RESUMO
There is increasing interest in developing in-depth proteomic approaches for mapping tissue heterogeneity in a cell-type-specific manner to better understand and predict the function of complex biological systems such as human organs. Existing spatially resolved proteomics technologies cannot provide deep proteome coverage due to limited sensitivity and poor sample recovery. Herein, we seamlessly combined laser capture microdissection with a low-volume sample processing technology that includes a microfluidic device named microPOTS (microdroplet processing in one pot for trace samples), multiplexed isobaric labeling, and a nanoflow peptide fractionation approach. The integrated workflow allowed us to maximize proteome coverage of laser-isolated tissue samples containing nanogram levels of proteins. We demonstrated that the deep spatial proteomics platform can quantify more than 5000 unique proteins from a small-sized human pancreatic tissue pixel (â¼60,000 µm2) and differentiate unique protein abundance patterns in pancreas. Furthermore, the use of the microPOTS chip eliminated the requirement for advanced microfabrication capabilities and specialized nanoliter liquid handling equipment, making it more accessible to proteomic laboratories.
Assuntos
Peptídeos , Proteoma , Proteômica , Humanos , Proteoma/análise , Proteômica/métodos , Peptídeos/análise , Peptídeos/química , Pâncreas/metabolismo , Pâncreas/química , Nanotecnologia , Técnicas Analíticas Microfluídicas/instrumentação , Microdissecção e Captura a Laser/métodosRESUMO
PURPOSE: Emerging evidence underscores the critical role of extrinsic factors within the microenvironment in protecting leukemia cells from therapeutic interventions, driving disease progression, and promoting drug resistance in acute myeloid leukemia (AML). This finding emphasizes the need for the identification of targeted therapies that inhibit intrinsic and extrinsic signaling to overcome drug resistance in AML. EXPERIMENTAL DESIGN: We performed a comprehensive analysis utilizing a cohort of â¼300 AML patient samples. This analysis encompassed the evaluation of secreted cytokines/growth factors, gene expression, and ex vivo drug sensitivity to small molecules. Our investigation pinpointed a notable association between elevated levels of CCL2 and diminished sensitivity to the MEK inhibitors (MEKi). We validated this association through loss-of-function and pharmacologic inhibition studies. Further, we deployed global phosphoproteomics and CRISPR/Cas9 screening to identify the mechanism of CCR2-mediated MEKi resistance in AML. RESULTS: Our multifaceted analysis unveiled that CCL2 activates multiple prosurvival pathways, including MAPK and cell-cycle regulation in MEKi-resistant cells. Employing combination strategies to simultaneously target these pathways heightened growth inhibition in AML cells. Both genetic and pharmacologic inhibition of CCR2 sensitized AML cells to trametinib, suppressing proliferation while enhancing apoptosis. These findings underscore a new role for CCL2 in MEKi resistance, offering combination therapies as an avenue to circumvent this resistance. CONCLUSIONS: Our study demonstrates a compelling rationale for translating CCL2/CCR2 axis inhibitors in combination with MEK pathway-targeting therapies, as a potent strategy for combating drug resistance in AML. This approach has the potential to enhance the efficacy of treatments to improve AML patient outcomes.
