Meta-analysis of the serum/plasma proteome identifies significant associations between COVID-19 with Alzheimer's/Parkinson's diseases.

In recent years, we have seen the widespread devastations and serious health complications manifested by COVID-19 globally. Although we have effectively controlled the pandemic, uncertainties persist regarding its potential long-term effects, including prolonged neurological issues. To gain comprehensive insights, we conducted a meta-analysis of mass spectrometry-based proteomics data retrieved from different studies with a total of 538 COVID-19 patients and 523 healthy controls. The meta-analysis revealed that top-enriched pathways were associated with neurological disorders, including Alzheimer's (AD) and Parkinson's disease (PD). Further analysis confirmed a direct correlation in the expression patterns of 24 proteins involved in Alzheimer's and 23 proteins in Parkinson's disease with COVID-19. Protein-protein interaction network and cluster analysis identified SNCA as a hub protein, a known biomarker for Parkinson's disease, in both AD and PD. To the best of our knowledge, this is the first meta-analysis study providing proteomic profiling evidence linking COVID-19 to neurological complications.

Exploring Nanocluster Potential Energy Surfaces via Deep Reinforcement Learning: Strategies for Global Minimum Search.

The search for global minimum (GM) configurations in nanoclusters is complicated by intricate potential energy landscapes replete with numerous local minima. The complexity of these landscapes escalates with increasing cluster size and compositional diversity. Evolutionary algorithms, such as genetic algorithms, are hampered by slow convergence rates and a propensity for prematurely settling on suboptimal solutions. Likewise, the basin hopping technique faces difficulties in navigating these complex landscapes effectively, particularly at larger scales. These challenges highlight the need for more sophisticated methodologies to efficiently scan the potential energy surfaces of nanoclusters. In response, our research has developed a novel deep reinforcement learning (DRL) framework specifically designed to explore the potential energy surfaces (PES) of nanoclusters, aiming to identify the GM configurations along with other low-energy states. This study demonstrates the framework's effectiveness in managing various nanocluster types, including both mono- and multimetallic compositions, and its proficiency in navigating complex energy landscapes. The model is characterized by remarkable adaptability and sustained efficiency, even as cluster sizes and feature vector dimensions increase. The demonstrated adaptability of DRL in this context underscores its considerable potential in materials science, particularly for the efficient discovery and optimization of novel nanomaterials. To the best of our knowledge, this is the first DRL framework designed for the GM search in nanoclusters, representing a significant innovation in the field.

Computational identification of potential bioactive compounds from Triphala against alcoholic liver injury by targeting alcohol dehydrogenase.

Alcoholic liver injury resulting from excessive alcohol consumption is a significant social concern. Alcohol dehydrogenase (ADH) plays a critical role in the conversion of alcohol to acetaldehyde, leading to tissue damage. The management of alcoholic liver injury encompasses nutritional support and, in severe cases liver transplantation, but potential adverse effects exist, and effective medications are currently unavailable. Natural products with their potential benefits and historical use in traditional medicine emerge as promising alternatives. Triphala, a traditional polyherbal formula demonstrates beneficial effects in addressing diverse health concerns, with a notable impact on treating alcoholic liver damage through enhanced liver metabolism. The present study aims to identify potential active phytocompounds in Triphala targeting ADH to prevent alcoholic liver injury. Screening 119 phytocompounds from the Triphala formulation revealed 62 of them showing binding affinity to the active site of the ADH1B protein. Promising lipid-like molecule from Terminalia bellirica, (4aS, 6aR, 6aR, 6bR, 7R, 8aR, 9R, 10R, 11R, 12aR, 14bS)-7, 10, 11-trihydroxy-9-(hydroxymethyl)-2, 2, 6a, 6b, 9, 12a-hexamethyl-1, 3, 4, 5, 6, 6a, 7, 8, 8a, 10, 11, 12, 13, 14b-tetradecahydropicene-4a-carboxylic acid showed high binding efficiency to a competitive ADH inhibitor, 4-Methylpyrazole. Pharmacokinetic analysis further confirmed the drug-likeness and non-hepatotoxicity of the top-ranked compound. Molecular dynamics simulation and MM-PBSA studies revealed the stability of the docked complexes with minimal fluctuation and consistency of the hydrogen bonds throughout the simulation. Together, computational investigations suggest that (4aS, 6aR, 6aR, 6bR, 7R, 8aR, 9R, 10R, 11R, 12aR, 14bS)-7, 10, 11-trihydroxy-9-(hydroxymethyl)-2, 2, 6a, 6b, 9, 12a-hexamethyl-1, 3, 4, 5, 6, 6a, 7, 8, 8a, 10, 11, 12, 13, 14b-tetradecahydropicene-4a-carboxylic acid from the Triphala formulation holds promise as an ADH inhibitor, suggesting an alternative therapy for alcoholic liver injury.

