Proteome Level Research Articles

Abstract 1958: EGFR and mTOR signaling facilitate RET-independent resistance to selective RET-TKIs

Abstract RET fusions are an oncogenic driver mutation occurring in 1-2% non-small cell lung cancer (NSCLC). Currently, RET-selective tyrosine kinase inhibitors (TKIs) selpercatinib and pralsetinib are approved for this patient population, demonstrating favorable antitumor activity and safety profiles in advanced RET fusion-positive NSCLC. However, tumors eventually become refractory to selective RET inhibitor monotherapy. We sought to identify signaling pathways which mediate RET-independent acquired resistance to RET inhibitors and effective therapeutic combinations to overcome resistance. Using LC-2/ad (CCDC6-RET) NSCLC cells, we assessed the expression and activation of critical signaling pathways at a proteomic level following RET knockdown using reverse-phase protein array. RET knockdown resulted in rapidly increased adaptive upregulation of EGFR, HER2, MET, mTOR and MEK pathway signaling; observations were confirmed by Western Blotting. To determine whether EGFR or MET signaling facilitate RET inhibitor resistance, LC-2/ad and NCCE-TH1101 (KIF5B-RET) NSCLC cells were treated with increasing concentrations of selpercatinib or pralsetinib with or without ligands for these receptors, EGF or HGF respectively, and after five days cell viability was measured by CellTiter Glo. EGFR and MET signaling promoted RET TKI resistance in both cell lines. Next, to evaluate whether blockade of EGFR or MET signaling could enhance the activity of RET inhibitors, we treated LC-2/ad and NCCE-TH1101 cells with RET TKIs alone or in combination with the EGFR TKI erlotinib or poziotinib, or the MET inhibitor SU11274. We observed an additive effect with EGFR but not MET inhibitors when combined with RET TKIs. Western blot analysis revealed that EGFR signaling could reactivate mTOR and MAPK signaling after RET TKI treatment. Therefore, we next tested whether inhibition of these downstream pathways might enhance RET TKI sensitivity. We observed that treatment of LC-2/ad and NCCE-TH1101 cells with an mTOR inhibitor (everolimus) or a MEK inhibitor (trametinib) in combination with RET TKIs increased the anti-tumor activity or RET inhibitors. Finally, we established cells with acquired resistance to selpercatinib and pralsetinib. RET TKI resistant cells did not harbor secondary RET fusion mutations but underwent epithelial to mesenchymal transition and exhibited pan-RET inhibitor resistance. Moreover, RET TKI resistant cells showed increased cell surface expression of EGFR and MET by flow cytometry. RET TKI resistant cells were sensitive to mTOR or MEK inhibitors in combination with RET TKIs. Collectively, our findings indicate that in RET-fusion positive NSCLC tumor cells, activation of bypass pathways including the EGFR, MAPK, mTOR pathways may facilitate RET inhibitor resistance and that blockade of bypass pathways may enhance the efficacy of selective RET inhibitors and overcome acquired RET TKI resistance. Citation Format: Tomohiro Takehara, Monique B. Nilsson, Ximeng Liu, Hibiki Udagawa, Naoto Morikawa, Alissa Poteete, Junqin He, Xiaoxing Yu, Thiru Arumugam, Koichi Goto, John V. Heymach. EGFR and mTOR signaling facilitate RET-independent resistance to selective RET-TKIs [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1958.

Abstract 4258: An innovative PDX and patient-derived 3D platform for improved translatability of new medical entities

Abstract The preclinical platform landscape in oncology has radically changed in the last 15 years, mainly due to an increased ability to generate patient-derived models. Multiple 3D tumor cell cultures methods, either from established cell lines or from primary cultures, have been developed to assess the activity of therapeutic agents in vitro. These methods are strongly influenced by the experimental conditions, such as cell culture conditions, the presence of 3D supports like Matrigel, and the experimental design of the assay, such as the choice of the dose and the duration of the assay. The ability of these methods to predict drug efficacy in the patient relies not only in the optimization of the experimental settings, but also on the representativeness of the patient population, and on the prevention of model drifts due to clonal selection and/or tumor cell adaptation to culture conditions with passages. In this project, we aimed to develop an innovative 3D cell model platform that addresses all these experimental challenges. To do so, we took advantage of our low passage Tumorgraft platform, a bank of more than 1500 patient-derived xenografts (PDXs) well characterized at molecular and pharmacological level and highly representative of the patient population, to generate the Tumorgraft3D platform. We derived TumorGraft3D models from low passage PDXs, by using proprietary, indication-specific culture conditions. By keeping these models at low passage, we were able to avoid clonal drifts. Moreover, the use of indication specific media allowed the preservation of patient’s and PDX’s tumor histology, molecular traits at genetic, gene expression and proteomic level, and displayed proliferation rates that recapitulate those observed in the parental PDXs. So far, we have successfully developed over 100 models from breast, ovarian, prostate, NSCLC, colorectal and gastric indications, with success rates up to 90%. Ex-vivo testing of standard of care highlighted different response profiles in accordance with the molecular feature of these models. The tumor dissociation process and culture conditions optimized with PDXs were also used for the development of 3D cultures derived from patients’ samples. These data demonstrated that our 3D platform can integrate the complementarity of our PDX and patient-derived 3D platform, which lies in the possibility of evaluating test agent activity in the repeatable and reproducible setting provided by the patient-derived 3D models, as well as in models directly derived from patients with more heterogeneous and indication-specific establishment success rate, and throughput dependent on the amount of primary tumor material. Citation Format: Samaneh Kamali, Stefano Cairo, Fu-Ju Chou, Taylor Light, Mara Gilardi, Brandon Walling, Christine Baer, Hsiu-Wen Tsai, Abhay Andar, Karin Abarca-Heidemann, Maria Mancini. An innovative PDX and patient-derived 3D platform for improved translatability of new medical entities [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4258.

