ProteomicsDB

Machine learning for the advancement of genome-scale metabolic modeling

Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the interrelations between genotype, phenotype, and external environment. The recent advancement of high-throughput experimental approaches and multi-omics strategies has generated a plethora of new and precise information from wide-ranging biological domains. On the other hand, the continuously growing field of machine learning (ML) and its specialized branch of deep learning (DL) provide essential computational architectures for decoding complex and heterogeneous biological data. In recent years, both multi-omics and ML have assisted in the escalation of CBM. Condition-specific omics data, such as transcriptomics and proteomics, helped contextualize the model prediction while analyzing a particular phenotypic signature. At the same time, the advanced ML tools have eased the model reconstruction and analysis to increase the accuracy and prediction power. However, the development of these multi-disciplinary methodological frameworks mainly occurs independently, which limits the concatenation of biological knowledge from different domains. Hence, we have reviewed the potential of integrating multi-disciplinary tools and strategies from various fields, such as synthetic biology, CBM, omics, and ML, to explore the biochemical phenomenon beyond the conventional biological dogma. How the integrative knowledge of these intersected domains has improved bioengineering and biomedical applications has also been highlighted. We categorically explained the conventional genome-scale metabolic model (GEM) reconstruction tools and their improvement strategies through ML paradigms. Further, the crucial role of ML and DL in omics data restructuring for GEM development has also been briefly discussed. Finally, the case-study-based assessment of the state-of-the-art method for improving biomedical and metabolic engineering strategies has been elaborated. Therefore, this review demonstrates how integrating experimental and in silico strategies can help map the ever-expanding knowledge of biological systems driven by condition-specific cellular information. This multiview approach will elevate the application of ML-based CBM in the biomedical and bioengineering fields for the betterment of society and the environment.

Unraveling neuroimaging insights in developmental epileptic encephalopathy type 25: a comprehensive review of reported cases and a novel SLC13A5 variant

Developmental and epileptic encephalopathy type 25 with amelogenesis imperfecta (DEE25) is a rare autosomal recessive disorder caused by homozygous or compound heterozygous disease-causing variants in the SLC13A5. These variants can disrupt energy production and delay brain development, leading to DEE25. Key symptoms include refractory seizures, often manifesting in neonates or infants, alongside global developmental delay, intellectual disability, progressive microcephaly, ataxia, spasticity, and speech difficulties. Dental anomalies related to amelogenesis imperfecta are common. Previous studies have typically reported normal or minimally altered early-life brain magnetic resonance imaging (MRI) findings in DEE25. However, our investigation identified a homozygous splice donor variant (NM_177550.5: c.1437 + 1G >T) in SLC13A5 through whole-exome sequencing in two affected siblings (P1 and P2). They displayed developmental delay, cerebral hypotonia, speech delay, recurrent seizures, mild but constant microcephaly, and motor impairments. Significantly, P1 exhibited novel findings on brain magnetic resonance imaging at age 5, including previously unreported extensive persistent hypomyelination. Meanwhile, P2 showed substantial loss of cerebral white matter in the frontoparietal region and delayed myelination at 18 months old. These discoveries broaden the DEE25 imaging spectrum and highlight the clinical heterogeneity even within siblings sharing the same variants.

Identification and Validation of PKR as a Direct Target for the Novel Sulfonamide-Substituted Tetrahydroquinoline Nonselective Inhibitor of the NLRP3 Inflammasome

The NLRP3 inflammasome is a critical component of the innate immune system. The persistent abnormal activation of the NLRP3 inflammasome is implicated in numerous human diseases. Herein, sulfonamide-substituted tetrahydroquinoline derivative S-9 was identified as the most promising NLRP3 inhibitor, without obvious cytotoxicity. In vitro, S-9 inhibited the priming and activation stages of the NLRP3 inflammasome. Incidentally, we also observed that S-9 had inhibitory effects on the NLRC4 and AIM2 inflammasomes. To elucidate the multiple anti-inflammatory activities of S-9, photoaffinity probe P-2, which contained a photoaffinity label and a functional handle, was developed for target identification by chemical proteomics. We identified PKR as a novel target of S-9 in addition to NLRP3 by target fishing. Furthermore, S-9 exhibited a significant anti-neuroinflammatory effect in vivo. In summary, our findings show that S-9 is a promising lead compound targeting both PKR and NLRP3 that could emerge as a molecular tool for treating inflammasome-related diseases.

Urinary Proteomics and Outcomes in Heart Failure With Preserved Ejection Fraction

BACKGROUND

Although several studies have addressed plasma proteomics in heart failure with preserved ejection fraction, limited data are available on the prognostic value of urinary proteomics. The objective of our study was to identify urinary proteins/peptides associated with death and heart failure admission in patients with heart failure with preserved ejection fraction.

METHODS AND RESULTS

The study population included participants enrolled in TOPCAT (Treatment of Preserved Cardiac Function Heart Failure With an Aldosterone Antagonist Trial). The relationship between urine protein levels and the risk of death or heart failure admission was assessed using Cox regression, in both nonadjusted analyses and adjusting for urine creatinine levels, and the MAGGIC (Meta-Analysis Global Group in Chronic Heart Failure) score. A total of 426 (12.4%) TOPCAT participants had urinary protein data and were included. There were 40 urinary proteins/peptides significantly associated with death or heart failure admission in nonadjusted analyses, 21 of which were also significant adjusted analyses. Top proteins in the adjusted analysis included ANGPTL2 (angiopoietin-like protein 2) (hazard ratio [HR], 0.5731 [95% CI, 0.47-0.7]; P=3.13E-05), AMY2A (α amylase 2A) (HR, 0.5496 [95% CI, 0.44-0.69]; P=0.0001), and DNASE1 (deoxyribonuclease-1) (HR, 0.5704 [95% CI, 0.46-0.71]; P=0.0002). Higher urinary levels of proteins involved in fibrosis (collagen VI α-1, collagen XV α-1), metabolism (pancreatic α-amylase 2A/B, mannosidase α class 1A member 1), and inflammation (heat shock protein family D member 1, inducible T cell costimulatory ligand) were associated with a lower risk of death or heart failure admission.

