Publications by authors named "Marco Hadisurya"

Pancreatic cancer has the worst prognosis of all common tumors. Earlier cancer diagnosis could increase survival rates and better assessment of metastatic disease could improve patient care. As such, there is an urgent need to develop biomarkers to diagnose this deadly malignancy.

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Signal-dependent RNA polymerase II (RNA Pol II) productive elongation is an integral component of gene transcription, including that of immediate early genes (IEGs) induced by neuronal activity. However, it remains unclear how productively elongating RNA Pol II overcomes nucleosomal barriers. Using RNAi, three degraders, and several small-molecule inhibitors, we show that the mammalian switch/sucrose non-fermentable (SWI/SNF) complex of neurons (neuronal BRG1/BRM-associated factor or nBAF) is required for activity-induced transcription of neuronal IEGs, including Arc.

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Correctly identifying perturbed biological pathways is a critical step in uncovering basic disease mechanisms and developing much-needed therapeutic strategies. However, whether current tools are optimal for unbiased discovery of relevant pathways remains unclear. Here, we create "Benchmark" to critically evaluate existing tools and find that most function sub-optimally.

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Osmotic stress significantly hampers plant growth and crop yields, emphasizing the need for a thorough comprehension of the underlying molecular responses. Previous research has demonstrated that osmotic stress rapidly induces calcium influx and signaling, along with the activation of a specific subset of protein kinases, notably the Raf-like protein (RAF)-sucrose nonfermenting-1-related protein kinase 2 (SnRK2) kinase cascades within minutes. However, the intricate interplay between calcium signaling and the activation of RAF-SnRK2 kinase cascades remains elusive.

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Article Synopsis
  • The study introduces a new method to track how metabolic enzymes respond to drugs by analyzing these enzymes in extracellular vesicles (EVs) in the bloodstream, which is important for improving personalized medicine.* -
  • Utilizing mass spectrometry and a multiplexed isotopic labeling technique, researchers quantified 34 proteins related to drug processing and found that certain drug treatments led to increased levels of specific enzymes in EVs, confirming that these changes reflected those in liver cells.* -
  • The findings suggest that monitoring metabolic enzymes in circulating EVs can serve as a reliable indicator for drug behavior, potentially revolutionizing drug development processes for more tailored therapies.*
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Background: In patients with severe acute respiratory distress syndrome (ARDS) associated with sepsis, lung recovery is considerably delayed, and mortality is much high. More insight into the process of lung regeneration in ARDS patients is needed. Exosomes are important cargos for intercellular communication by serving as autocrine and/or paracrine.

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Extracellular vesicles (EVs) have emerged as a promising source of disease biomarkers for noninvasive early stage diagnoses, but a bottleneck in EV sample processing restricts their immense potential in clinical applications. Existing methods are limited by a low EV yield and integrity, slow processing speeds, low sample capacity, and poor recovery efficiency. We aimed to address these issues with a high-throughput automated workflow for EV isolation, EV lysis, protein extraction, and protein denaturation.

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Extracellular vesicle (EV) proteomics emerges as an effective tool for discovering potential biomarkers for disease diagnosis, monitoring, and therapeutics. However, the current workflow of mass spectrometry-based EV proteome analysis is not fully compatible in a clinical setting due to inefficient EV isolation methods and a tedious sample preparation process. To streamline and improve the efficiency of EV proteome analysis, here we introduce a one-pot analytical pipeline integrating a robust EV isolation approach, EV total recovery and purification (EVtrap), with protein sample preparation, to detect urinary EV proteome.

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Many biological processes are regulated through dynamic protein phosphorylation. Monitoring disease-relevant phosphorylation events in circulating biofluids is highly appealing but also technically challenging. We introduce here a functionally tunable material and a strategy, extracellular vesicles to phosphoproteins (EVTOP), which achieves one-pot extracellular vesicles (EVs) isolation, extraction, and digestion of EV proteins, and enrichment of phosphopeptides, with only a trace amount of starting biofluids.

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Extracellular vesicles (EVs) have emerged as a valuable source for disease biomarkers and an alternative drug delivery system due to their ability to carry cargo and target specific cells. Proper isolation, identification, and analytical strategy are required for evaluating their potential in diagnostics and therapeutics. Here, a method is detailed to isolate plasma EVs and analyze their proteomic profiling, combining EVtrap-based high-recovery EV isolation, phase-transfer surfactant method for protein extraction, and mass spectrometry qualitative and quantitative strategies for EV proteome characterization.

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Background: Mutations in the leucine-rich repeat kinase 2 (LRRK2) gene have been recognized as genetic risk factors for Parkinson's disease (PD). However, compared to cancer, fewer genetic mutations contribute to the cause of PD, propelling the search for protein biomarkers for early detection of the disease.

Methods: Utilizing 138 urine samples from four groups, healthy individuals (control), healthy individuals with G2019S mutation in the LRRK2 gene (non-manifesting carrier/NMC), PD individuals without G2019S mutation (idiopathic PD/iPD), and PD individuals with G2019S mutation (LRRK2 PD), we applied a proteomics strategy to determine potential diagnostic biomarkers for PD from urinary extracellular vesicles (EVs).

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Translating the research capability and knowledge in cancer signaling into clinical settings has been slow and ineffective. Recently, extracellular vesicles (EVs) have emerged as a promising source for developing disease phosphoprotein markers to monitor disease status. This study focuses on the development of a robust data-independent acquisition (DIA) using mass spectrometry to profile urinary EV phosphoproteomics for renal cell cancer (RCC) grades differentiation.

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Pancreatic cancer has the worst prognosis of all common tumors. Earlier cancer diagnosis could increase survival rates and better assessment of metastatic disease could improve patient care. As such, there is an urgent need to develop biomarkers to diagnose this deadly malignancy.

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Current strategies in circulating tumor cell (CTC) isolation in pancreatic cancer heavily rely on the EpCAM and cytokeratin cell status. EpCAM is generally not considered a good marker given its transitory change during Epithelial to Mesenchymal Transition (EMT) or reverse EMT. There is a need to identify other surface markers to capture the complete repertoire of PDAC CTCs.

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Article Synopsis
  • Pancreatic cancer is challenging to diagnose early due to the absence of reliable biomarkers, leading to late-stage detection and poor patient outcomes.
  • Researchers used high-throughput mass spectrometry to analyze extracellular vesicles (EVs) from pancreatic cancer cells under various conditions, identifying candidate biomarkers KIF5B and SFRP2.
  • The study found that the EVs from co-cultured cells have distinct protein profiles and impact the microbiome, indicating KIF5B and SFRP2 could be valuable for early detection and understanding disease progression.
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The invasive nature and the pain caused to patients inhibit the routine use of tissue biopsy-based procedures for cancer diagnosis and surveillance. The analysis of extracellular vesicles (EVs) from biofluids has recently gained significant traction in the liquid biopsy field. EVs offer an essential "snapshot" of their precursor cells in real time and contain an information-rich collection of nucleic acids, proteins, lipids, and so on.

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