Abstract 888: Uncovering clinically relevant omics signatures from pan-cancer imaging and multi-omics data integration

Abstract Despite advancements in deep learning for histopathology, integrating these insights with multi-omics data to uncover clinically relevant omics pathway-level signatures remains a challenge. Our study addresses this gap by applying unsupervised learning techniques on pan-cancer multi-omics data, leveraging 3,080 Hematoxylin and Eosin (H&E) images from 1,010 patients in Clinical Proteomic Tumor Analysis Consortium (CPTAC) to uncover omics pathway-level signatures that drive discernable morphology phenotypes at the tissue level. First, imaging models were trained to predict clinical and mutation outcomes, and thereafter, integrated with transcriptomic and proteomic expression data using sparse canonical correlation analysis. Our findings reveal that images of TP53 mutated samples exhibited increased nuclear size and dense lymphoplasmacytic infiltration. These morphological changes correlated with neutrophil and macrophage signaling at the proteomic level, and IL-1 mediated signaling at the transcriptomic level, highlighting the complementary perspectives different omics can provide. To further elucidate immune interactions, we applied multi-omics deconvolution to identify 7 immune subtypes, characterizing each with gene set enrichment, germline DNA variations, and kinase activations. We show that imaging models trained to predict these subtypes differentiate tissue morphologies corresponding to immune enrichment (AUROC: 0.84). To further confirm the robustness of our approach, we trained models to predict co-regulated proteomic modules clustered by independent component analysis. We demonstrate a signature in pan-squamous tumors, consisting of T cell markers and interferon-gamma response proteins like CD4, CD48, and GBP5, which imaging models can successfully predict from histopathology. Manual review from pathologists confirmed lymphocytic T-cell density as a differentiator in samples with the highest and lowest T-cell signaling. Together, these approaches demonstrate that genomic characteristics discovered via unbiased mining of pan-cancer multi-omics data manifest as quantitative imaging phenotypes. These results underscore the potential of multi-omics and digital pathology to integratively uncover and confirm new cancer biology. Our ongoing work will explore predicting drug response and survival from morphology patterns related to multi-omics signatures. Citation Format: Joshua Wang, Runyu Hong, Jimin Tan, Wenke Liu, David Fenyȯ. Uncovering clinically relevant omics signatures from pan-cancer imaging and multi-omics data integration [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 888.

Proteomic profiling reveals that ESR1 mutations enhance cyclin-dependent kinase signaling

Three quarters of all breast cancers express the estrogen receptor (ER, ESR1 gene), which promotes tumor growth and constitutes a direct target for endocrine therapies. ESR1 mutations have been implicated in therapy resistance in metastatic breast cancer, in particular to aromatase inhibitors. ESR1 mutations promote constitutive ER activity and affect other signaling pathways, allowing cancer cells to proliferate by employing mechanisms within and without direct regulation by the ER. Although subjected to extensive genetic and transcriptomic analyses, understanding of protein alterations remains poorly investigated. Towards this, we employed an integrated mass spectrometry based proteomic approach to profile the protein and phosphoprotein differences in breast cancer cell lines expressing the frequent Y537N and Y537S ER mutations. Global proteome analysis revealed enrichment of mitotic and immune signaling pathways in ER mutant cells, while phosphoprotein analysis evidenced enriched activity of proliferation associated kinases, in particular CDKs and mTOR. Integration of protein expression and phosphorylation data revealed pathway-dependent discrepancies (motility vs proliferation) that were observed at varying degrees across mutant and wt ER cells. Additionally, protein expression and phosphorylation patterns, while under different regulation, still recapitulated the estrogen-independent phenotype of ER mutant cells. Our study is the first proteome-centric characterization of ESR1 mutant models, out of which we confirm estrogen independence of ER mutants and reveal the enrichment of immune signaling pathways at the proteomic level.