CONCLUSIONS

Our study identifies several novel associations between urinary proteins/peptides and outcomes in heart failure with preserved ejection fraction. Many of these associations are independent of clinical risk scores and may aid in risk stratification in this patient population.

PIRCHE application major versions 3 and 4 lead to equivalent T cell epitope mismatch scores in solid organ and stem cell transplantation modules

PIRCHE scores in organ and stem cell transplantation have been shown to correlate with increased risk of donor-specific HLA antibodies and graft-versus-host disease, respectively. With advancements of the PIRCHE application server, it is critical to compare the predicted scores with previous versions. This manuscript compares the newly introduced PIRCHE version 4.2 with its predecessor version 3.3, which was widely used in retrospective studies, using a virtual cohort of 10,000 transplant pairs. In the stem cell transplantation module, both versions yield identical results in 100% of the test population. In the solid organ module, 97% of the test population has identical PIRCHE scores. The deviating cases (3%) were attributed to refinements in the PIRCHE algorithm's specification. Furthermore, the magnitude of the difference is likely to be below the detection limit for clinical effects, confirming the equivalence in PIRCHE scores between versions 3.3 and 4.2.

Levels of neuronal pentraxin 2 in plasma is associated with cognitive function in patients with schizophrenia

RATIONALE

The precise diagnosis and treatment of cognitive impairment remains a major challenge in the field of schizophrenia (SCZ) research. Synaptic dysfunction and loss are thought to be closely related to the occurrence and development of SCZ and may be involved in cognitive dysfunction.

OBJECTIVES

The purpose of this study was to investigate whether neuronal pentraxins (NPTXs) plays a role in the etiology of SCZ and provide evidence of its possible therapeutic value a new target for drug development.

METHODS

We recruited 275 participants, of whom 148 were SCZ from psychiatric hospital and 127 healthy control (HC) subjects from communities. Plasma concentrations of NPTXs were measured in HC and SCZ at baseline and after 8 weeks of antipsychotic treatment. The MATRICS Cognitive Consensus Battery was used to evaluate cognitive function. Furthermore, the brain is parcellated into 246 subregions using the Brainnetome atlas, and we extracted regional white matter volumes from magnetic resonance images of the SCZ groups.

RESULTS

Plasma NPTX2 levels were significantly lower in SCZ compared with HC subjects, but were significantly raised in SCZ after 8 weeks of antipsychotic treatment compared to baseline. In addition, baseline plasma NPTX2 levels were positively correlated with cognitive performance.

CONCLUSIONS

These findings indicate that NPTX2 may reveal novel aspects of disease etiology and act as a promising target for new drug development.

Myeloid-derived growth factor and its effects on cardiovascular and metabolic diseases

Myeloid-derived growth factor (MYDGF) is a paracrine protein produced by bone marrow-derived monocytes and macrophages. Current research shows that it has protective effects on the cardiovascular system, such as repairing heart tissue after myocardial infarction, enhancing cardiomyocyte proliferation, improving cardiac regeneration after myocardial injury, regulating proliferation and survival of endothelial cells, reducing endothelial cell damage, resisting pressure overload-induced heart failure, as well as protecting against atherosclerosis. Furthermore, regarding the metabolic diseases, MYDGF has effects of improving type 2 diabetes mellitus, relieving non-alcoholic fatty liver disease, alleviating glomerular diseases, and resisting osteoporosis. Herein, we will discuss the biology of MYDGF and its effects on cardiovascular and metabolic diseases.

Camptothecin structure simplification elaborated new imidazo[2,1-b]quinazoline derivative as a human topoisomerase I inhibitor with efficacy against bone cancer cells and colon adenocarcinoma

Camptothecin is a pentacyclic natural alkaloid that inhibits the hTop1 enzyme involved in DNA transcription and cancer cell growth. Camptothecin structure pitfalls prompted us to design new congeners using a structure simplification strategy to reduce the ring extension number from pentacyclic to tetracyclic while maintaining potential stacking of the new compounds with the DNA base pairs at the Top1-mediated cleavage complex and aqueous solubility, as well as minimizing compound-liver toxicity. The principal axis of this study was the verification of hTop1 inhibiting activity as a possible mechanism of action and the elaboration of new simplified inhibitors with improved pharmacodynamic and pharmacokinetic profiling using three structure panels (A-C) of (isoquinolinoimidazoquinazoline), (imidazoquinazoline), and (imidazoisoquinoline), respectively. DNA relaxation assay identified five compounds as hTop1 inhibitors belonging to the imidazoisoquinolines 3a,b, the imidazoquinazolines 12, and the isoquinolinoimidazoquinazolines 7a,b. In an MTT cytotoxicity assay against different cancer cell lines, compound 12 was the most potent against HOS bone cancer cells (IC50 = 1.47 μM). At the same time, the other inhibitors had no detectable activity against any cancer cell type. Compound (12) demonstrated great penetrating power in the HOS cancer cells' 3D-multicellular tumor spheroid model. Bioinformatics research of the hTop1 gene revealed that the TP53 cell proliferative gene is in the network of hTop1. The finding is confirmed empirically using the gene expression assay that proved the increase in p53 expression. The impact of structure simplification on compound 12 profile, characterized by the absence of acute oral liver toxicity when compared to Doxorubicin as a standard inhibitor, the lethal dose measured on Swiss Albino female mice and reported at LD50 = 250 mg/kg, and therapeutic significance in reducing colon adenocarcinoma tumor volume by 75.36 % after five weeks of treatment with compound 12. The molecular docking solutions of the active CPT-based derivative 12 and the inactive congener 14 into the active site of hTop1 and the activity cliffing of such MMP directed us to recommend the addition of HBD and HBA variables to compound 12 imidazoquinazoline core scaffold to enhance the potency via hydrogen bond formation with the major groove amino acids (Asp533, Lys532) as well as maintaining the hydrogen bond with the minor groove amino acid Arg364.