Potential protein kinase inhibitors that target G-quadruplex DNA structures in the human telomeric regions.

Telomeric regions contain Guanine-rich sequences arranged in a planar manner and connected by Hoogsteen hydrogen bonds that can fold into G-quadruplex (G4) DNA structures, and can be stabilized by monovalent metal cations. The presence of G4 DNA holds significance in cancer-related processes, especially due to their regulatory potential at transcriptional and translational levels of oncogene and tumor suppressor genes. The objective of this current research is to explore the evolving realm of FDA-approved protein kinase inhibitors, with a specific emphasis on their capacity to stabilize the G4 DNA structures formed at the human telomeric regions. This involves investigating the possibility of repurposing FDA-approved protein kinase inhibitors as a novel approach for targeting multiple cancer types. In this context, we have selected 16 telomeric G4 DNA structures as targets and 71 FDA-approved small-molecule protein kinase inhibitors as ligands. To investigate their binding affinities, molecular docking of human telomeric G4 DNA with nuclear protein kinase inhibitors and their corresponding co-crystalized ligands were performed. We found that Ponatinib and Lapatinib interact with all the selected G4 targets, the binding free energy calculations, and molecular dynamic simulations confirm their binding efficacy and stability. Thus, it is hypothesized that Ponatinib and Lapatinib may stabilize human telomeric G4 DNA in addition to their ability to inhibit BCR-ABL and the other members of the EGFR family. As a result, we also hypothesize that the stabilization of G4 DNA might represent an additional underlying mechanism contributing to their efficacy in exerting anti-cancer effects.

Targeting AFP-RARß complex formation: a potential strategy for treating AFP-positive hepatocellular carcinoma.

Alpha-fetoprotein (AFP) is a glycoprotein primarily expressed during embryogenesis, with declining levels postnatally. Elevated AFP levels correlate with pathological conditions such as liver fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). Recent investigations underscore AFP's intracellular role in HCC progression, wherein it forms complexes with proteins like Phosphatase and tensin homolog (PTEN), Caspase 3 (CASP3), and Retinoic acid receptors and Retinoid X receptors (RAR/RXR). RAR and RXR regulate gene expression linked to cell death and tumorigenesis in normal physiology. AFP impedes RAR/RXR dimerization, nuclear translocation, and function, promoting gene expression favoring cancer progression in HCC that provoked us to target AFP as a drug candidate. Despite extensive studies, inhibitors targeting AFP to disrupt complex formation and activities remain scarce. In this study, employing protein-protein docking, amino acid residues involved in AFP-RARß interaction were identified, guiding the definition of AFP's active site for potential inhibitor screening. Currently, kinase inhibitors play a significant role in cancer treatment and, the present study explores the potential of repurposing FDA-approved protein kinase inhibitors to target AFP. Molecular docking with kinase inhibitors revealed Lapatinib as a candidate drug of the AFP-RARß complex. Molecular dynamics simulations and binding energy calculations, employing Mechanic/Poisson-Boltzmann Surface Area (MM-PBSA), confirmed Lapatinib's stability with AFP. The study suggests Lapatinib's potential in disrupting the AFP-RARß complex, providing a promising avenue for treating molecularly stratified AFP-positive HCC or its early stages.