Assuntos
Quimiocina CCL2 , Resistencia a Medicamentos Antineoplásicos , Leucemia Mieloide Aguda , Inibidores de Proteínas Quinases , Receptores CCR2 , Transdução de Sinais , Humanos , Leucemia Mieloide Aguda/tratamento farmacológico , Leucemia Mieloide Aguda/genética , Leucemia Mieloide Aguda/metabolismo , Leucemia Mieloide Aguda/patologia , Receptores CCR2/metabolismo , Receptores CCR2/antagonistas & inibidores , Receptores CCR2/genética , Resistencia a Medicamentos Antineoplásicos/genética , Quimiocina CCL2/metabolismo , Quimiocina CCL2/genética , Inibidores de Proteínas Quinases/farmacologia , Inibidores de Proteínas Quinases/uso terapêutico , Transdução de Sinais/efeitos dos fármacos , Linhagem Celular Tumoral , Proliferação de Células/efeitos dos fármacos , Animais , Piridonas/farmacologia , Piridonas/uso terapêutico , CamundongosRESUMO
The leaf-cutter ant fungal garden ecosystem is a naturally evolved model system for efficient plant biomass degradation. Degradation processes mediated by the symbiotic fungus Leucoagaricus gongylophorus are difficult to characterize due to dynamic metabolisms and spatial complexity of the system. Herein, we performed microscale imaging across 12-µm-thick adjacent sections of Atta cephalotes fungal gardens and applied a metabolome-informed proteome imaging approach to map lignin degradation. This approach combines two spatial multiomics mass spectrometry modalities that enabled us to visualize colocalized metabolites and proteins across and through the fungal garden. Spatially profiled metabolites revealed an accumulation of lignin-related products, outlining morphologically unique lignin microhabitats. Metaproteomic analyses of these microhabitats revealed carbohydrate-degrading enzymes, indicating a prominent fungal role in lignocellulose decomposition. Integration of metabolome-informed proteome imaging data provides a comprehensive view of underlying biological pathways to inform our understanding of metabolic fungal pathways in plant matter degradation within the micrometer-scale environment.
Assuntos
Lignina , Consórcios Microbianos , Lignina/metabolismo , Consórcios Microbianos/fisiologia , Animais , Formigas/metabolismo , Formigas/microbiologia , Ecossistema , Proteômica/métodos , Proteoma/metabolismo , SimbioseRESUMO
Acute myeloid leukemia is a poor-prognosis cancer commonly stratified by genetic aberrations, but these mutations are often heterogeneous and fail to consistently predict therapeutic response. Here, we combine transcriptomic, proteomic, and phosphoproteomic datasets with ex vivo drug sensitivity data to help understand the underlying pathophysiology of AML beyond mutations. We measure the proteome and phosphoproteome of 210 patients and combine them with genomic and transcriptomic measurements to identify four proteogenomic subtypes that complement existing genetic subtypes. We build a predictor to classify samples into subtypes and map them to a "landscape" that identifies specific drug response patterns. We then build a drug response prediction model to identify drugs that target distinct subtypes and validate our findings on cell lines representing various stages of quizartinib resistance. Our results show how multiomics data together with drug sensitivity data can inform therapy stratification and drug combinations in AML.
Assuntos
Leucemia Mieloide Aguda , Proteogenômica , Humanos , Proteômica/métodos , Leucemia Mieloide Aguda/tratamento farmacológico , Leucemia Mieloide Aguda/genética , Leucemia Mieloide Aguda/metabolismo , Genômica/métodos , MutaçãoRESUMO
Proteogenomics refers to the integration of comprehensive genomic, transcriptomic, and proteomic measurements from the same samples with the goal of fully understanding the regulatory processes converting genotypes to phenotypes, often with an emphasis on gaining a deeper understanding of disease processes. Although specific genetic mutations have long been known to drive the development of multiple cancers, gene mutations alone do not always predict prognosis or response to targeted therapy. The benefit of proteogenomics research is that information obtained from proteins and their corresponding pathways provides insight into therapeutic targets that can complement genomic information by providing an additional dimension regarding the underlying mechanisms and pathophysiology of tumors. This review describes the novel insights into tumor biology and drug resistance derived from proteogenomic analysis while highlighting the clinical potential of proteogenomic observations and advances in technique and analysis tools.