Illuminating phenotypic drug responses of sarcoma cells to kinase inhibitors by phosphoproteomics

Kinase inhibitors (KIs) are important cancer drugs but often feature polypharmacology that is molecularly not understood. This disconnect is particularly apparent in cancer entities such as sarcomas for which the oncogenic drivers are often not clear. To investigate more systematically how the cellular proteotypes of sarcoma cells shape their response to molecularly targeted drugs, we profiled the proteomes and phosphoproteomes of 17 sarcoma cell lines and screened the same against 150 cancer drugs. The resulting 2550 phenotypic profiles revealed distinct drug responses and the cellular activity landscapes derived from deep (phospho)proteomes (9-10,000 proteins and 10-27,000 phosphorylation sites per cell line) enabled several lines of analysis. For instance, connecting the (phospho)proteomic data with drug responses revealed known and novel mechanisms of action (MoAs) of KIs and identified markers of drug sensitivity or resistance. All data is publicly accessible via an interactive web application that enables exploration of this rich molecular resource for a better understanding of active signalling pathways in sarcoma cells, identifying treatment response predictors and revealing novel MoA of clinical KIs.

Mechanosensing in Metabolism

Electrical mechanosensing is a process mediated by specialized ion channels, gated directly or indirectly by mechanical forces, which allows cells to detect and subsequently respond to mechanical stimuli. The activation of mechanosensitive (MS) ion channels, intrinsically gated by mechanical forces, or mechanoresponsive (MR) ion channels, indirectly gated by mechanical forces, results in electrical signaling across lipid bilayers, such as the plasma membrane. While the functions of mechanically gated channels within a sensory context (e.g., proprioception and touch) are well described, there is emerging data demonstrating functions beyond touch and proprioception, including mechanoregulation of intracellular signaling and cellular/systemic metabolism. Both MR and MS ion channel signaling have been shown to contribute to the regulation of metabolic dysfunction, including obesity, insulin resistance, impaired insulin secretion, and inflammation. This review summarizes our current understanding of the contributions of several MS/MR ion channels in cell types implicated in metabolic dysfunction, namely, adipocytes, pancreatic β-cells, hepatocytes, and skeletal muscle cells, and discusses MS/MR ion channels as possible therapeutic targets. © 2024 American Physiological Society. Compr Physiol 14:5269-5290, 2024.

A quantitative model for virus uncoating predicts influenza A infectivity

For virus infection of new host cells, the disassembly of the protective outer protein shell (capsid) is a critical step, but the mechanisms and host-virus interactions underlying the dynamic, active, and regulated uncoating process are largely unknown. Here, we develop an experimentally supported, multiscale kinetics model that elucidates mechanisms of influenza A virus (IAV) uncoating in cells. Biophysical modeling demonstrates that interactions between capsid M1 proteins, host histone deacetylase 6 (HDAC6), and molecular motors can physically break the capsid in a tug-of-war mechanism. Biochemical analysis and biochemical-biophysical modeling identify unanchored ubiquitin chains as essential and allow robust prediction of uncoating efficiency in cells. Remarkably, the different infectivity of two clinical strains can be ascribed to a single amino acid variation in M1 that affects binding to HDAC6. By identifying crucial modules of viral infection kinetics, the mechanisms and models presented here could help formulate novel strategies for broad-range antiviral treatment.

Systemic proteome phenotypes reveal defective metabolic flexibility in Mecp2 mutants

Genes mutated in monogenic neurodevelopmental disorders are broadly expressed. This observation supports the concept that monogenic neurodevelopmental disorders are systemic diseases that profoundly impact neurodevelopment. We tested the systemic disease model focusing on Rett syndrome, which is caused by mutations in MECP2. Transcriptomes and proteomes of organs and brain regions from Mecp2-null mice as well as diverse MECP2-null male and female human cells were assessed. Widespread changes in the steady-state transcriptome and proteome were identified in brain regions and organs of presymptomatic Mecp2-null male mice as well as mutant human cell lines. The extent of these transcriptome and proteome modifications was similar in cortex, liver, kidney, and skeletal muscle and more pronounced than in the hippocampus and striatum. In particular, Mecp2- and MECP2-sensitive proteomes were enriched in synaptic and metabolic annotated gene products, the latter encompassing lipid metabolism and mitochondrial pathways. MECP2 mutations altered pyruvate-dependent mitochondrial respiration while maintaining the capacity to use glutamine as a mitochondrial carbon source. We conclude that mutations in Mecp2/MECP2 perturb lipid and mitochondrial metabolism systemically limiting cellular flexibility to utilize mitochondrial fuels.