Incidence and prevalence of chronic pulmonary aspergillosis in patients with post-tuberculosis lung abnormality: Results from a community survey in North India.

BACKGROUND: Post-tuberculosis lung abnormality (PTLA) is the most common risk factor for developing chronic pulmonary aspergillosis (CPA). However, the prevalence and incidence of CPA in PTLA patients in India remain unknown. OBJECTIVES: We aimed to ascertain the incidence and prevalence of CPA in subjects with PTLA. METHODS: We identified a cohort of pulmonary tuberculosis who completed anti-tuberculosis therapy (ATT) before November 2019 from the records of the 12 tuberculosis treatment centers attached to the national program. We recorded the clinical and demographic details. We performed computed tomography (CT) of the chest and estimated serum A. fumigatus-specific IgG. We categorised subjects as PTLA with or without CPA using a composite of clinical, radiological, and microbiological features. We resurveyed the subjects at 6 months (or earlier) for the presence of new symptoms. We calculated the prevalence and the incidence rate (per 100-person years) of CPA. RESULTS: We included 117 subjects with PTLA, with a median of 3 years after ATT completion. Eleven subjects had CPA in the initial survey, and one additional case developed CPA during the second survey. The prevalence of CPA in PTLA subjects was 10.3% (12/117). The total observation period was 286.7 person-years. The median (interquartile range) time to develop CPA after ATT completion was 12.5 (5-36.7) months. We found the CPA incidence rate (95% confidence interval) of 4.2 (1.8-6.5) per 100-person years. CONCLUSION: Chronic pulmonary aspergillosis complicates 10% of PTLA subjects after successful outcomes with ATT. Four new CPA cases may develop per 100-persons years of observation after ATT completion. We suggest screening patients with PTLA who develop new symptoms for CPA.

Mining proteomics data to extract post-translational modifications associated with gastric cancer.

Gastric cancers are highly heterogeneous, deep-seated tumours associated with late diagnosis and poor prognosis. Post-translational modifications (PTMs) of proteins are known to be well-associated with oncogenesis and metastasis in most cancers. Several enzymes which drive PTMs have also been used as theranostics in cancers of the breast, ovary, prostate and bladder. However, there is limited data on PTMs in gastric cancers. Considering that experimental protocols for simultaneous analysis of multiple PTMs are being explored, a data-driven approach involving reanalysis of mass spectrometry-derived data is useful in cataloguing altered PTMs. We subjected publicly available mass spectrometry data on gastric cancer to an iterative searching strategy for fetching PTMs including phosphorylation, acetylation, citrullination, methylation and crotonylation. These PTMs were catalogued and further analyzed for their functional enrichment through motif analysis. This value-added approach delivered identification of 21,710 unique modification sites on 16,364 modified peptides. Interestingly, we observed 278 peptides corresponding to 184 proteins to be differentially abundant. Using bioinformatics approaches, we observed that majority of these altered PTMs/proteins belonged to cytoskeletal and extracellular matrix proteins, which are known to be perturbed in gastric cancer. The dataset derived by this mutiPTM investigation can provide leads to further investigate the potential role of altered PTMs in gastric cancer management.

Helicobacter pylori regulated microRNA map of human gastric cells.