Abstract 7291: Detection of a truncated form of multidrug resistance protein 1 (MRP1) in plasma sEVs derived from cancer patients

Abstract Multidrug resistance protein 1 (MRP1/ABCC1) is an ATP dependent transmembrane efflux pump that confers to drug resistance. Overexpression of MRP1 has been observed in both pancreatic ductal adenocarcinoma (PDAC) and breast cancer tissues. Numerous proteomics studies have revealed the presence of MRP1 in small extracellular vesicles (sEVs) released from cancer cells. Cancer-derived sEVs are known to be released into the circulation, thus becoming attractive candidates for non-invasive biomarker development. However, whether the MRP1 level in plasma sEVs is indicative of human cancer has not been evaluated. This study examined the MRP1 expression in plasma sEVs derived from patients with PDAC, breast and colon cancer, and age- and gender-matched healthy subjects. The patient plasma samples were obtained primarily from the NCI-sponsored Cooperative Human Tissue Network and Stephenson Cancer Center. Healthy plasma samples were provided by the Oklahoma Blood institute. Human pancreatic cancer cell lines PANC-1, MIA PaCa-2 and BxPC-3, and the pancreatic ductal cell line hTERT-HPNE were tested for MRP1 expression prior to examination of plasma samples. Cellular and plasma sEVs were isolated using the double filtration followed by polymer precipitation method. Western blot was performed to assess the expression level of MRP1 and proteomics was applied to confirm the detection of a truncated form of MRP1 in plasma sEVs. MRP1 expression was detected in the sEVs derived from all three pancreatic cancer cell lines but absent in the sEVs from the HPNE line. Overexpression of a truncated MRP1 protein was detected in plasma sEVs derived from patients with early stage (stage I-IIA) and late stage (III-IV) PDAC, compared with that from matched healthy subjects (n=17 for each group). Similar findings were evident in plasma sEVs from patients with breast cancer (n=7), while the results remained inconclusive in plasma sEVs from patients with colon cancer (n=7). Protein structure analysis indicated that the truncated form of MRP1 likely consists of the regions between the QCRL-1 motif and the C-terminus of the protein. Proteomic analysis detected the peptides derived from the C-terminus region, consistent with the structural analysis. Furthermore, the truncated form of MRP1 can only be detected using antibodies against the epitopes in the C-terminus and QCRL-1 region of MRP1 protein. In summary, we have detected a truncated form of MRP1 protein in plasma sEVs derived from cancer patients, and the level of its expression is significantly elevated in patients with PDAC and breast cancer. Citation Format: Kritisha Bhandari, Jeng Shi Kong, Chao Xu, Ajay Jain, Wei-Qun Ding. Detection of a truncated form of multidrug resistance protein 1 (MRP1) in plasma sEVs derived from cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7291.

Multi-omics characterization of partial chemical reprogramming reveals evidence of cell rejuvenation

Partial reprogramming by cyclic short-term expression of Yamanaka factors holds promise for shifting cells to younger states and consequently delaying the onset of many diseases of aging. However, the delivery of transgenes and potential risk of teratoma formation present challenges for in vivo applications. Recent advances include the use of cocktails of compounds to reprogram somatic cells, but the characteristics and mechanisms of partial cellular reprogramming by chemicals remain unclear. Here, we report a multi-omics characterization of partial chemical reprogramming in fibroblasts from young and aged mice. We measured the effects of partial chemical reprogramming on the epigenome, transcriptome, proteome, phosphoproteome, and metabolome. At the transcriptome, proteome, and phosphoproteome levels, we saw widescale changes induced by this treatment, with the most notable signature being an upregulation of mitochondrial oxidative phosphorylation. Furthermore, at the metabolome level, we observed a reduction in the accumulation of aging-related metabolites. Using both transcriptomic and epigenetic clock-based analyses, we show that partial chemical reprogramming reduces the biological age of mouse fibroblasts. We demonstrate that these changes have functional impacts, as evidenced by changes in cellular respiration and mitochondrial membrane potential. Taken together, these results illuminate the potential for chemical reprogramming reagents to rejuvenate aged biological systems and warrant further investigation into adapting these approaches for in vivo age reversal.

Multi-omics characterization of partial chemical reprogramming reveals evidence of cell rejuvenation.