GSE1 links the HDAC1/CoREST co-repressor complex to DNA damage

Post-translational modifications of histones are important regulators of the DNA damage response (DDR). By using affinity purification mass spectrometry (AP-MS) we discovered that genetic suppressor element 1 (GSE1) forms a complex with the HDAC1/CoREST deacetylase/demethylase co-repressor complex. In-depth phosphorylome analysis revealed that loss of GSE1 results in impaired DDR, ATR signalling and γH2AX formation upon DNA damage induction. Altered profiles of ATR target serine-glutamine motifs (SQ) on DDR-related hallmark proteins point to a defect in DNA damage sensing. In addition, GSE1 knock-out cells show hampered DNA damage-induced phosphorylation on SQ motifs of regulators of histone post-translational modifications, suggesting altered histone modification. While loss of GSE1 does not affect the histone deacetylation activity of CoREST, GSE1 appears to be essential for binding of the deubiquitinase USP22 to CoREST and for the deubiquitination of H2B K120 in response to DNA damage. The combination of deacetylase, demethylase, and deubiquitinase activity makes the USP22-GSE1-CoREST subcomplex a multi-enzymatic eraser that seems to play an important role during DDR. Since GSE1 has been previously associated with cancer progression and survival our findings are potentially of high medical relevance.

Targeted Proteomic Quantitation of NRF2 Signaling and Predictive Biomarkers in HNSCC

The NFE2L2 (NRF2) oncogene and transcription factor drives a gene expression program that promotes cancer progression, metabolic reprogramming, immune evasion, and chemoradiation resistance. Patient stratification by NRF2 activity may guide treatment decisions to improve outcome. Here, we developed a mass spectrometry-based targeted proteomics assay based on internal standard-triggered parallel reaction monitoring to quantify 69 NRF2 pathway components and targets, as well as 21 proteins of broad clinical significance in head and neck squamous cell carcinoma (HNSCC). We improved an existing internal standard-triggered parallel reaction monitoring acquisition algorithm, called SureQuant, to increase throughput, sensitivity, and precision. Testing the optimized platform on 27 lung and upper aerodigestive cancer cell models revealed 35 NRF2 responsive proteins. In formalin-fixed paraffin-embedded HNSCCs, NRF2 signaling intensity positively correlated with NRF2-activating mutations and with SOX2 protein expression. Protein markers of T-cell infiltration correlated positively with one another and with human papilloma virus infection status. CDKN2A (p16) protein expression positively correlated with the human papilloma virus oncogenic E7 protein and confirmed the presence of translationally active virus. This work establishes a clinically actionable HNSCC protein biomarker assay capable of quantifying over 600 peptides from frozen or formalin-fixed paraffin-embedded archived tissues in under 90 min.

Proteogenomics in Cancer: Then and Now

For years, the paths of sequencing technologies and mass spectrometry have occurred in isolation, with each developing its own unique culture and expertise. These two technologies are crucial for inspecting complementary aspects of the molecular phenotype across the central dogma. Integrative multiomics strives to bridge the analysis gap among different fields to complete more comprehensive mechanisms of life events and diseases. Proteogenomics is one integrated multiomics field. Here in this review, we mainly summarize and discuss three aspects: workflow of proteogenomics, proteogenomics applications in cancer research, and the SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis of proteogenomics in cancer research. In conclusion, proteogenomics has a promising future as it clarifies the functional consequences of many unannotated genomic abnormalities or noncanonical variants and identifies driver genes and novel therapeutic targets across cancers, which would substantially accelerate the development of precision oncology.

Members of the CUGBP Elav-like family of RNA-binding proteins are expressed in distinct populations of primary sensory neurons

Primary sensory dorsal root ganglia (DRG) neurons are diverse, with distinct populations that respond to specific stimuli. Previously, we observed that functionally distinct populations of DRG neurons express mRNA transcript variants with different 3' untranslated regions (3'UTRs). 3'UTRs harbor binding sites for interaction with RNA-binding proteins (RBPs) for transporting mRNAs to subcellular domains, modulating transcript stability, and regulating the rate of translation. In the current study, analysis of publicly available single-cell RNA-sequencing data generated from adult mice revealed that 17 3'UTR-binding RBPs were enriched in specific populations of DRG neurons. This included four members of the CUG triplet repeat (CUGBP) Elav-like family (CELF): CELF2 and CELF4 were enriched in peptidergic, CELF6 in both peptidergic and nonpeptidergic, and CELF3 in tyrosine hydroxylase-expressing neurons. Immunofluorescence studies confirmed that 60% of CELF4+ neurons are small-diameter C fibers and 33% medium-diameter myelinated (likely Aδ) fibers and showed that CELF4 is distributed to peripheral termini. Coexpression analyses using transcriptomic data and immunofluorescence revealed that CELF4 is enriched in nociceptive neurons that express GFRA3, CGRP, and the capsaicin receptor TRPV1. Reanalysis of published transcriptomic data from macaque DRG revealed a highly similar distribution of CELF members, and reanalysis of single-nucleus RNA-sequencing data derived from mouse and rat DRG after sciatic injury revealed differential expression of CELFs in specific populations of sensory neurons. We propose that CELF RBPs may regulate the fate of mRNAs in populations of nociceptors, and may play a role in pain and/or neuronal regeneration following nerve injury.

Personalised modelling of clinical heterogeneity between medium-chain acyl-CoA dehydrogenase patients

BACKGROUND

Monogenetic inborn errors of metabolism cause a wide phenotypic heterogeneity that may even differ between family members carrying the same genetic variant. Computational modelling of metabolic networks may identify putative sources of this inter-patient heterogeneity. Here, we mainly focus on medium-chain acyl-CoA dehydrogenase deficiency (MCADD), the most common inborn error of the mitochondrial fatty acid oxidation (mFAO). It is an enigma why some MCADD patients-if untreated-are at risk to develop severe metabolic decompensations, whereas others remain asymptomatic throughout life. We hypothesised that an ability to maintain an increased free mitochondrial CoA (CoASH) and pathway flux might distinguish asymptomatic from symptomatic patients.