BACKGROUND: Helicobacter pylori is an infection of concern for its chronic colonization leading to peptic ulcers and gastric cancer. In recent times, microRNAs have been extensively studied to understand their role in the pathogenesis of this bacteria in diverse contexts of gastric diseases. The current analysis reports the microRNA-mRNA interactions that are associated with effective survival and virulence of this pathogen. MATERIALS AND METHODS: We convened differentially regulated human microRNAs responsive to H. pylori infection (HP-hDEmiRs) at different multiplicity of infection and time points in human gastric cell lines through retrospective data mining of experimental studies. In view of the molecular disparity of clinical samples and animal models, data from tissue, serum/plasma, urine, and ascites were excluded. Further, we utilized diverse bioinformatics approaches to retrieve experimentally validated, high-confidence targets of the HP-hDEmiRs to analyze the microRNA-mRNA interactions that are relevant to H. pylori pathogenesis. RESULTS: A total of 39 HP-hDEmiRs that showed unidirectional expression of either overexpression or downregulation were identified to modulate 23 targets explicitly studied under this infection. We also identified 476 experimentally validated targets regulated by at least 4 of the HP-hDEmiRs. In addition to the pathways prior-associated with H. pylori infection, the microRNA-mRNA interactome analysis identified several cellular processes and pathways highly associated with cell cycle, cell division, migration, and carcinogenesis. CONCLUSION: This study generated a platform to study the mechanisms utilized by this pathogen using microRNAs as surrogate.

Cluster-MLP: An Active Learning Genetic Algorithm Framework for Accelerated Discovery of Global Minimum Configurations of Pure and Alloyed Nanoclusters.

Structural characterization of nanoclusters is one of the major challenges in nanocluster modeling owing to the multitude of possible configurations of arrangement of cluster atoms. The genetic algorithm (GA), a class of evolutionary algorithms based on the principles of natural evolution, is a commonly employed search method for locating the global minimum configuration of nanoclusters. Although a GA search at the DFT level is required for the accurate description of a potential energy surface to arrive at the correct global minimum configuration of nanoclusters, computationally expensive DFT evaluation of the significantly larger number of cluster geometries limits its practicability. Recently, machine learning potentials (MLP) that are learned from DFT calculations gained significant attention as computationally cheap alternative options that provide DFT level accuracy. As the accuracy of the MLP predictions is dependent on the quality and quantity of the training DFT data, active learning (AL) strategies have gained significant momentum to bypass the need of large and representative training data. In this application note, we present Cluster-MLP, an on-the-fly active learning genetic algorithm framework that employs the Flare++ machine learning potential (MLP) for accelerating the GA search for global minima of pure and alloyed nanoclusters. We have used a modified version the Birmingham parallel genetic algorithm (BPGA) for the nanocluster GA search which is then incorporated into distributed evolutionary algorithms in Python (DEAP), an evolutionary computational framework for fast prototyping or technical experiments. We have shown that the incorporation of the AL framework in the BPGA significantly reduced the computationally expensive DFT calculations. Moreover, we have shown that both the AL-GA and DFT-GA predict the same global minima for all the clusters we tested.

Electrocatalytic reduction of CO2 on size-selected nanoclusters of first-row transition metal nanoclusters: a comprehensive mechanistic investigation.

