Comparative proteomic analysis of saliva from chewing tobacco users and oral cancer patients reveals shared biomarkers: A case control observational study.

Objective: The aim of this study was to compare the salivary proteomic profile of smokeless tobacco users with that of non-users and oral cancer patients using Liquid Chromatography-Mass Spectrometry/ Mass Spectrometry (LC-MS/MS).

Methods: Saliva samples from 65 participants were collected in three groups: control (25 participants), smokeless tobacco users (25 participants), and oral cancer (15 participants).

Results: The analysis revealed 343 protein groups with significantly altered abundance in the saliva samples (P < 0.

Proteomic and metabolomic signatures of U87 glioblastoma cells treated with cisplatin and/or paclitaxel.

Background: Glioblastoma (GBM) is a primary malignancy of the central nervous system and is classified as a grade IV astrocytoma by the World Health Organization (WHO). Although GBM rarely metastasizes, its prognosis remains poor. Moreover, the standard treatment for GBM, temozolomide (TMZ), is associated with chemoresistance, which is a major factor behind GBM-related deaths.

Serum multi-omics analysis in hindlimb unloading mice model: Insights into systemic molecular changes and potential diagnostic and therapeutic biomarkers.

Microgravity, in space travel and prolonged bed rest conditions, induces cardiovascular deconditioning along with skeletal muscle mass loss and weakness. The findings of microgravity research may also aid in the understanding and treatment of human health conditions on Earth such as muscle atrophy, and cardiovascular diseases. Due to the paucity of biomarkers and the unknown underlying mechanisms of cardiovascular and skeletal muscle deconditioning in these environments, there are insufficient diagnostic and preventative measures.

Multi-Omics Profiling of Grown on Solid Versus Liquid Media.

is a common pathogenic fungus that presents a challenge to healthcare facilities. It can switch between a yeast cell form that diffuses through the bloodstream to colonize internal organs and a filamentous form that penetrates host mucosa. Understanding the pathogen's strategies for environmental adaptation and, ultimately, survival, is crucial.

Selected Gut Bacteria from Water Monitor Lizard Exhibit Effects against Pathogenic Belonging to the T4 Genotype.

Water monitor lizards (WMLs) reside in unhygienic and challenging ecological surroundings and are routinely exposed to various pathogenic microorganisms. It is possible that their gut microbiota produces substances to counter microbial infections. Here we determine whether selected gut bacteria of water monitor lizards (WMLs) possess anti-amoebic properties using of the T4 genotype.

Ramadan diurnal intermittent fasting is associated with significant plasma metabolomics changes in subjects with overweight and obesity: A prospective cohort study.

Introduction: During the holy month of Ramadan, adult healthy Muslims are mandated to abstain from dawn to sunset, with free eating at night hours that may extend up to 12 h. The current work was designed to investigate the metabolomics changes incurred upon the observance of Ramadan diurnal intermittent fasting (RDIF).

Methods: Twenty-five metabolically healthy participants with overweight and obesity (7 females and 18 males, with a mean age of 39.

Skin Cancer Metabolic Profile Assessed by Different Analytical Platforms.

Skin cancer, including malignant melanoma (MM) and keratinocyte carcinoma (KC), historically named non-melanoma skin cancers (NMSC), represents the most common type of cancer among the white skin population. Despite decades of clinical research, the incidence rate of melanoma is increasing globally. Therefore, a better understanding of disease pathogenesis and resistance mechanisms is considered vital to accomplish early diagnosis and satisfactory control.

Evaluation of Two Simultaneous Metabolomic and Proteomic Extraction Protocols Assessed by Ultra-High-Performance Liquid Chromatography Tandem Mass Spectrometry.

Untargeted multi-omics analysis of plasma is an emerging tool for the identification of novel biomarkers for evaluating disease prognosis, and for developing a better understanding of molecular mechanisms underlying human disease. The successful application of metabolomic and proteomic approaches relies on reproducibly quantifying a wide range of metabolites and proteins. Herein, we report the results of untargeted metabolomic and proteomic analyses from blood plasma samples following analyte extraction by two frequently-used solvent systems: chloroform/methanol and methanol-only.

Mass spectroscopy-based proteomics and metabolomics analysis of triple-positive breast cancer cells treated with tamoxifen and/or trastuzumab.

Purpose: HER2-enriched breast cancer with high levels of hormone receptor expression, known as "triple positive" breast cancer, may represent a new entity with a relatively favourable prognosis against which the combination of chemotherapy, HER-2 inhibition, and endocrine treatment may be considered overtreatment. We explored the effect of the anticancer drugs tamoxifen and trastuzumab, both separately and in combination, on the integrated proteomic and metabolic profile of "triple positive" breast cancer cells (BT-474).