Partial reprogramming by cyclic short-term expression of Yamanaka factors holds promise for shifting cells to younger states and consequently delaying the onset of many diseases of aging. However, the delivery of transgenes and potential risk of teratoma formation present challenges for in vivo applications. Recent advances include the use of cocktails of compounds to reprogram somatic cells, but the characteristics and mechanisms of partial cellular reprogramming by chemicals remain unclear. Here, we report a multi-omics characterization of partial chemical reprogramming in fibroblasts from young and aged mice. We measured the effects of partial chemical reprogramming on the epigenome, transcriptome, proteome, phosphoproteome, and metabolome. At the transcriptome, proteome, and phosphoproteome levels, we saw widescale changes induced by this treatment, with the most notable signature being an upregulation of mitochondrial oxidative phosphorylation. Furthermore, at the metabolome level, we observed a reduction in the accumulation of aging-related metabolites. Using both transcriptomic and epigenetic clock-based analyses, we show that partial chemical reprogramming reduces the biological age of mouse fibroblasts. We demonstrate that these changes have functional impacts, as evidenced by changes in cellular respiration and mitochondrial membrane potential. Taken together, these results illuminate the potential for chemical reprogramming reagents to rejuvenate aged biological systems and warrant further investigation into adapting these approaches for in vivo age reversal.

Abstract 5503: Ultrahigh-plex spatial phenotyping of head and neck cancer tissue uncovers multiomic signatures of immunotherapy response

Abstract Background Targeted immune checkpoint inhibitors (ICI) with anti-PD-1/PD-L1 therapy offer durable treatment of mucosal head and neck squamous cell cancer (HNSCC), in both human papillomavirus-positive (HPV+) and negative (HPV-) patients. However, currently available biomarker signatures for targeted ICI therapies have limited predictive value. Our recent ultrahigh-plex profiling of HNSCC tissue with 100+ cancer hallmarks of tumor and immunobiology uncovered distinct spatial domains that serve as defining factors for clinical response and resistance. Methods Our unbiased analysis of whole-slide metastatic HNSCC tumors is from a clinical cohort of patients treated with Pembrolizumab/Nivolumab. The cohort consisted of patients with a range of outcomes from complete vs partial vs progressive disease responses to ICI therapy. We first characterized the tumor microenvironment using our ultrahigh-plex protein panel with 100+ antibodies on the PhenoCycler®-Fusion platform. To expand upon our biomarker discovery, we included multiomic cancer hallmarks with a multimodal protein/RNA detection panel. Targeted spatial RNA detection was performed to complement and augment the microenvironment characterization achieved by our protein panel. To further consolidate the multiomic data, we leveraged MaxFuse, a state-of-the art computational framework that integrates multimodal spatial and single-cell expression data. Results Our multiomic spatial phenotyping uncovered diverse tumor regions, each with distinct biomarker expression that is reflected across modalities including protein, RNA, and metabolic activity, indicating regions likely associated with resistance to immunotherapy. Our multiomic data integration also revealed spatial signatures associated with different tissue compartments, such as the tumor and non-tumor associated tertiary lymphoid structures. Conclusions We demonstrate a multi-pronged approach that incorporated both novel experimental and computational techniques for elucidating tumor microenvironment in HNSCC tissue prior to ICI-based immunotherapy. Our multiomic approach provides deeper characterization of the HNSCC at the transcriptomic and proteomic level incorporating depth across the entire transcriptome and single-cell spatial resolution of key protein determinants for predicting and furthering our understanding of immunotherapy response to ICI therapy. Citation Format: Aditya Pratapa, Lydia Hernandez, Bassem Ben Cheikh, Niyati Jhaveri, Arutha Kulasinghe. Ultrahigh-plex spatial phenotyping of head and neck cancer tissue uncovers multiomic signatures of immunotherapy response [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5503.

Abstract 1986: Acquisition of carboplatin resistance corresponds with reduced sacituzumab govitecan efficacy in triple negative breast cancer patient derived xenografts