Recycling CO2 back to fuels offers an ideal solution to control anthropogenic global CO2 emissions as well as providing a sustainable green solution to alternative energy resources from a cheap and earth-abundant carbon source. Size-selected nanoclusters open a novel area in catalysis as these atomically precise nanoclusters possess unique electronic and catalytic properties different from larger nanoparticles and traditional bulk catalysts. In this work, we have investigated the ability of first-row transition metal nanoclusters (Sc-Cu) of varying sizes (3 to 10 atoms) for CO2 electroreduction (CO2RR). Employing computational hydrogen model (CHE), we have performed detailed analyzes on various CO2RR electrocatalytic reaction pathways on all nanocluster surfaces. We have identified a general trend of decreasing adsorption energies while moving across the periodic table from Sc to Cu. Moreover, we have found a general preference for CHO* mediated pathways over COH* mediated pathways for methane formation. The CHO* mediated pathways prefer the reaction route via CHO* → CH2O* → CH2OH* → CH2* → CH3* → CH4 + * on most of the nanocluster surfaces. In addition, we have established that methanol formation is greatly disfavored on all nanocluster surfaces, and the release of CO and HCOOH is greatly suppressed on all nanoclusters. We have identified several nanoclusters as potential nanocluster-based electrocatalysts for CO2RR for methane formation with relatively lower limiting potential values below 0.50 V. CO2 electroreduction versus hydrogen evolution reaction (HER) competition was also evaluated on various nanoclusters, and we identified a number of nanoclusters (Ti6, V5, V6, Mn4, Mn7, Mn10, Fe4, Fe8, Fe10, Ni4, and Cu5) that can suppress the formation of HER over CO2RR. We have also established a linear scaling relationship between the adsorption free energies of various CO2RR adsorbates to the adsorption free energies of CO2*, O*, and C* adsorbates. We have found that scaling free energy relationships that exit on heterogeneous catalysts such as the correlation between the adsorption energies of AHx with the adsorption energies of atom A (A = C, N, O, S, etc.) often breaks on nanocluster surfaces, especially for adsorbates with more than one binding motifs.

A Curated Resource for Phosphosite-specific Signature Analysis.

Signaling pathways are orchestrated by post-translational modifications (PTMs) such as phosphorylation. However, pathway analysis of PTM data sets generated by mass spectrometry (MS)-based proteomics is typically performed at a gene-centric level because of the lack of appropriately curated PTM signature databases and bioinformatic tools that leverage PTM site-specific information. Here we present the first version of PTMsigDB, a database of modification site-specific signatures of perturbations, kinase activities and signaling pathways curated from more than 2,500 publications. We adapted the widely used single sample Gene Set Enrichment Analysis approach to utilize PTMsigDB, enabling PTMSignature Enrichment Analysis (PTM-SEA) of quantitative MS data. We used a well-characterized data set of epidermal growth factor (EGF)-perturbed cancer cells to evaluate our approach and demonstrated better representation of signaling events compared with gene-centric methods. We then applied PTM-SEA to analyze the phosphoproteomes of cancer cells treated with cell-cycle inhibitors and detected mechanism-of-action specific signatures of cell cycle kinases. We also applied our methods to analyze the phosphoproteomes of PI3K-inhibited human breast cancer cells and detected signatures of compounds inhibiting PI3K as well as targets downstream of PI3K (AKT, MAPK/ERK) covering a substantial fraction of the PI3K pathway. PTMsigDB and PTM-SEA can be freely accessed at https://github.com/broadinstitute/ssGSEA2.0.

A draft map of the human proteome.

The availability of human genome sequence has transformed biomedical research over the past decade. However, an equivalent map for the human proteome with direct measurements of proteins and peptides does not exist yet. Here we present a draft map of the human proteome using high-resolution Fourier-transform mass spectrometry. In-depth proteomic profiling of 30 histologically normal human samples, including 17 adult tissues, 7 fetal tissues and 6 purified primary haematopoietic cells, resulted in identification of proteins encoded by 17,294 genes accounting for approximately 84% of the total annotated protein-coding genes in humans. A unique and comprehensive strategy for proteogenomic analysis enabled us to discover a number of novel protein-coding regions, which includes translated pseudogenes, non-coding RNAs and upstream open reading frames. This large human proteome catalogue (available as an interactive web-based resource at http://www.humanproteomemap.org) will complement available human genome and transcriptome data to accelerate biomedical research in health and disease.

Nickel Bis(diselenolene) as a Catalyst for Olefin Purification.