RESULTS

We built and experimentally validated, for the first time, a kinetic model of the human liver mFAO. Metabolites were partitioned according to their water solubility between the bulk aqueous matrix and the inner membrane. Enzymes are also either membrane-bound or in the matrix. This metabolite partitioning is a novel model attribute and improved predictions. MCADD substantially reduced pathway flux and CoASH, the latter due to the sequestration of CoA as medium-chain acyl-CoA esters. Analysis of urine from MCADD patients obtained during a metabolic decompensation showed an accumulation of medium- and short-chain acylcarnitines, just like the acyl-CoA pool in the MCADD model. The model suggested some rescues that increased flux and CoASH, notably increasing short-chain acyl-CoA dehydrogenase (SCAD) levels. Proteome analysis of MCADD patient-derived fibroblasts indeed revealed elevated levels of SCAD in a patient with a clinically asymptomatic state. This is a rescue for MCADD that has not been explored before. Personalised models based on these proteomics data confirmed an increased pathway flux and CoASH in the model of an asymptomatic patient compared to those of symptomatic MCADD patients.

CONCLUSIONS

We present a detailed, validated kinetic model of mFAO in human liver, with solubility-dependent metabolite partitioning. Personalised modelling of individual patients provides a novel explanation for phenotypic heterogeneity among MCADD patients. Further development of personalised metabolic models is a promising direction to improve individualised risk assessment, management and monitoring for inborn errors of metabolism.

Systemic Proteome Phenotypes Reveal Defective Metabolic Flexibility in Mecp2 Mutants

Genes mutated in monogenic neurodevelopmental disorders are broadly expressed. This observation supports the concept that monogenic neurodevelopmental disorders are systemic diseases that profoundly impact neurodevelopment. We tested the systemic disease model focusing on Rett syndrome, which is caused by mutations in MECP2. Transcriptomes and proteomes of organs and brain regions from Mecp2-null mice as well as diverse MECP2-null male and female human cells were assessed. Widespread changes in the steady-state transcriptome and proteome were identified in brain regions and organs of presymptomatic Mecp2-null male mice as well as mutant human cell lines. The extent of these transcriptome and proteome modifications was similar in cortex, liver, kidney, and skeletal muscle and more pronounced than in the hippocampus and striatum. In particular, Mecp2- and MECP2-sensitive proteomes were enriched in synaptic and metabolic annotated gene products, the latter encompassing lipid metabolism and mitochondrial pathways. MECP2 mutations altered pyruvate-dependent mitochondrial respiration while maintaining the capacity to use glutamine as a mitochondrial carbon source. We conclude that mutations in Mecp2/MECP2 perturb lipid and mitochondrial metabolism systemically limiting cellular flexibility to utilize mitochondrial fuels.

Quantitative proteomics and phosphoproteomics of PP2A-PPP2R5D variants reveal deregulation of RPS6 phosphorylation via converging signaling cascades

Genetic germline variants of PPP2R5D (encoding: phosphoprotein phosphatase 2 regulatory protein 5D) result in PPP2R5D-related disorder (Jordan's Syndrome), which is characterized by intellectual disability, hypotonia, seizures, macrocephaly, autism spectrum disorder, and delayed motor skill development. The disorder originates from de novo single nucleotide mutations, generating missense variants that act in a dominant manner. Pathogenic mutations altering 13 different amino acids have been identified, with the E198K variant accounting for ∼40% of reported cases. However, the generation of a heterozygous E198K variant cell line to study the molecular effects of the pathogenic mutation has been challenging. Here, we use CRISPR-PRIME genomic editing to introduce a transition (c.592G>A) in a single PPP2R5D allele in HEK293 cells, generating E198K-heterozygous lines to complement existing E420K variant lines. We generate global protein and phosphorylation profiles of WT, E198K, and E420K cell lines and find unique and shared changes between variants and WT cells in kinase- and phosphatase-controlled signaling cascades. We observed ribosomal protein S6 (RPS6) hyperphosphorylation as a shared signaling alteration, indicative of increased ribosomal protein S6-kinase activity. Treatment with rapamycin or an RPS6-kinase inhibitor (LY2584702) suppressed RPS6 phosphorylation in both, suggesting upstream activation of mTORC1/p70S6K. Intriguingly, our data suggests ERK-dependent activation of mTORC1 in both E198K and E420K variant cells, with additional AKT-mediated mTORC1 activation in the E420K variant. Thus, although upstream activation of mTORC1 differs between PPP2R5D-related disorder genotypes, inhibition of mTORC1 or RPS6 kinases warrants further investigation as potential therapeutic strategies for patients.

Human Schlafen 11 inhibits influenza A virus production

Schlafen (SLFN) proteins are a subset of interferon-stimulated early response genes with antiviral properties. An antiviral mechanism of SLFN11 was previously demonstrated in human immunodeficiency virus type 1 (HIV-1)-infected cells, and it was shown that SLFN11 inhibited HIV-1 virus production in a codon usage-specific manner. The codon usage patterns of many viruses are vastly different from those of their hosts. The codon usage-specific inhibition of HIV-1 expression by SLFN11 suggests that SLFN11 may be able to inhibit other viruses with a suboptimal codon usage pattern. However, the effect of SLFN11 on the replication of influenza A virus (IAV) has never been reported. The induction of SLFN11 expression was observed upon IAV infection. The reduction of SLFN11 expression also promotes influenza virus replication. Moreover, we found that overexpression of SLFN11 could reduce the expression of a reporter gene with a viral codon usage pattern, and the inhibition of viral hemagglutinin (HA) gene was codon-specific as the expression of codon optimized HA was not affected. These results indicate that SLFN11 inhibits the influenza A virus in a codon-specific manner and that SLFN11 may contribute to innate defense against influenza A viruses.