Assuntos
Medicina de Precisão , Proteogenômica , Humanos , Proteômica , Genômica , Espectrometria de MassasRESUMO
With advanced mass spectrometry (MS)-based proteomics, genome-scale proteome coverage can be achieved from bulk tissues. However, such bulk measurement lacks spatial resolution and obscures important tissue heterogeneity, which make it impossible for proteome mapping of tissue microenvironment. Here we report an integrated wet collection of single tissue voxel and Surfactant-assisted One-Pot voxel processing method termed wcSOP for robust label-free single voxel proteomics. wcSOP capitalizes on buffer droplet-assisted wet collection of single tissue voxel dissected by LCM into the PCR tube cap and MS-compatible surfactant-assisted one-pot voxel processing in the collection cap. This convenient method allows reproducible label-free quantification of â¼900 and â¼4,600 proteins for single voxel from fresh frozen human spleen tissue at 20 µm × 20 µm × 10 µm (close to single cells) and 200 µm × 200 µm × 10 µm (â¼100 cells), respectively. 100s-1000s of protein signatures with differential expression levels were identified to be spatially resolved between spleen red and white pulp regions depending on the voxel size. Region-specific signaling pathways were enriched from single voxel proteomics data. Antibody-based CODEX imaging was used to validate label-free MS quantitation for single voxel analysis. The wcSOP-MS method paves the way for routine robust single voxel proteomics and spatial proteomics.
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Type 1 diabetes (T1D) results from autoimmune destruction of ß cells. Insufficient availability of biomarkers represents a significant gap in understanding the disease cause and progression. We conduct blinded, two-phase case-control plasma proteomics on the TEDDY study to identify biomarkers predictive of T1D development. Untargeted proteomics of 2,252 samples from 184 individuals identify 376 regulated proteins, showing alteration of complement, inflammatory signaling, and metabolic proteins even prior to autoimmunity onset. Extracellular matrix and antigen presentation proteins are differentially regulated in individuals who progress to T1D vs. those that remain in autoimmunity. Targeted proteomics measurements of 167 proteins in 6,426 samples from 990 individuals validate 83 biomarkers. A machine learning analysis predicts if individuals would remain in autoimmunity or develop T1D 6 months before autoantibody appearance, with areas under receiver operating characteristic curves of 0.871 and 0.918, respectively. Our study identifies and validates biomarkers, highlighting pathways affected during T1D development.
Assuntos
Diabetes Mellitus Tipo 1 , Células Secretoras de Insulina , Humanos , Diabetes Mellitus Tipo 1/diagnóstico , Autoimunidade , Autoanticorpos , BiomarcadoresRESUMO
The need for a clinically accessible method with the ability to match protein activity within heterogeneous tissues is currently unmet by existing technologies. Our proteomics sample preparation platform, named microPOTS (Microdroplet Processing in One pot for Trace Samples), can be used to measure relative protein abundance in micron-scale samples alongside the spatial location of each measurement, thereby tying biologically interesting proteins and pathways to distinct regions. However, given the smaller pixel/voxel number and amount of tissue measured, standard mass spectrometric analysis pipelines have proven inadequate. Here we describe how existing computational approaches can be adapted to focus on the specific biological questions asked in spatial proteomics experiments. We apply this approach to present an unbiased characterization of the human islet microenvironment comprising the entire complex array of cell types involved while maintaining spatial information and the degree of the islet's sphere of influence. We identify specific functional activity unique to the pancreatic islet cells and demonstrate how far their signature can be detected in the adjacent tissue. Our results show that we can distinguish pancreatic islet cells from the neighboring exocrine tissue environment, recapitulate known biological functions of islet cells, and identify a spatial gradient in the expression of RNA processing proteins within the islet microenvironment.
Assuntos
Ilhotas Pancreáticas , Proteoma , Humanos , Proteoma/metabolismo , Ilhotas Pancreáticas/metabolismo , Espectrometria de MassasRESUMO
There is increasing interest in developing in-depth proteomic approaches for mapping tissue heterogeneity at a cell-type-specific level to better understand and predict the function of complex biological systems, such as human organs. Existing spatially resolved proteomics technologies cannot provide deep proteome coverages due to limited sensitivity and poor sample recovery. Herein, we seamlessly combined laser capture microdissection with a low-volume sample processing technology that includes a microfluidic device named microPOTS (Microdroplet Processing in One pot for Trace Samples), the multiplexed isobaric labelling, and a nanoflow peptide fractionation approach. The integrated workflow allowed to maximize proteome coverage of laser-isolated tissue samples containing nanogram proteins. We demonstrated the deep spatial proteomics can quantify more than 5,000 unique proteins from a small-sized human pancreatic tissue pixel (â¼60,000 µm2) and reveal unique islet microenvironments.
