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Piernik, M.

Publications and source records attributed to Piernik, M..

2 recordsLinked to original sources

Large-scale transcriptomic meta-analysis identifies novel therapeutic targets for ulcerative colitis

Ulcerative colitis (UC) is a chronic inflammatory bowel disease for which the vast majority of the approved therapies target the immune system. We aimed to identify consistently dysregulated genes and pathways across independent UC transcriptomic cohorts, distinguishing constitutive from inflammation-dependent changes. We performed a random-effects meta-analysis of 14 microarray datasets from the Gene Expression Omnibus (972 mucosal biopsies, 9 platforms), comparing inflamed UC, uninflamed UC, and inflamed Crohns disease (CD) to controls, as well as UC to CD directly. The inflamed UC analysis revealed an upregulated inflammatory transcriptomic profile in UC, providing a rationale for the use of all approved anti-inflammatory therapies. In parallel, the predominant downregulated signal was metabolic, driven by PPARGC1A, PPARGC1B, and ES-RRA, indicating a coordinated, inflammation-dependent collapse. The uninflamed UC analysis revealed a separate set of potentially constitutive vulnerabilities -- fully suppressed glucuronidation, upregulated translation, complement priming, and altered iron export -- that are not downstream of the energy collapse. The metabolic deficit was more severe in UC than in CD, while immune pathways were shared. These findings suggest a two-layer model of UC pathology: a constitutive impairment of metabolic pathways that is further exacerbated by inflammation. The inflammation analysis reveals new targets for immune suppression while the constitutive analysis identifies targets for proactive intervention between flares directed at the metabolic deficiency.

genomics↗

DBFE: Distribution-based feature extraction from copy number and structural variants in whole-genome data

MotivationWhole-genome sequencing has revolutionized biosciences by providing tools for constructing complete DNA sequences of individuals. With entire genomes at hand, scientists can pinpoint DNA fragments responsible for different cancers and predict patient responses to cancer treatments. However, the sheer volume of whole-genome data makes it difficult to encode the characteristics of genomic variants as features for machine learning algorithms. ResultsWe present three feature extraction methods that facilitate classifier learning from distributions of genomic variants. The proposed approaches use binning, clustering, and kernel density estimation to produce features that discriminate between two groups of patients. Experiments on genomes of 219 ovarian, 61 lung, and 929 breast cancer patients show that the proposed approaches automatically identify genomic biomarkers associated with cancer subtypes and clinical response to oncological treatment. Finally, we show that the extracted features can be used alongside unsupervised learning methods to analyze genomic samples. AvailabilityThe source code of the presented algorithms and reproducible experimental scripts are available on Github at https://github.com/MNMdiagnostics/dbfe Contactmaciej.piernik@cs.put.poznan.pl

genomics↗