Abstract Sacituzumab Govitecan (SG) is an antibody drug conjugate that targets the epithelial glycoprotein Trop-2. SG has been approved for the treatment of patients with triple negative breast cancer (TNBC) or estrogen receptor positive (ER+) breast cancer after they have received at least two previous systemic treatments. The objectives of this study were to identify biomarkers of SG response using breast cancer patient-derived xenografts (PDXs) and determine if SG efficacy changes when cells become carboplatin resistant (CR). Analysis of transcriptomic and proteomic levels of Trop2 using bulk RNAseq, scRNA-seq, and tandem mass tag spectrometry found that overall ER+ PDXs had significantly lower RNA (p-value less than 0.001) and protein (p-value less than 0.001) expression of Trop-2 than TNBC PDXs. Immunohistochemical assessment of 42 TNBC and 27 ER+ patient tumor samples identified variation in Trop-2 expression; within each cohort, expression varied from highly positive to completely negative. To test the association of Trop-2 with drug responsiveness, cells from 19 different PDXs were cultured in vitro and administered increasing doses of SG. TNBC cells were more responsive to SG treatments compared to ER+ cells (p-value equaling 0.005). Correlation analysis identified a positive relationship of drug response with protein abundance which was stronger in TNBC samples. In vivo studies with 7 TNBC PDXs found that over 70% were highly susceptible to SG treatments, with some tumors being completely eradicated or exhibiting a total inhibition of tumor growth which resulted in a long-term durable response after 10 weeks of treatment. In vivo across all the PDXs, Trop-2 expression alone did not strongly correlate with SG treatment success. Interestingly, CR sublines derived from 2 carboplatin sensitive PDXs were significantly less responsive to SG than their parental PDXs (p-value less than 0.001). These two CR sublines were also significantly less sensitive to the majority of 555 other drugs identified by the NCI compared to their parental PDXs. Ongoing studies are defining mechanisms of reduced SG efficacy in CR models. Overall, these studies find that SG is highly effective in many TNBC PDX models and should be utilized earlier in treatment protocols, especially in metastatic patients. Citation Format: Carson J. Walker, Julia E. Altman, Emily K. Zboril, Rachel K. Myrick, Nicole S. Hairr, David C. Boyd, Bin Hu, Mikhail G. Dozmorov, J. Chuck Harrell. Acquisition of carboplatin resistance corresponds with reduced sacituzumab govitecan efficacy in triple negative breast cancer patient derived xenografts [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1986.

Abstract 1751: Deep multi-omics analysis of uterine leiomyosarcoma reveals defective HRD signature, a novel gene fusion and the amplification of the poor-prognosis CTHRC1 gene

Abstract Uterine leiomyosarcoma (uLMS) is a rare and aggressive cancer representing approximately 2-5% of all uterine malignancies. The molecular heterogeneity and pathogenesis of uLMS are notwell understood. To unravel the intricate molecular landscape of uLMS, we conducted a multi-omics workflow consisting of 4 high-throughput sequencing technologies; short- and long-readwhole genome sequencing (WGS), RNA-Seq, and Tandem mass tag (TMT) labeling enablingmassively parallel proteome proteomic analyses. Our study cohort consisted of 97 fresh frozentissue samples collected from 68 female patients, with 19 uLMS, 37 leiomyoma (LM), and 41normal myometrium (MM). A deep analysis of somatic variants with clinical impact revealed 46variants present in 90% of samples and two actionable therapeutic targets: IDH1_p.Arg132Cysand KRAS_p.Gly12Val. Also, 80% of the samples presented a chromothripsis signature and 60%presented the SBS3 COSMIC signature related to homologous recombination deficiency (HRD).Analysis of the proliferation score in RNA-Seq data revealed a notable difference among thetumor groups, signifying a higher proliferation index in the uLMS group, suggesting a moreaggressive growth pattern with implications for clinical behavior and disease progression. Afterapplying a customized workflow to identify balanced chromosomal abnormalities of genefusions with clinical impact, the proto-oncogene eukaryotic elongation factor 1 alpha (EEF1A1)emerged as a prominent fusion partner. Eight fusions in 7 samples were identified withtranscriptional overexpression in positive cases. At the proteomic level the enrichment analysisunveiled activated pathways associated with the innate immune system, while suppressedpathways pertained to extracellular matrix (ECM) organization and smooth muscle contraction.With an integrated bioinformatics pipeline, we identified copy number amplification of theCTHRC1 gene in 80% of the samples, accompanied by increased gene expression and proteinlevels. This gene is related to collagen remodeling and degradation and has been demonstratedto be upregulated in some solid tumors and is closely associated with malignancy. Survivalcurve analysis highlighted a significantly poorer prognosis in uLMS exhibiting CTHRC1 geneamplification with potential clinical relevance of CTHRC1 amplification as a prognostic indicatorin uLMS. Taken together these results show the overall suppression of ECM and collagenremodeling in uLMS associated with poor prognosis. Additionally, these data provide the firstevidence that the EEF1A1 gene has a strong selective pressure in uLMS underscoring theindispensable role of precision genomics in advancing precision medicine. Citation Format: Raul Maia Falcao, Jorge Estefano de Souza, William Mathieson, Joseph Woodward Carlson, Tirzah Braz Petta. Deep multi-omics analysis of uterine leiomyosarcoma reveals defective HRD signature, a novel gene fusion and the amplification of the poor-prognosis CTHRC1 gene [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1751.