RESUMO
Despite their diminutive size, islets of Langerhans play a large role in maintaining systemic energy balance in the body. New technologies have enabled us to go from studying the whole pancreas to isolated whole islets, to partial islet sections, and now to islet substructures isolated from within the islet. Using a microfluidic nanodroplet-based proteomics platform coupled with laser capture microdissection and field asymmetric waveform ion mobility spectrometry, we present an in-depth investigation of protein profiles specific to features within the islet. These features include the islet-acinar interface vascular tissue, inner islet vasculature, isolated endocrine cells, whole islet with vasculature, and acinar tissue from around the islet. Compared to interface vasculature, unique protein signatures observed in the inner vasculature indicate increased innervation and intra-islet neuron-like crosstalk. We also demonstrate the utility of these data for identifying localized structure-specific drug-target interactions using existing protein/drug binding databases.
Assuntos
Ilhotas Pancreáticas , Ilhotas Pancreáticas/metabolismo , Proteômica/métodos , Proteínas/metabolismo , Microdissecção e Captura a LaserRESUMO
Acute Myeloid Leukemia (AML) affects 20,000 patients in the US annually with a five-year survival rate of approximately 25%. One reason for the low survival rate is the high prevalence of clonal evolution that gives rise to heterogeneous sub-populations of leukemic cells with diverse mutation spectra, which eventually leads to disease relapse. This genetic heterogeneity drives the activation of complex signaling pathways that is reflected at the protein level. This diversity makes it difficult to treat AML with targeted therapy, requiring custom patient treatment protocols tailored to each individual's leukemia. Toward this end, the Beat AML research program prospectively collected genomic and transcriptomic data from over 1000 AML patients and carried out ex vivo drug sensitivity assays to identify genomic signatures that could predict patient-specific drug responses. However, there are inherent weaknesses in using only genetic and transcriptomic measurements as surrogates of drug response, particularly the absence of direct information about phosphorylation-mediated signal transduction. As a member of the Clinical Proteomic Tumor Analysis Consortium, we have extended the molecular characterization of this cohort by collecting proteomic and phosphoproteomic measurements from a subset of these patient samples (38 in total) to evaluate the hypothesis that proteomic signatures can improve the ability to predict response to 26 drugs in AML ex vivo samples. In this work we describe our systematic, multi-omic approach to evaluate proteomic signatures of drug response and compare protein levels to other markers of drug response such as mutational patterns. We explore the nuances of this approach using two drugs that target key pathways activated in AML: quizartinib (FLT3) and trametinib (Ras/MEK), and show how patient-derived signatures can be interpreted biologically and validated in cell lines. In conclusion, this pilot study demonstrates strong promise for proteomics-based patient stratification to assess drug sensitivity in AML.
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Spatial proteomics holds great promise for revealing tissue heterogeneity in both physiological and pathological conditions. However, one significant limitation of most spatial proteomics workflows is the requirement of large sample amounts that blurs cell-type-specific or microstructure-specific information. In this study, we developed an improved sample preparation approach for spatial proteomics and integrated it with our previously-established laser capture microdissection (LCM) and microfluidics sample processing platform. Specifically, we developed a hanging drop (HD) method to improve the sample recovery by positioning a nanowell chip upside-down during protein extraction and tryptic digestion steps. Compared with the commonly-used sitting-drop method, the HD method keeps the tissue pixel away from the container surface, and thus improves the accessibility of the extraction/digestion buffer to the tissue sample. The HD method can increase the MS signal by 7 fold, leading to a 66% increase in the number of identified proteins. An average of 721, 1489, and 2521 proteins can be quantitatively profiled from laser-dissected 10 µm-thick mouse liver tissue pixels with areas of 0.0025, 0.01, and 0.04 mm2, respectively. The improved system was further validated in the study of cell-type-specific proteomes of mouse uterine tissues.