Nickel bis(dithiolene) reversibly binds olefins via a known interligand binding mechanism, but the complex has limited practical use, due to a competitive intraligand addition which results in decomposition. The present work examines an alternative nickel-based complex that eliminates the decomposition route. Specifically, we have examined the olefin binding processes of nickel bis(diselenolene) complexes using modern density functional theory. Both the inter- and intraligand adducts of the nickel bis(diselenolenes) are thermodynamically more stable than their dithiolene analogues. We have predicted that nickel bis(diselenolene) complexes do not decompose after the intraligand addition, and that the overall activation energies for the kinetically accessible products are quite small. In short, our computational work predicts that nickel bis(diselenolene) complexes are better electrocatalysts for olefin purification than the previous candidates, superior to the previously studied nickel bis(dithiolene) complexes.

Mechanism of Ethylene Addition to Nickel Bis(oxothiolene) and Nickel Bis(dioxolene) Complexes.

The electrochemically reversible binding of olefins by nickel bis(dithiolene) has been extensively studied, both theoretically and computationally. To optimize a catalyst for this process, we have investigated all possible reaction pathways of ethylene addition onto the related complex nickel bis(dioxolene), and the two isomers (cis and trans) of nickel bis(oxothiolene). Modern DFT calculations predict that the nickel bis(dioxolene) complex has limited practical use due to high barriers to binding. However, each of the two isomers of the nickel bis(oxothiolene) complexes display enhanced properties versus the original nickel bis(dithiolene) complex. Specifically, in nickel bis(dithiolene), the intraligand binding of olefins leads to decomposition, whereas interligand binding is required for reversibility; the two nickel bis(oxothiolene) complexes have greater selectivity toward the formation of the desired interligand adducts. For the full reaction pathways, the new complexes' binding mechanisms are contrasted with the mechanism of the original catalyst.

Plasma Proteome Database as a resource for proteomics research: 2014 update.

Plasma Proteome Database (PPD; http://www.plasmaproteomedatabase.org/) was initially described in the year 2005 as a part of Human Proteome Organization's (HUPO's) pilot initiative on Human Plasma Proteome Project. Since then, improvements in proteomic technologies and increased throughput have led to identification of a large number of novel plasma proteins. To keep up with this increase in data, we have significantly enriched the proteomic information in PPD. This database currently contains information on 10,546 proteins detected in serum/plasma of which 3784 have been reported in two or more studies. The latest version of the database also incorporates mass spectrometry-derived data including experimentally verified proteotypic peptides used for multiple reaction monitoring assays. Other novel features include published plasma/serum concentrations for 1278 proteins along with a separate category of plasma-derived extracellular vesicle proteins. As plasma proteins have become a major thrust in the field of biomarkers, we have enabled a batch-based query designated Plasma Proteome Explorer, which will permit the users in screening a list of proteins or peptides against known plasma proteins to assess novelty of their data set. We believe that PPD will facilitate both clinical and basic research by serving as a comprehensive reference of plasma proteins in humans and accelerate biomarker discovery and translation efforts.

Differential proteomic analysis of synovial fluid from rheumatoid arthritis and osteoarthritis patients.

BACKGROUND: Rheumatoid arthritis and osteoarthritis are two common musculoskeletal disorders that affect the joints. Despite high prevalence rates, etiological factors involved in these disorders remain largely unknown. Dissecting the molecular aspects of these disorders will significantly contribute to improving their diagnosis and clinical management. In order to identify proteins that are differentially expressed between these two conditions, a quantitative proteomic profiling of synovial fluid obtained from rheumatoid arthritis and osteoarthritis patients was carried out by using iTRAQ labeling followed by high resolution mass spectrometry analysis. RESULTS: We have identified 575 proteins out of which 135 proteins were found to be differentially expressed by ≥3-fold in the synovial fluid of rheumatoid arthritis and osteoarthritis patients. Proteins not previously reported to be associated with rheumatoid arthritis including, coronin-1A (CORO1A), fibrinogen like-2 (FGL2), and macrophage capping protein (CAPG) were found to be upregulated in rheumatoid arthritis. Proteins such as CD5 molecule-like protein (CD5L), soluble scavenger receptor cysteine-rich domain-containing protein (SSC5D), and TTK protein kinase (TTK) were found to be upregulated in the synovial fluid of osteoarthritis patients. We confirmed the upregulation of CAPG in rheumatoid arthritis synovial fluid by multiple reaction monitoring assay as well as by Western blot. Pathway analysis of differentially expressed proteins revealed a significant enrichment of genes involved in glycolytic pathway in rheumatoid arthritis. CONCLUSIONS: We report here the largest identification of proteins from the synovial fluid of rheumatoid arthritis and osteoarthritis patients using a quantitative proteomics approach. The novel proteins identified from our study needs to be explored further for their role in the disease pathogenesis of rheumatoid arthritis and osteoarthritis.Sartaj Ahmad and Raja Sekhar Nirujogi contributed equally to this article.

