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Lehtiö, J.

Publications and source records attributed to Lehtiö, J..

7 recordsLinked to original sources

Proteomics of steady-state and culture-derived human erythropoiesis reveals the dynamics of erythroid stress

To generate a mature red blood cell, erythropoiesis requires the committed erythroid progenitor cell and later erythroblast stages to undergo a continuum of extreme morphological and functional remodeling. In vitro culture has been crucial to understand the extensive proteome dynamics that underlie this process. However, the millions of cells required for conventional proteomics technologies preclude the study of erythropoiesis within the steady-state human bone marrow (BM). Thus, culture-induced stress may shape our understanding of erythroid maturation. To address this knowledge gap, we developed an optimized methodology for low-cell proteomics (pauciproteomics) and pursued total proteomic analysis of paired steady-state BM and culture-derived erythroid precursor stages from 11 healthy donors. This approach defines four main protein expression patterns during BM erythroid maturation which are only partially recapitulated by cultured cells. Culture-induced stress rewires the erythroid proteome from early stages of differentiation, reshaping major transcription factor networks, kinase pathways, metabolic enzymes and erythrokine regulation. Notably, single-cell RNA sequencing analysis of steady-state healthy and myelodysplastic syndromes (MDS) BM demonstrates that the cultured cell proteome parallels gene expression dynamics within the MDS BM, modelling the regulation of in vivo stress erythropoiesis. Culture-specific alterations were mostly restricted to extensive carbonic anhydrase dysregulation and embryofetal globin switching, both potentially linked to altered gas exchange dynamics in culture. In conclusion, we reveal the steady-state erythroid proteome and its substantial remodeling by culture-induced stress, reframing existing knowledge on erythroid differentiation, defining new elements of stress erythropoiesis and guiding future studies in the field.

physiology↗

RNA splicing factor mutations drive myeloid neoplasm oncogenesis through protein complex poisoning

RNA splicing factor mutations (SFmut) are founding oncogenic events which cause RNA splicing errors with unpredictable gene expression dynamics. Despite extensive transcriptomic studies, SFmut cancer-initiating mechanisms remain elusive. Among SFmut cancers, myeloid neoplasms (MN) alone offer a setting where true cancer-driving SFmut stem cells can be identified, namely the hematopoietic stem cell (HSC). Within a cohort of 62 MN patients and 20 healthy donors, we conducted long/short-read single-cell transcriptomics (10X-ONT, n = 21) and immunophenotype-resolved low-cell proteomics (pauciproteomics, n = 78) to resolve gene expression, RNA splicing and protein expression dynamics across healthy and SFmut MN hematopoiesis. During hematopoietic differentiation, SFmut RNA mis-splicing decouples proteotranscriptomic dynamics in a mutation-specific manner. Pauciproteomics defines the functional effects of SFmut RNA mis-splicing on the protein layer, identifying early-stage protein effects which poison entire functional protein complexes and progressively disrupt the proteome-wide network during cellular maturation. Importantly, this information could neither be resolved nor predicted by transcriptomic analyses. Finally, we functionally validate the proteome dynamics of HSC through induced pluripotent stem cell culture models of early hematopoietic differentiation, identifying candidate mechanisms for SFmut oncogenesis. Overall, these data elucidate SFmut MN disease biology with unprecedented granularity, defining the molecular consequences of SFmut RNA mis-splicing and offering a generalizable framework for cancer stem cell studies.

cancer biology↗

Proteomic profiling reveals pleiotropic antimetabolite activity of triciribine in acute lymphoblastic leukemia

Acute lymphoblastic leukemia (ALL) exhibits marked genetic and metabolic heterogeneity that limits the efficacy of targeted therapies. Antimetabolite strategies remain central to ALL treatment, yet the mechanisms underlying differential drug sensitivity are incompletely defined. Here, we investigated the activity of the purine analog triciribine (TCN) across diverse ALL cellular models. We find that TCN exerts potent cytotoxic effects in multiple ALL cell lines, exceeding those observed with canonical Akt inhibitors in our datasets. Phosphoproteomic analyses indicate that, at early time points, TCN does not primarily suppress Akt signaling but instead induces transient pathway activation accompanied by inhibition of cyclin-dependent kinases in both sensitive and resistant cells. The monophosphorylated metabolite TCN-P represents the predominant intracellular species and requires adenosine kinase (ADK) for activity, with ADK protein levels positively correlating with TCN sensitivity in both cell lines and primary patient samples. Using Proteome Integral Solubility Alteration profiling, we identify candidate protein interactions of TCN-P distributed across multiple cellular pathways, including nucleotide metabolism, DNA replication, and protein synthesis. These interactions are accompanied by impaired purine biosynthesis, DNA damage, translational stress, and cell-cycle arrest, as supported by time-course quantitative proteomics and immunoblot analyses. Together, these findings characterize triciribine as an antileukemic agent with pleiotropic antimetabolite activity in ALL and highlight ADK-dependent metabolism as a key determinant of therapeutic sensitivity, suggesting that ADK levels may serve as a predictive biomarker to stratify patients for triciribine-based precision treatment strategies.