Assuntos
Proteoma , Proteômica , Animais , Microdissecção e Captura a Laser/métodos , Camundongos , Proteômica/métodos , Manejo de Espécimes/métodos , Fluxo de TrabalhoRESUMO
Cellular components are non-randomly arranged with respect to the shape and polarity of the whole cell.1-4 Patterning within cells can extend down to the level of individual proteins and mRNA.5,6 But how much of the proteome is actually localized with respect to cell polarity axes? Proteomics combined with cellular fractionation7-11 has shown that most proteins localize to one or more organelles but does not tell us how many proteins have a polarized localization with respect to the large-scale polarity axes of the intact cell. Genome-wide localization studies in yeast12-15 found that only a few percent of proteins have a localized position relative to the cell polarity axis defined by sites of polarized cell growth. Here, we describe an approach for analyzing protein distribution within a cell with a visibly obvious global patterning-the giant ciliate Stentor coeruleus.16,17 Ciliates, including Stentor, have highly polarized cell shapes with visible surface patterning.1,18 A Stentor cell is roughly 2 mm long, allowing a "proteomic dissection" in which microsurgery is used to separate cellular fragments along the anterior-posterior axis, followed by comparative proteomic analysis. In our analysis, 25% of the proteome, including signaling proteins, centrin/SFI proteins, and GAS2 orthologs, shows a polarized location along the cell's anterior-posterior axis. We conclude that a large proportion of all proteins are polarized with respect to global cell polarity axes and that proteomic dissection provides a simple and effective approach for spatial proteomics.
Assuntos
Cilióforos , Proteoma , Polaridade Celular/genética , Cilióforos/genética , Morfogênese/genética , Proteoma/metabolismo , Proteômica , Saccharomyces cerevisiaeRESUMO
Background: ACE2 is a key enzyme in the renin-angiotensin system (RAS) capable of balancing the RAS by metabolizing angiotensin II (AngII). First described in cardiac tissue, abundance of ACE2 is highest in the kidney, and it is also expressed in several extrarenal tissues. Previously, we reported an association between enhanced susceptibility to hypertension and elevated renal AngII levels in global ACE2-knockout mice. Methods: To examine the effect of ACE2 expressed in the kidney, relative to extrarenal expression, on the development of hypertension, we used a kidney crosstransplantation strategy with ACE2-KO and WT mice. In this model, both native kidneys are removed and renal function is provided entirely by the transplanted kidney, such that four experimental groups with restricted ACE2 expression are generated: WTâWT (WT), KOâWT (KidneyKO), WTâKO (SystemicKO), and KOâKO (TotalKO). Additionally, we used nanoscale mass spectrometry-based proteomics to identify ACE2 fragments in early glomerular filtrate of mice. Results: Although significant differences in BP were not detected, a major finding of our study is that shed or soluble ACE2 (sACE2) was present in urine of KidneyKO mice that lack renal ACE2 expression. Detection of sACE2 in the urine of KidneyKO mice during AngII-mediated hypertension suggests that sACE2 originating from extrarenal tissues can reach the kidney and be excreted in urine. To confirm glomerular filtration of ACE2, we used micropuncture and nanoscale proteomics to detect peptides derived from ACE2 in the Bowman's space. Conclusions: Our findings suggest that both systemic and renal tissues may contribute to sACE2 in urine, identifying the kidney as a major site for ACE2 actions. Moreover, filtration of sACE2 into the lumen of the nephron may contribute to the pathophysiology of kidney diseases characterized by disruption of the glomerular filtration barrier.
