for future precision medicine for sepsis.

bioinformatics↗

bioRxiv2023

Is a Picture Worth 1,000 SNPs? Effects of User-Submitted Photographs on Ancestry Estimates from Direct-to-Consumer Canine Genetic Tests

ObjectiveTo evaluate whether the breed ancestry predictions of direct-to-consumer (DTC) genetic tests for dogs are influenced by the user-provided photograph. AnimalsTwelve pet dogs considered purebred (i.e., registered with a breed organization) representing twelve different breeds. MethodsSix buccal swabs per dog were collected by the owners and submitted to six DTC genetic testing companies. The experimenters registered each sample with the company. For half of the dogs, the registration included a photograph of the DNA donor. For the other half of the dogs, photographs were swapped between dogs. Analysis of the DNA and breed ancestry prediction was conducted by each company. Each companys breed predictions were evaluated to assess whether the condition (i.e., matching versus shuffled photograph) affected the odds of identifying the DNA donors registered breed. A convolutional neural network was also used to predict breed based solely on the photograph as a positive control. ResultsFive of the six tests always produced results that included the registered breed. One test and the convolutional neural network were unlikely to identify the registered breed and frequently returned results that included the breed in the photograph. This result suggests that one test on the market is relying on the photograph more than the DNA sample. Additionally, differences in the predictions made across all tests underscore the challenge of identifying breed ancestry, even in purebred dogs. Clinical RelevanceVeterinarians are likely to encounter patients who have conducted DTC genetic testing and may find themselves in the position of explaining genetic test results that they did not order. This systematic comparison of tests on the market provides context for interpreting unexpected results from consumer-grade DTC genetic testing kits.

genetics↗