RhoGAP6 interacts with COPI to regulate protein transport

RhoGAP6 is the most highly expressed GTPase-activating protein (GAP) in platelets specific for RhoA. Structurally RhoGAP6 contains a central catalytic GAP domain surrounded by large, disordered N- and C-termini of unknown function. Sequence analysis revealed three conserved consecutive overlapping di-tryptophan motifs close to the RhoGAP6 C-terminus which were predicted to bind to the mu homology domain (MHD) of δ-COP, a component of the COPI vesicle complex. We confirmed an endogenous interaction between RhoGAP6 and δ-COP in human platelets using GST-CD2AP which binds an N-terminal RhoGAP6 SH3 binding motif. Next, we confirmed that the MHD of δ-COP and the di-tryptophan motifs of RhoGAP6 mediate the interaction between both proteins. Each of the three di-tryptophan motifs appeared necessary for stable δ-COP binding. Proteomic analysis of other potential RhoGAP6 di-tryptophan motif binding partners indicated that the RhoGAP6/δ-COP interaction connects RhoGAP6 to the whole COPI complex. 14-3-3 was also established as a RhoGAP6 binding partner and its binding site was mapped to serine 37. We provide evidence of potential cross-regulation between 14-3-3 and δ-COP binding, however, neither δ-COP nor 14-3-3 binding to RhoGAP6 impacted RhoA activity. Instead, analysis of protein transport through the secretory pathway demonstrated that RhoGAP6/δ-COP binding increased protein transport to the plasma membrane, as did a catalytically inactive mutant of RhoGAP6. Overall, we have identified a novel interaction between RhoGAP6 and δ-COP which is mediated by conserved C-terminal di-tryptophan motifs, and which might control protein transport in platelets.

Integrating Multi-Omics Analysis for Enhanced Diagnosis and Treatment of Glioblastoma: A Comprehensive Data-Driven Approach

The most aggressive primary malignant brain tumor in adults is glioblastoma (GBM), which has poor overall survival (OS). There is a high relapse rate among patients with GBM despite maximally safe surgery, radiation therapy, temozolomide (TMZ), and aggressive treatment. Hence, there is an urgent and unmet clinical need for new approaches to managing GBM. The current study identified modules (MYC, EGFR, PIK3CA, SUZ12, and SPRK2) involved in GBM disease through the NeDRex plugin. Furthermore, hub genes were identified in a comprehensive interaction network containing 7560 proteins related to GBM disease and 3860 proteins associated with signaling pathways involved in GBM. By integrating the results of the analyses mentioned above and again performing centrality analysis, eleven key genes involved in GBM disease were identified. ProteomicsDB and Gliovis databases were used for determining the gene expression in normal and tumor brain tissue. The NetworkAnalyst and the mGWAS-Explorer tools identified miRNAs, SNPs, and metabolites associated with these 11 genes. Moreover, a literature review of recent studies revealed other lists of metabolites related to GBM disease. The enrichment analysis of identified genes, miRNAs, and metabolites associated with GBM disease was performed using ExpressAnalyst, miEAA, and MetaboAnalyst tools. Further investigation of metabolite roles in GBM was performed using pathway, joint pathway, and network analyses. The results of this study allowed us to identify 11 genes (UBC, HDAC1, CTNNB1, TRIM28, CSNK2A1, RBBP4, TP53, APP, DAB1, PINK1, and RELN), five miRNAs (hsa-mir-221-3p, hsa-mir-30a-5p, hsa-mir-15a-5p, hsa-mir-130a-3p, and hsa-let-7b-5p), six metabolites (HDL, N6-acetyl-L-lysine, cholesterol, formate, N, N-dimethylglycine/xylose, and X2. piperidinone) and 15 distinct signaling pathways that play an indispensable role in GBM disease development. The identified top genes, miRNAs, and metabolite signatures can be targeted to establish early diagnostic methods and plan personalized GBM treatment strategies.

Development of allosteric and selective CDK2 inhibitors for contraception with negative cooperativity to cyclin binding

Compared to most ATP-site kinase inhibitors, small molecules that target an allosteric pocket have the potential for improved selectivity due to the often observed lower structural similarity at these distal sites. Despite their promise, relatively few examples of structurally confirmed, high-affinity allosteric kinase inhibitors exist. Cyclin-dependent kinase 2 (CDK2) is a target for many therapeutic indications, including non-hormonal contraception. However, an inhibitor against this kinase with exquisite selectivity has not reached the market because of the structural similarity between CDKs. In this paper, we describe the development and mechanism of action of type III inhibitors that bind CDK2 with nanomolar affinity. Notably, these anthranilic acid inhibitors exhibit a strong negative cooperative relationship with cyclin binding, which remains an underexplored mechanism for CDK2 inhibition. Furthermore, the binding profile of these compounds in both biophysical and cellular assays demonstrate the promise of this series for further development into a therapeutic selective for CDK2 over highly similar kinases like CDK1. The potential of these inhibitors as contraceptive agents is seen by incubation with spermatocyte chromosome spreads from mouse testicular explants, where they recapitulate Cdk2-/- and Spdya-/- phenotypes.