Assuntos
Enzima de Conversão de Angiotensina 2 , Hipertensão , Rim , Sistema Renina-Angiotensina , Animais , Camundongos , Angiotensina II/metabolismo , Angiotensina II/farmacologia , Enzima de Conversão de Angiotensina 2/genética , Enzima de Conversão de Angiotensina 2/metabolismo , Hipertensão/genética , Hipertensão/metabolismo , Rim/metabolismo , Camundongos Knockout , Peptidil Dipeptidase A/genética , Peptidil Dipeptidase A/metabolismo , Peptidil Dipeptidase A/farmacologia , Sistema Renina-Angiotensina/genética , Sistema Renina-Angiotensina/fisiologiaRESUMO
Global and phosphoproteome profiling has demonstrated great utility for the analysis of clinical specimens. One barrier to the broad clinical application of proteomic profiling is the large amount of biological material required, particularly for phosphoproteomicsâcurrently on the order of 25 mg wet tissue weight. For hematopoietic cancers such as acute myeloid leukemia (AML), the sample requirement is ≥10 million peripheral blood mononuclear cells (PBMCs). Across large study cohorts, this requirement will exceed what is obtainable for many individual patients/time points. For this reason, we were interested in the impact of differential peptide loading across multiplex channels on proteomic data quality. To achieve this, we tested a range of channel loading amounts (approximately the material obtainable from 5E5, 1E6, 2.5E6, 5E6, and 1E7 AML patient cells) to assess proteome coverage, quantification precision, and peptide/phosphopeptide detection in experiments utilizing isobaric tandem mass tag (TMT) labeling. As expected, fewer missing values were observed in TMT channels with higher peptide loading amounts compared to lower loadings. Moreover, channels with a lower loading have greater quantitative variability than channels with higher loadings. A statistical analysis showed that decreased loading amounts result in an increase in the type I error rate. We then examined the impact of differential loading on the detection of known differences between distinct AML cell lines. Similar patterns of increased data missingness and higher quantitative variability were observed as loading was decreased resulting in fewer statistical differences; however, we found good agreement in features identified as differential, demonstrating the value of this approach.
Assuntos
Fosfopeptídeos , Proteômica/métodos , Proteômica/normas , Espectrometria de Massas em Tandem/métodos , Espectrometria de Massas em Tandem/normas , Células Cultivadas , Cromatografia de Afinidade , Confiabilidade dos Dados , Humanos , Marcação por Isótopo , Leucócitos Mononucleares/química , Fosfopeptídeos/análise , Fosfopeptídeos/química , Fosfopeptídeos/isolamento & purificaçãoRESUMO
Despite wide use of anti-vascular endothelial growth factor (VEGF) therapy for many solid cancers, most individuals become resistant to this therapy, leading to disease progression. Therefore, new biomarkers and strategies for blocking adaptive resistance of cancer to anti-VEGF therapy are needed. As described here, we demonstrate that cancer-derived small extracellular vesicles package increasing quantities of VEGF and other factors in response to anti-VEGF therapy. The packaging process of VEGF into small extracellular vesicles (EVs) is mediated by the tetraspanin CD63. Furthermore, small EV-VEGF (eVEGF) is not accessible to anti-VEGF antibodies and can trigger intracrine VEGF signaling in endothelial cells. eVEGF promotes angiogenesis and enhances tumor growth despite bevacizumab treatment. These data demonstrate a mechanism where VEGF is partitioned into small EVs and promotes tumor angiogenesis and progression. These findings have clinical implications for biomarkers and therapeutic strategies for ovarian cancer.
Assuntos
Vesículas Extracelulares/metabolismo , Tetraspanina 30/metabolismo , Fator A de Crescimento do Endotélio Vascular/antagonistas & inibidores , Idoso , Animais , Bevacizumab/farmacologia , Bevacizumab/uso terapêutico , Linhagem Celular Tumoral , Proliferação de Células , Modelos Animais de Doenças , Vesículas Extracelulares/ultraestrutura , Feminino , Humanos , Camundongos , Camundongos Nus , Pessoa de Meia-Idade , Neovascularização Patológica/metabolismo , Neovascularização Patológica/patologia , Neoplasias Ovarianas/tratamento farmacológico , Isoformas de Proteínas/metabolismo , Transdução de Sinais , Fator A de Crescimento do Endotélio Vascular/metabolismo , Receptor 2 de Fatores de Crescimento do Endotélio Vascular/metabolismoRESUMO
Mass-spectrometry-based proteomic analysis is a powerful approach for discovering new disease biomarkers. However, certain critical steps of study design such as cohort selection, evaluation of statistical power, sample blinding and randomization, and sample/data quality control are often neglected or underappreciated during experimental design and execution. This tutorial discusses important steps for designing and implementing a liquid-chromatography-mass-spectrometry-based biomarker discovery study. We describe the rationale, considerations and possible failures in each step of such studies, including experimental design, sample collection and processing, and data collection. We also provide guidance for major steps of data processing and final statistical analysis for meaningful biological interpretations along with highlights of several successful biomarker studies. The provided guidelines from study design to implementation to data interpretation serve as a reference for improving rigor and reproducibility of biomarker development studies.