Proteomic analysis of human osteoarthritis synovial fluid.

BACKGROUND: Osteoarthritis is a chronic musculoskeletal disorder characterized mainly by progressive degradation of the hyaline cartilage. Patients with osteoarthritis often postpone seeking medical help, which results in the diagnosis being made at an advanced stage of cartilage destruction. Sustained efforts are needed to identify specific markers that might help in early diagnosis, monitoring disease progression and in improving therapeutic outcomes. We employed a multipronged proteomic approach, which included multiple fractionation strategies followed by high resolution mass spectrometry analysis to explore the proteome of synovial fluid obtained from osteoarthritis patients. In addition to the total proteome, we also enriched glycoproteins from synovial fluid using lectin affinity chromatography. RESULTS: We identified 677 proteins from synovial fluid of patients with osteoarthritis of which 545 proteins have not been previously reported. These novel proteins included ADAM-like decysin 1 (ADAMDEC1), alanyl (membrane) aminopeptidase (ANPEP), CD84, fibulin 1 (FBLN1), matrix remodelling associated 5 (MXRA5), secreted phosphoprotein 2 (SPP2) and spondin 2 (SPON2). We identified 300 proteins using lectin affinity chromatography, including the glycoproteins afamin (AFM), attractin (ATRN), fibrillin 1 (FBN1), transferrin (TF), tissue inhibitor of metalloproteinase 1 (TIMP1) and vasorin (VSN). Gene ontology analysis confirmed that a majority of the identified proteins were extracellular and are mostly involved in cell communication and signaling. We also confirmed the expression of ANPEP, dickkopf WNT signaling pathway inhibitor 3 (DKK3) and osteoglycin (OGN) by multiple reaction monitoring (MRM) analysis of osteoarthritis synovial fluid samples. CONCLUSIONS: We present an in-depth analysis of the synovial fluid proteome from patients with osteoarthritis. We believe that the catalog of proteins generated in this study will further enhance our knowledge regarding the pathophysiology of osteoarthritis and should assist in identifying better biomarkers for early diagnosis.

Thermal and photochemical reactivity of manganese tricarbonyl and tetracarbonyl complexes with a bulky diazabutadiene ligand.

The manganese tricarbonyl complex fac-Mn(Br)(CO)3((i)Pr2Ph-DAB) (1) [(i)Pr2Ph-DAB = (N,N'-bis(2,6-di-isopropylphenyl)-1,4-diaza-1,3-butadiene)] was synthesized from the reaction of Mn(CO)5Br with the sterically encumbered DAB ligand. Compound 1 exhibits rapid CO release under low power visible light irradiation (560 nm) suggesting its possible use as a photoCORM. The reaction of compound 1 with TlPF6 in the dark afforded the manganese(I) tetracarbonyl complex, [Mn(CO)4((i)Pr2Ph-DAB)][PF6] (2). While 2 is comparatively more stable than 1 in light, it demonstrates high thermal reactivity such that dissolution in CH3CN or THF at room temperature results in rapid CO loss and formation of the respective solvate complexes. This unusual reactivity is due to the large steric profile of the DAB ligand which results in a weak Mn-CO binding interaction.

