Intra-Individual Variations in How Insulin Sensitivity Responds to Long-Term Exercise: Predictions by Machine Learning Based on Large-Scale Serum Proteomics.

Physical activity is effective for preventing and treating type 2 diabetes, but some individuals do not achieve metabolic benefits from exercise ("non-responders"). We investigated non-responders in terms of insulin sensitivity changes following a 12-week supervised strength and endurance exercise program. We used a hyperinsulinaemic euglycaemic clamp to measure insulin sensitivity among 26 men aged 40-65, categorizing them into non-responders or responders based on their insulin sensitivity change scores. The exercise regimen included VO2max, muscle strength, whole-body MRI scans, muscle and fat biopsies, and serum samples. mRNA sequencing was performed on biopsies and Olink proteomics on serum samples. Non-responders showed more visceral and intramuscular fat and signs of dyslipidaemia and low-grade inflammation at baseline and did not improve in insulin sensitivity following exercise, although they showed gains in VO2max and muscle strength. Impaired IL6-JAK-STAT3 signalling in non-responders was suggested by serum proteomics analysis, and a baseline serum proteomic machine learning (ML) algorithm predicted insulin sensitivity responses with high accuracy, validated across two independent exercise cohorts. The ML model identified 30 serum proteins that could forecast exercise-induced insulin sensitivity changes.

Transcriptome analysis of primary adult B-cell lineage acute lymphoblastic leukemia identifies pathogenic variants and gene fusions, and predicts subtypes for in depth molecular diagnosis.

BACKGROUND: B-cell acute lymphoblastic leukemia (B-ALL) is classified into subgroups based on known driver oncogenes and molecular lesions, including translocations and recurrent mutations. However, the current diagnostic tests do not identify subtypes or oncogenic lesions for all B-ALL samples, creating a heterogeneous B-ALL group of unknown subtypes. METHODS: We sorted primary adult B-ALL cells and performed transcriptome analysis by bulk RNA sequencing (RNA-seq). RESULTS: Transcriptomic analysis of an adult B-ALL cohort allowed the classification of four patient samples with subtypes that were not previously revealed by standard gene panels. The leukemia of two patients were of the DUX4 subtype and two were CRLF2+ Ph-like B-ALL. Furthermore, single nucleotide variant analysis detected the oncogenic NRAS-G12D, KRAS-G12D, and KRAS-G13D mutations in three of the patient samples, presenting targetable mutations. Additional oncogenic variants and gene fusions were uncovered, as well as multiple variants in the PDE4DIP gene across five of the patient samples. CONCLUSION: We demonstrate that RNA-seq is an effective tool for precision medicine in B-ALL by providing comprehensive molecular profiling of leukemia cells, identifying subtype and oncogenic lesions, and stratifying patients for appropriate therapy.