Transcriptomic and proteomic profiles of fetal versus adult mesenchymal stromal cells and mesenchymal stromal cell-derived extracellular vesicles

BackgroundMesenchymal stem/stromal cells (MSCs) can regenerate tissues through engraftment and differentiation but also via paracrine signalling via extracellular vesicles (EVs). Fetal-derived MSCs (fMSCs) have been shown, both in vitro and in animal studies, to be more efficient than adult MSC (aMSCs) in generating bone and muscle but the underlying reason for this difference has not yet been clearly elucidated. In this study, we aimed to systematically investigate the differences between fetal and adult MSCs and MSC-derived EVs at the phenotypic, RNA, and protein levels.MethodsWe carried out a detailed and comparative characterization of culture-expanded fetal liver derived MSCs (fMSCs) and adult bone marrow derived MSCs (aMSCs) phenotypically, and the MSCs and MSC-derived EVs were analysed using transcriptomics and proteomics approaches with RNA Sequencing and Mass Spectrometry.ResultsFetal MSCs were smaller, exhibited increased proliferation and colony-forming capacity, delayed onset of senescence, and demonstrated superior osteoblast differentiation capability compared to their adult counterparts. Gene Ontology analysis revealed that fMSCs displayed upregulated gene sets such as “Positive regulation of stem cell populations”, “Maintenance of stemness” and “Muscle cell development/contraction/Myogenesis” in comparison to aMSCs. Conversely, aMSCs displayed upregulated gene sets such as “Complement cascade”, “Adipogenesis”, “Extracellular matrix glycoproteins” and “Cellular metabolism”, and on the protein level, “Epithelial cell differentiation” pathways. Signalling entropy analysis suggested that fMSCs exhibit higher signalling promiscuity and hence, higher potency than aMSCs. Gene ontology comparisons revealed that fetal MSC-derived EVs (fEVs) were enriched for “Collagen fibril organization”, “Protein folding”, and “Response to transforming growth factor beta” compared to adult MSC-derived EVs (aEVs), whereas no significant difference in protein expression in aEVs compared to fEVs could be detected.ConclusionsThis study provides detailed and systematic insight into the differences between fMSCs and aMSCs, and MSC-derived EVs. The key finding across phenotypic, transcriptomic and proteomic levels is that fMSCs exhibit higher potency than aMSCs, meaning they are in a more undifferentiated state. Additionally, fMSCs and fMSC-derived EVs may possess greater bone forming capacity compared to aMSCs. Therefore, using fMSCs may lead to better treatment efficacy, especially in musculoskeletal diseases.

The Pd (II) Reduction Mechanisms in Bacillus megaterium Y-4 Revealed by Proteomic Analysis.

Many studies have been conducted on the microbial reduction of Pd (II) to palladium nanoparticles (Pd-NPs) due to the environmental friendliness, low cost, and the decreased toxicity of Pd (II) ions. In this study, we investigate the reduction mechanism of Pd (II) by Bacillus megaterium Y-4 through proteomics. The data are available via ProteomeXchange with identifier PXD049711. Our results revealed that B. megaterium Y-4 may use the endogenous electron donor (NAD(P)H) generated by nirB, tdh, and fabG and reductase to reduce Pd (II) to Pd-NPs. The expression levels of fabG, tdh, gudB, and rocG that generate NAD(P)H were further increased, and the number of reduced Pd-NPs was further increased with the exogenous electron donor sodium formate. Endogenous electron mediators such as quinones and flavins in B. megaterium Y-4 can further enhance Pd (II) reduction. The findings provided invaluable information regarding the reduction mechanism of Pd (II) by B. megaterium Y-4 at the proteome level.

CLCA2: A Potential Guardian against Premature Senescence and Skin Aging.

Cellular senescence, a state of irreversible growth arrest, is implicated in various age-related pathologies, including skin aging. In this study, we investigated the role of CLCA2, a calcium-activated chloride channel accessory protein, in cellular senescence and its implications for skin aging. Utilizing UVB and Nutlin3a-induced senescence models, we observed the upregulation of CLCA2 at both transcriptomic and proteomic levels, suggesting its involvement in senescence pathways. Further analysis revealed that the depletion of CLCA2 led to accelerated senescence onset, characterized by classic senescence markers and a unique secretome profile. In 3D skin equivalent models, SEs constructed with CLCA2 knockdown fibroblasts exhibited features reminiscent of aged skin, underscoring the importance of CLCA2 in maintaining skin homeostasis. Our findings highlight CLCA2 as a novel regulator of cellular senescence and its potential implications for skin aging mechanisms.