Etiologically Significant microRNAs in Hepatitis B Virus-Induced Hepatocellular Carcinoma.

Hepatitis B virus (HBV) infection has been causally linked to hepatocellular carcinoma (HCC) in more than 50% cases. MicroRNAs (miRNAs) play cross-cutting mechanistic roles in the complex interplay between viral pathogenesis, host survival, and clinical outcomes. The present study set out to identify etiologically significant human miRNAs associated with HBV infection in liver-related pathologies leading to HCC. In diverse tissue types, we assembled 573 miRNAs differentially expressed in HBV-associated liver pathologies, HBV infection, fibrosis, cirrhosis, acute on chronic liver failure, and HCC. Importantly, 43 human differentially expressed miRNAs (hDEmiRs) were regulated in serum/plasma and liver tissue of patients with HBV-positive conditions. However, only two hDEmiRs, hsa-miR-21-5p and hsa-miR-143-3p, were regulated across all disease conditions. To shortlist the functional miRNAs in HBV-induced HCC pathogenesis, a reverse bioinformatics analysis was performed using eight GEO datasets and the TCGA database containing the list of differentially regulated mRNAs in HCC. A comparative study using these data with the identified targets of hDEmiRs, a set of unidirectionally regulated hDEmiRs with the potential to modulate mRNAs in HCC, were found. Moreover, our study identified five miRNAs; hsa-miR-98-5p, hsa-miR-193b-3p, hsa-miR-142-5p, hsa-miR-522-5p, and hsa-miR-370-3p targeting PIGC, KNTC1, CSTF2, SLC41A2, and RAB17, respectively, in HCC. These hDEmiRs and their targets could be pivotal in HBV infection and subsequent liver pathologies modulating HCC clinical progression. HBV infection is the largest contributor to HCC, and the present study comprises the first of its kind compendium of hDEmiRs related to HBV-related pathologies.

Integrative Placental Multi-Omics Analysis Reveals Perturbed Pathways and Potential Prognostic Biomarkers in Gestational Hypertension.

BACKGROUND: Gestational hypertension (GH) is a severe complication that occurs after 20 weeks of pregnancy; however, its molecular mechanisms are not yet fully understood. OBJECTIVE: Through this case-control discovery phase study, we aimed to find disease-specific candidate placental microRNAs (miRNAs) and metabolite markers for differentiating GH by integrating next-generation sequencing and metabolomics multi-omics analysis of placenta. Using small RNA sequencing and metabolomics of placental tissues of healthy pregnant (HP, n = 24) and GH subjects (n = 20), the transcriptome and metabolome were characterized in both groups. RESULTS: The study identified a total of 44 downregulated placental miRNAs which includes three novel, three mature and 38 precursor miRNAs. Six miRNAs including three mature (hsa-miR-181a-5p, hsa-miR-498-5p, and hsa-miR-26b-5p) and three novel (NC_000016.10_1061, NC_000005.10_475, and NC_000001.11_53) were considered for final target prediction and functional annotation. Integrative analysis of differentially expressed miRNAs and metabolites yielded five pathways such as purine, glutathione, glycerophospholipid, inositol phosphate and ß-alanine to be significantly perturbed in GH. We present fourteen genes (LPCAT1, LPCAT2, DGKH, PISD, GPAT2, PTEN, SACM1L, PGM2, AMPD3, AK7, AK3, CNDP1, IDH2, and ODC1) and eight metabolites (xanthosine, xanthine, spermine, glycine, CDP-Choline, glyceraldehyde 3-phosphate, ß-alanine, and histidine) with potential to distinguish GH and HP. CONCLUSION: The differential expression of miRNAs, their target genes, altered metabolites and metabolic pathways in GH patients were identified for the first time in our study. Further, the altered miRNAs and metabolites were integrated to build their inter-connectivity network. The findings obtained from our study may be used as a valuable source to further unravel the molecular pathways associated with GH and also for the evaluation of prognostic markers.