Deep learning from harmonized peptide libraries enables retention time prediction of diverse post translational modifications

In proteomics experiments, peptide retention time (RT) is an orthogonal property to fragmentation when assessing detection confidence. Advances in deep learning enable accurate RT prediction for any peptide from sequence alone, including those yet to be experimentally observed. Here we present Chronologer, an open-source software tool for rapid and accurate peptide RT prediction. Using new approaches to harmonize and false-discovery correct across independently collected datasets, Chronologer is built on a massive database with >2.2 million peptides including 10 common post-translational modification (PTM) types. By linking knowledge learned across diverse peptide chemistries, Chronologer predicts RTs with less than two-thirds the error of other deep learning tools. We show how RT for rare PTMs, such as OGlcNAc, can be learned with high accuracy using as few as 10-100 example peptides in newly harmonized datasets. This iteratively updatable workflow enables Chronologer to comprehensively predict RTs for PTM-marked peptides across entire proteomes.

A previously uncharacterized Factor Associated with Metabolism and Energy (FAME/C14orf105/CCDC198/1700011H14Rik) is related to evolutionary adaptation, energy balance, and kidney physiology

In this study we use comparative genomics to uncover a gene with uncharacterized function (1700011H14Rik/C14orf105/CCDC198), which we hereby name FAME (Factor Associated with Metabolism and Energy). We observe that FAME shows an unusually high evolutionary divergence in birds and mammals. Through the comparison of single nucleotide polymorphisms, we identify gene flow of FAME from Neandertals into modern humans. We conduct knockout experiments on animals and observe altered body weight and decreased energy expenditure in Fame knockout animals, corresponding to genome-wide association studies linking FAME with higher body mass index in humans. Gene expression and subcellular localization analyses reveal that FAME is a membrane-bound protein enriched in the kidneys. Although the gene knockout results in structurally normal kidneys, we detect higher albumin in urine and lowered ferritin in the blood. Through experimental validation, we confirm interactions between FAME and ferritin and show co-localization in vesicular and plasma membranes.

Predicting response and toxicity to immune checkpoint inhibitors in lung cancer using antibodies to frameshift neoantigens

PURPOSE

To evaluate a new class of blood-based biomarkers, anti-frameshift peptide antibodies, for predicting both tumor responses and adverse immune events to immune checkpoint inhibitor (ICI) therapies in advanced lung cancer patients.

EXPERIMENTAL DESIGN

Serum samples were obtained from 74 lung cancer patients prior to palliative PD-(L)1 therapies with subsequently recorded tumor responses and immune adverse events (irAEs). Pretreatment samples were assayed on microarrays of frameshift peptides (FSPs), representing ~ 375,000 variant peptides that tumor cells can be informatically predicted to produce from translated mRNA processing errors. Serum-antibodies specifically recognizing these ligands were measured. Binding activities preferentially associated with best-response and adverse-event outcomes were determined. These antibody bound FSPs were used in iterative resampling analyses to develop predictive models of tumor response and immune toxicity.

RESULTS

Lung cancer serum samples were classified based on predictive models of ICI treatment outcomes. Disease progression was predicted pretreatment with ~ 98% accuracy in the full cohort of all response categories, though ~ 30% of the samples were indeterminate. This model was built with a heterogeneous sample cohort from patients that (i) would show either clear response or stable outcomes, (ii) would be administered either single or combination therapies and (iii) were diagnosed with different lung cancer subtypes. Removing the stable disease, combination therapy or SCLC groups from model building increased the proportion of samples classified while performance remained high. Informatic analyses showed that several of the FSPs in the all-response model mapped to translations of variant mRNAs from the same genes. In the predictive model for treatment toxicities, binding to irAE-associated FSPs provided 90% accuracy pretreatment, with no indeterminates. Several of the classifying FSPs displayed sequence similarity to self-proteins.

CONCLUSIONS

Anti-FSP antibodies may serve as biomarkers for predicting ICI outcomes when tested against ligands corresponding to mRNA-error derived FSPs. Model performances suggest this approach might provide a single test to predict treatment response to ICI and identify patients at high risk for immunotherapy toxicities.

Cold Shock Domain Protein DbpA Orchestrates Tubular Cell Damage and Interstitial Fibrosis in Inflammatory Kidney Disease

DNA-binding protein A (DbpA) belongs to the Y-box family of cold shock domain proteins that exert transcriptional and translational activities in the cell via their ability to bind and regulate mRNA. To investigate the role of DbpA in kidney disease, we utilized the murine unilateral ureter obstruction (UUO) model, which recapitulates many features of obstructive nephropathy seen in humans. We observed that DbpA protein expression is induced within the renal interstitium following disease induction. Compared with wild-type animals, obstructed kidneys from Ybx3-deficient mice are protected from tissue injury, with a significant reduction in the number of infiltrating immune cells as well as in extracellular matrix deposition. RNAseq data from UUO kidneys show that Ybx3 is expressed by activated fibroblasts, which reside within the renal interstitium. Our data support a role for DbpA in orchestrating renal fibrosis and suggest that strategies targeting DbpA may be a therapeutic option to slow disease progression.

Vitamin K-dependent carboxylation regulates Ca2+ flux and adaptation to metabolic stress in β cells

Vitamin K is a micronutrient necessary for γ-carboxylation of glutamic acids. This post-translational modification occurs in the endoplasmic reticulum (ER) and affects secreted proteins. Recent clinical studies implicate vitamin K in the pathophysiology of diabetes, but the underlying molecular mechanism remains unknown. Here, we show that mouse β cells lacking γ-carboxylation fail to adapt their insulin secretion in the context of age-related insulin resistance or diet-induced β cell stress. In human islets, γ-carboxylase expression positively correlates with improved insulin secretion in response to glucose. We identify endoplasmic reticulum Gla protein (ERGP) as a γ-carboxylated ER-resident Ca²⁺-binding protein expressed in β cells. Mechanistically, γ-carboxylation of ERGP protects cells against Ca²⁺ overfilling by diminishing STIM1 and Orai1 interaction and restraining store-operated Ca²⁺ entry. These results reveal a critical role of vitamin K-dependent carboxylation in regulation of Ca²⁺ flux in β cells and in their capacity to adapt to metabolic stress.