Method: We employed ultra-high-performance liquid chromatography-quadrupole time of flight mass spectrometry using a Bruker timsTOF to investigate changes in BT-474 cell line treated with either tamoxifen, trastuzumab or a combination.

Multi-Omics Analysis Revealed a Significant Alteration of Critical Metabolic Pathways Due to Sorafenib-Resistance in Hep3B Cell Lines.

Hepatocellular carcinoma (HCC) is the second prominent cause of cancer-associated death worldwide. Usually, HCC is diagnosed in advanced stages, wherein sorafenib, a multiple target tyrosine kinase inhibitor, is used as the first line of treatment. Unfortunately, resistance to sorafenib is usually encountered within six months of treatment.

Unveiling the mechanism of action of nature-inspired anti-cancer compounds using a multi-omics approach.

The 2020 global cancer registry has ranked breast cancer (BCa) as the most commonly diagnosed type of cancer and the most common cause of cancer-related deaths in women worldwide. Increasing resistance and significant side effects continue to limit the efficacy of anti-BCa drugs, hence the need to identify new drug targets and to develop novel compounds to overcome these limitations. Nature-inspired anti-cancer compounds are becoming increasingly popular since they often provide a relatively safe and effective alternative.

Proteomic Approaches to Unravel Mechanisms of Antibiotic Resistance and Immune Evasion of Bacterial Pathogens.

The profound effects of and distress caused by the global COVID-19 pandemic highlighted what has been known in the health sciences a long time ago: that bacteria, fungi, viruses, and parasites continue to present a major threat to human health. Infectious diseases remain the leading cause of death worldwide, with antibiotic resistance increasing exponentially due to a lack of new treatments. In addition to this, many pathogens share the common trait of having the ability to modulate, and escape from, the host immune response.

Untargeted Metabolomics of Breast Cancer Cells MCF-7 and SkBr3 Treated With Tamoxifen/Trastuzumab.

Background/aim: Trastuzumab and tamoxifen are two of the most widely prescribed anti-cancer drugs for breast cancer (BC). To date, few studies have explored the impact of anticancer drugs on metabolic pathways in BC. Metabolomics is an emerging technology that can identify new biomarkers for tracking therapy response and novel therapeutic targets.

Cell Wall Proteomics Reveal Phenotypic Adaption of Drug-Resistant to Subinhibitory Rifampicin Exposure.

Despite the availability of effective drug treatment, (Mtb), the causative agent of TB disease, kills ~1. 5 million people annually, and the rising prevalence of drug resistance increasingly threatens to worsen this plight. We previously showed that sublethal exposure to the frontline anti-TB drug, rifampicin, resulted in substantial adaptive remodeling of the proteome of the model organism, , in the drug-sensitive mc155 strain [wild type (WT)].

Gel-free sample preparation techniques and bioinformatic enrichment analysis to in depth characterise the cell wall proteome of mycobacteria.

The comprehensive characterisation of the cell wall proteome of mycobacteria is of considerable relevance to both the discovery of new drug targets as well as to the design of new vaccines against . However, due to its extremely hydrophobic nature, the coverage of proteomic studies of this subcellular compartment is still far from complete. Here, we report novel gel-free cell wall sample preparation procedures and quantitative LC-MS/MS measurements on a Q Exactive mass spectrometer.

Cell wall enrichment unveils proteomic changes in the cell wall during treatment of Mycobacterium smegmatis with sub-lethal concentrations of rifampicin.

Understanding the cell wall of mycobacteria is crucial for improving drug design or identifying new antigens suitable to vaccination. Yet this remains problematic due to the complexity of the cell wall composition. In this study, we successfully developed gel-free approaches to study cell wall proteins in Mycobacterium smegmatis.

A temporal proteome dynamics study reveals the molecular basis of induced phenotypic resistance in Mycobacterium smegmatis at sub-lethal rifampicin concentrations.

In the last 40 years only one new antitubercular drug has been approved, whilst resistance to current drugs, including rifampicin, is spreading. Here, we used the model organism Mycobacterium smegmatis to study mechanisms of phenotypic mycobacterial resistance, employing quantitative mass spectrometry-based proteomics to investigate the temporal effects of sub-lethal concentrations of rifampicin on the mycobacterial proteome at time-points corresponding to early response, onset of bacteriostasis and early recovery. Across 18 samples, a total of 3,218 proteins were identified from 31,846 distinct peptides averaging 16,250 identified peptides per sample.