Reference Materials for Improving Reliability of Multiomics Profiling

AbstractHigh-throughput technologies for multiomics or molecular phenomics profiling have been extensively adopted in biomedical research and clinical applications, offering a more comprehensive understanding of biological processes and diseases. Omics reference materials play a pivotal role in ensuring the accuracy, reliability, and comparability of laboratory measurements and analyses. However, the current application of omics reference materials has revealed several issues, including inappropriate selection and underutilization, leading to inconsistencies across laboratories. This review aims to address these concerns by emphasizing the importance of well-characterized reference materials at each level of omics, encompassing (epi-)genomics, transcriptomics, proteomics, and metabolomics. By summarizing their characteristics, advantages, and limitations along with appropriate performance metrics pertinent to study purposes, we provide an overview of how omics reference materials can enhance data quality and data integration, thus fostering robust scientific investigations with omics technologies.

Functional characterization of Francisella tularensis subspecies holarctica genotypes during tick cell and macrophage infections using a proteogenomic approach.

Tularemia is a vector-borne disease caused by the Gram-negative bacterium Francisella tularensis. Known hosts and vectors in Europe are hare and ticks. F. tularensis is transmitted from ticks and animals, but also from the hydrotelluric environment and the consumption of contaminated water or food. A changing climate expands the range in which ticks can live and consequently might contribute to increasing case numbers of tularemia. Two subspecies of F. tularensis are human pathogenic. Francisella tularensis tularensis (Ftt) is endemic in North America, while Francisella tularensis holarctica (Fth) is the only subspecies causing tularemia in Europe. Ft is classified as a category A bioterrorism agent due to its low infectious dose, multiple modes of transmission, high infectivity and potential for airborne transmission and has become a global public health concern. In line with the European survey and previous phylogenetic studies, Switzerland shows the co-distribution of B.6 and B.12 strains with different geographical distribution and prevalence within the country. To establish itself in different host environments of ticks and mammals, F. tularensis presumably undergoes substantial changes on the transcriptomics and proteomic level. Here we investigate the transcriptomic and proteomic differences of five strains of Fth upon infection of rabbit macrophages and tick cells.

A multiomics dataset for the study of RNA modifications in human macrophage differentiation and polarisation

RNA modifications have emerged as central regulators of gene expression programs. Amongst RNA modifications are N6-methyladenosine (m6A) and RNA 5-hydroxymethylcytosine (5hmC). While m6A is established as a versatile regulator of RNA metabolism, the functions of RNA 5hmC are unclear. Despite some evidence linking RNA modifications to immunity, their implications in gene expression control in macrophage development and functions remain unclear. Here we present a multi-omics dataset capturing different layers of the gene expression programs driving macrophage differentiation and polarisation. We obtained mRNA-Seq, m6A-IP-Seq, 5hmC-IP-Seq, Polyribo-Seq and LC-MS/MS data from monocytes and resting-, pro- and anti-inflammatory-like macrophages. We present technical validation showing high quality and correlation between samples for all datasets, and evidence of biological consistency of modelled macrophages at the transcriptomic, epitranscriptomic, translational and proteomic levels. This multi-omics dataset provides a resource for the study of RNA m6A and 5hmC in the context of macrophage biology and spans the gene expression process from transcripts to proteins.

Machine learning-based investigation of regulated cell death for predicting prognosis and immunotherapy response in glioma patients

Glioblastoma is a highly aggressive and malignant type of brain cancer that originates from glial cells in the brain, with a median survival time of 15 months and a 5-year survival rate of less than 5%. Regulated cell death (RCD) is the autonomous and orderly cell death under genetic control, controlled by precise signaling pathways and molecularly defined effector mechanisms, modulated by pharmacological or genetic interventions, and plays a key role in maintaining homeostasis of the internal environment. The comprehensive and systemic landscape of the RCD in glioma is not fully investigated and explored. After collecting 18 RCD-related signatures from the opening literature, we comprehensively explored the RCD landscape, integrating the multi-omics data, including large-scale bulk data, single-cell level data, glioma cell lines, and proteome level data. We also provided a machine learning framework for screening the potentially therapeutic candidates. Here, based on bulk and single-cell sequencing samples, we explored RCD-related phenotypes, investigated the profile of the RCD, and developed an RCD gene pair scoring system, named RCD.GP signature, showing a reliable and robust performance in predicting the prognosis of glioblastoma. Using the machine learning framework consisting of Lasso, RSF, XgBoost, Enet, CoxBoost and Boruta, we identified seven RCD genes as potential therapeutic targets in glioma and verified that the SLC43A3 highly expressed in glioma grades and glioma cell lines through qRT-PCR. Our study provided comprehensive insights into the RCD roles in glioma, developed a robust RCD gene pair signature for predicting the prognosis of glioma patients, constructed a machine learning framework for screening the core candidates and identified the SLC43A3 as an oncogenic role and a prediction biomarker in glioblastoma.