cancer biology↗

Mass spectrometry-based proteomics delivers in-depth proteome profiling of FFPE lung cancer biopsies from single glass slides

Clinical proteomics has the potential to add a valuable data layer to genomic and histopathological analyses in precision oncology, but its application to limited clinical material remains challenging. Here, we demonstrate that state-of-the-art mass spectrometry-based proteomics enables in-depth proteomic profiling of formalin-fixed paraffin-embedded (FFPE), including small biopsies and single tissue sections mounted on glass slides. Despite minimal input material, single-slide analyses of clinical lung tumor specimens yielded biologically and clinically informative data, supporting detection of actionable proteins, immune-related signatures, and multivariate biomarkers. These results establish the feasibility of proteomics for retrospective FFPE studies and routine clinical practice, expanding opportunities for biomarker discovery and precision medicine from scarce tissue material.

cancer biology↗

Pathway activation model for personalized prediction of drug synergy

Targeted monotherapies for cancer often fail due to inherent or acquired drug resistance. By aiming at multiple targets simultaneously, drug combinations can produce synergistic interactions that increase drug effectiveness and reduce resistance. Computational models based on the integration of omics data have been used to identify synergistic combinations, but predicting drug synergy remains a challenge. Here, we introduce DIPx, an algorithm for personalized prediction of drug synergy based on biologically motivated tumor- and drug-specific pathway activation scores (PASs). We trained and validated DIPx in the AstraZeneca-Sanger (AZS) DREAM Challenge dataset using two separate test sets: Test Set 1 comprised the combinations already present in the training set, while Test Set 2 contained combinations absent from the training set, thus indicating the models ability to handle novel combinations. The Spearman correlation coefficients between predicted and observed drug synergy were 0.50 (95% CI: 0.47-0.53) in Test Set 1 and 0.26 (95% CI: 0.22-0.30) in Test Set 2, compared to 0.38 (95% CI: 0.34-0.42) and 0.18 (95% CI: 0.16-0.20), respectively, for the best performing method in the Challenge. We show evidence that higher synergy is associated with higher functional interaction between the drug targets, and this functional interaction information is captured by PAS. We illustrate the use of PAS to provide a potential biological explanation in terms of activated pathways that mediate the synergistic effects of combined drugs. In summary, DIPx can be a useful tool for personalized prediction of drug synergy and exploration of activated pathways related to the effects of combined drugs.

bioinformatics↗

Integrative multi-omics analysis reveals molecular subtypes and tumor evolution of synovial sarcoma

Synovial sarcomas (SS) are malignant mesenchymal tumors characterized by the SS18-SSX fusion gene, which drives tumorigenesis by altering the composition of the BAF complex. Secondary genomic alterations that determine variations in tumor phenotype or clinical presentation are largely unknown. Herein, we present transcriptome, targeted DNA-sequencing, and proteomics analysis of 91 synovial sarcomas from 55 patients. We identified three SS clusters (SSCs) characterized by distinct histology, tumor microenvironments, genomic complexities, therapeutic effects, and clinical outcomes. Eight BAF complex components are differentially expressed among SSCs, and their role in mesenchymal-epithelial-transition is supported by single cell sequencing. The epithelial cells of biphasic tumors are more susceptible to developing copy number alterations, including amplification of PDCD1 and TMPRSS2. Our findings explain broad concepts in SS biology and imply that the BAF composition at the start of the tumorigenesis (i.e. the cellular linage) may determine the SS subtype, providing a rationale for individualized treatment strategies.

cancer biology↗

Glioblastoma stem cells express non-canonical proteins and exclusive mesenchymal-like or non-mesenchymal-like protein signatures

Glioblastomas (GBM) origin, recurrence and resistance to treatment are driven by GBM cancer stem cells (GSCs). Existing transcriptomic characterisations of GBM classify the tumours to three subtypes: classical, proneural, and mesenchymal. The comprehension of how expression patterns of the GBM subtypes are reflected at global proteome level in GSCs is limited. To characterise protein expression in GSCs, we performed in-depth proteogenomic analysis of patient-derived GSCs by RNA-sequencing and mass-spectrometry proteomics. We identified and quantified over 10,000 proteins in two independent GSCs panels, and propose a GSC-associated proteomic signature (GSAPS) that defines two distinct morphological conditions; one defined by a set of proteins expressed in non-mesenchymal - proneural and classical - GSCs (GPC-like), and another expressed in mesenchymal GSCs (GM-like). The expression of GM-like protein set in GBM tissue was associated with hypoxia, necrosis, recurrence, and worse overall survival in GBM patients. In a proof-of-concept proteogenomic approach, we discovered 252 non-canonical peptides expressed in GSCs, i.e., protein sequences that are variant or derive from genome regions previously considered protein-non-coding. We report new variants of the heterogeneous ribonucleoproteins (HNRNPs), which are implicated in mRNA splicing. Furthermore, we show that per-gene mRNA-protein correlations in GSCs are moderate and vary compared to GBM tissue.

cancer biology↗