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Espectrometria de Massas/métodos , Proteínas/química , Proteômica/métodos , Biomarcadores/química , Humanos , Reprodutibilidade dos TestesRESUMO
Our study details the stepwise evolution of gilteritinib resistance in FLT3-mutated acute myeloid leukemia (AML). Early resistance is mediated by the bone marrow microenvironment, which protects residual leukemia cells. Over time, leukemia cells evolve intrinsic mechanisms of resistance, or late resistance. We mechanistically define both early and late resistance by integrating whole-exome sequencing, CRISPR-Cas9, metabolomics, proteomics, and pharmacologic approaches. Early resistant cells undergo metabolic reprogramming, grow more slowly, and are dependent upon Aurora kinase B (AURKB). Late resistant cells are characterized by expansion of pre-existing NRAS mutant subclones and continued metabolic reprogramming. Our model closely mirrors the timing and mutations of AML patients treated with gilteritinib. Pharmacological inhibition of AURKB resensitizes both early resistant cell cultures and primary leukemia cells from gilteritinib-treated AML patients. These findings support a combinatorial strategy to target early resistant AML cells with AURKB inhibitors and gilteritinib before the expansion of pre-existing resistance mutations occurs.
Assuntos
Compostos de Anilina/farmacologia , Aurora Quinase B/metabolismo , Biomarcadores Tumorais/metabolismo , Resistencia a Medicamentos Antineoplásicos , Regulação Neoplásica da Expressão Gênica/efeitos dos fármacos , Leucemia Mieloide Aguda/tratamento farmacológico , Pirazinas/farmacologia , Microambiente Tumoral , Aurora Quinase B/genética , Biomarcadores Tumorais/genética , Exoma , Humanos , Leucemia Mieloide Aguda/genética , Leucemia Mieloide Aguda/patologia , Metaboloma , Inibidores de Proteínas Quinases/farmacologia , Proteoma , Células Tumorais CultivadasRESUMO
MOTIVATION: Viruses infect, reprogram and kill microbes, leading to profound ecosystem consequences, from elemental cycling in oceans and soils to microbiome-modulated diseases in plants and animals. Although metagenomic datasets are increasingly available, identifying viruses in them is challenging due to poor representation and annotation of viral sequences in databases. RESULTS: Here, we establish efam, an expanded collection of Hidden Markov Model (HMM) profiles that represent viral protein families conservatively identified from the Global Ocean Virome 2.0 dataset. This resulted in 240 311 HMM profiles, each with at least 2 protein sequences, making efam >7-fold larger than the next largest, pan-ecosystem viral HMM profile database. Adjusting the criteria for viral contig confidence from 'conservative' to 'eXtremely Conservative' resulted in 37 841 HMM profiles in our efam-XC database. To assess the value of this resource, we integrated efam-XC into VirSorter viral discovery software to discover viruses from less-studied, ecologically distinct oxygen minimum zone (OMZ) marine habitats. This expanded database led to an increase in viruses recovered from every tested OMZ virome by â¼24% on average (up to â¼42%) and especially improved the recovery of often-missed shorter contigs (<5 kb). Additionally, to help elucidate lesser-known viral protein functions, we annotated the profiles using multiple databases from the DRAM pipeline and virion-associated metaproteomic data, which doubled the number of annotations obtainable by standard, single-database annotation approaches. Together, these marine resources (efam and efam-XC) are provided as searchable, compressed HMM databases that will be updated bi-annually to help maximize viral sequence discovery and study from any ecosystem. AVAILABILITY AND IMPLEMENTATION: The resources are available on the iVirus platform at (doi.org/10.25739/9vze-4143). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.