Proteomics Clustering Research of ITRAQ Markers in Plasma of AIDS Patients with Different Chinese Medicine Syndromes.

We aimed to distinguish the different Chinese medicine (CM) syndromes of acquired immune deficiency syndrome (AIDS) patients at the proteomics level. We collected AIDS patients diagnosed with different CM syndromes from Weishi County, Kaifeng City, Henan Province, China, including Qi-deficiency syndrome (named QD group) and dampness-heat syndrome (named DH group). Healthy people were collected as controls from Weishi County, Kaifeng city, Henan Province, China. The plasma from three groups were labeled with ITRAQ, LC/MC was used for protein quantitative analysis. Finally, sequence search and cluster analysis were performed. Overall, 27 different proteins were found. Three proteins were up-regulated and 2 proteins down-regulated in the QD group, 11 proteins up-regulated and 13 proteins down-regulated in the DH group. Compared with DH group, there were 7 different proteins in QD group, among which 5 proteins were down-regulated and 2 proteins were up-regulated. When the target protein of DH group was up-regulated, the protein of HC group was down-regulated correspondingly. The significance analysis and clustering of protein results showed that DH group was significantly different from QD group and HC group at the protein level (P<0.05). However, the QD group could not be effectively distinguished from the HC group. AAT, PF4, C-reactive protein and c4bp may be used as potential biomarkers in DH group. Mass spectrometry based on feature selection can be used to classify different CM syndromes.

Metagenomic analysis unravels novel taxonomic differences in the uterine microbiome between healthy mares and mares with endometritis.

The application of high throughput technologies has enabled unravelling of unique differences between healthy mares and mares with endometritis at transcriptomic and proteomic levels. However, differences in the uterine microbiome are yet to be investigated. The present study was aimed at evaluating the differences in uterine microbiome between healthy mares and mares with endometritis. Low-volume lavage (LVL) samples were collected from the uterus of 30 mares classified into healthy (n=15) and endometritis (n=15) based on their reproductive history, intrauterine fluid accumulation, gross appearance of LVL samples, endometrial cytology and bacterial culture. The samples were subjected to 16S rRNA sequencing. Notable differences in the uterine microbiome were observed between healthy mares and mares with endometritis at various taxonomic levels. In healthy mares, the most abundant phylum, class, order and family were Firmicutes, Bacilli, Bacillales and Paenibacillaceae, respectively. In contrast, the most abundant corresponding taxonomic levels in mares with endometritis were Proteobacteria, Gammaproteobacteria, Enterobacterales and Enterobacteriaceae, respectively. At the genus level, Brevibacillus and Paenibacillus were more abundant in healthy mares, whereas Escherichia, Salmonella and Klebsiella were more abundant in mares with endometritis. In healthy mares, Brevibacillus brevis was the most abundant species, followed by Brevibacillus choshinensis and Paenibacillus sp JDR-2. However, in mares with endometritis, Escherichia coli was the most abundant species, followed by Salmonella enterica and Klebsiella pneumoniae. These results confirmed the previously reported presence of a uterine microbiome in healthy mares and helped unravel some alterations that occur in mares with endometritis. The findings can potentially help formulate new approaches to prevent or treat equine endometritis.

CDH1 gene as biomarker towards breast cancer prediction*

Breast cancer is considered to be happened due to genetic aberration. Out of several genes expressed, it is found that cadherin 1, type 1 (CDH1) is responsible in several ways to control the metabolic order in human. Deregulation of the function of protein E-cadherin, expressed from CDH1 plays an important role in lobular breast cancer. In order to understand the root cause of this recent claim, we focus on CDH1 gene: whether the genetic information translated due to any deviation/alteration/modification in its sequence is related to the occurrence of the different types breast cancer. Towards this end, quantitative analysis of different biophysical and bio-chemical properties of CDH1 gene in genomic and proteomic levels from the available genomic (cDNA) sequences of CDH1 gene (obtained from the COSMIC Database for 78 patients, suffering from various types of breast cancer) clearly emphasizes that alternation/modification in the sequence of the CDH1 gene can be detrimental. Furthermore, Random forest, K-nearest neighbour and stochastic gradient descent (SGD) algorithms are applied on the derived dataset to classify the types of breast cancer, and to validate our hypothesis regarding the acute role of CDH1 as potential bio marker for breast cancer. Analysis of the mutated CDH1 gene sequences, and their related parameters using aforesaid machine learning techniques clearly establish that CDH1 gene can take the deterministic role in predicting the chances of occurrences of different types of breast cancer with an accuracy of > 90 % . Such an observation opens a new paradigm in diagnostic approach of breast cancer. Communicated by Ramaswamy H. Sarma