The Metal-binding Protein Atlas (MbPA): an integrated database for curating metalloproteins in all aspects

Metal-binding proteins are essential for the vital activities and engage in their roles by acting in concert with metal cations. MbPA (The Metal-binding Protein Atlas) is the most comprehensive resource up to now dedicated to curating metal-binding proteins. Currently, it contains 106373 entries and 440187 sites related to 54 metals and 8169 species. Users can view all metal-binding proteins and species-specific proteins in MbPA. There are also metal-proteomics data that quantitatively describes protein expression in different tissues and organs. By analyzing the data of the amino acid residues at the metal-binding site, it is found that about 80% of the metal ions tend to bind to cysteine, aspartic acid, glutamic acid, and histidine. Moreover, we use Diversity Measure to confirm that the diversity of metal-binding is specific in different area of periodic table, and further elucidate the binding modes of 19 transition metals on 20 amino acids. In addition, MbPA also embraces 6855 potential pathogenic mutations related to metalloprotein. The resource is freely available at http://bioinfor.imu.edu.cn/mbpa.

Quantitative proteomics and phosphoproteomics of PPP2R5D variants reveal deregulation of RPS6 phosphorylation through converging signaling cascades

Variants in the phosphoprotein phosphatase-2 regulatory protein-5D gene ( PPP2R5D ) cause the clinical phenotype of Jordan's Syndrome (PPP2R5D-related disorder), which includes intellectual disability, hypotonia, seizures, macrocephaly, autism spectrum disorder and delayed motor skill development. The disorder originates from de novo single nucleotide mutations, generating missense variants that act in a dominant manner. Pathogenic mutations altering 13 different amino acids have been identified, with the E198K variant accounting for ∼40% of reported cases. Here, we use CRISPR-PRIME genomic editing to introduce a transition (c.592G>A) in the PPP2R5D allele in a heterozygous manner in HEK293 cells, generating E198K-heterozygous lines to complement existing E420K variant lines. We generate global protein and phosphorylation profiles of wild-type, E198K, and E420K cell lines and find unique and shared changes between variants and wild-type cells in kinase- and phosphatase-controlled signaling cascades. As shared signaling alterations, we observed ribosomal protein S6 (RPS6) hyperphosphorylation, indicative of increased ribosomal protein S6-kinase activity. Rapamycin treatment suppressed RPS6 phosphorylation in both, suggesting activation of mTORC1. Intriguingly, our data suggest AKT-dependent (E420K) and -independent (E198K) activation of mTORC1. Thus, although upstream activation of mTORC1 differs between PPP2R5D-related disorder genotypes, treatment with rapamycin or a p70S6K inhibitor warrants further investigation as potential therapeutic strategies for patients.

Machine Learning on Large-Scale Proteomics Data Identifies Tissue and Cell-Type Specific Proteins

Using data from 183 public human data sets from PRIDE, a machine learning model was trained to identify tissue and cell-type specific protein patterns. PRIDE projects were searched with ionbot and tissue/cell type annotation was manually added. Data from physiological samples were used to train a Random Forest model on protein abundances to classify samples into tissues and cell types. Subsequently, a one-vs-all classification and feature importance were used to analyze the most discriminating protein abundances per class. Based on protein abundance alone, the model was able to predict tissues with 98% accuracy, and cell types with 99% accuracy. The F-scores describe a clear view on tissue-specific proteins and tissue-specific protein expression patterns. In-depth feature analysis shows slight confusion between physiologically similar tissues, demonstrating the capacity of the algorithm to detect biologically relevant patterns. These results can in turn inform downstream uses, from identification of the tissue of origin of proteins in complex samples such as liquid biopsies, to studying the proteome of tissue-like samples such as organoids and cell lines.

Pepstatin-Based Probes for Photoaffinity Labeling of Aspartic Proteases and Application to Target Identification

Aspartic proteases are a small class of proteases implicated in a wide variety of human diseases. Covalent chemical probes for photoaffinity labeling (PAL) of these proteases are underdeveloped. We here report a full on-resin synthesis of clickable PAL probes based on the natural product inhibitor pepstatin incorporating a minimal diazirine reactive group. The position of this group in the inhibitor determines the labeling efficiency. The most effective probes sensitively detect cathepsin D, a biomarker for breast cancer, in cell lysates. Moreover, through chemical proteomics experiments and deep learning algorithms, we identified sequestosome-1, an important player in autophagy, as a direct interaction partner and substrate of cathepsin D.

SMPD1 expression profile and mutation landscape help decipher genotype-phenotype association and precision diagnosis for acid sphingomyelinase deficiency

BACKGROUND

Acid sphingomyelinase deficiency (ASMD) disorder, also known as Niemann-Pick disease (NPD) is a rare genetic disease caused by mutations in SMPD1 gene, which encodes sphingomyelin phosphodiesterase (ASM). Except for liver and spleen enlargement and lung disease, two subtypes (Type A and B) of NDP have different onset times, survival times, ASM activities, and neurological abnormalities. To comprehensively explore NPD's genotype-phenotype association and pathophysiological characteristics, we collected 144 NPD cases with strict quality control through literature mining.

RESULTS

The difference in ASM activity can differentiate NPD type A from other subtypes, with the ratio of ASM activity to the reference values being lower in type A (threshold 0.045 (4.45%)). Severe variations, such as deletion and insertion, can cause complete loss of ASM function, leading to type A, whereas relatively mild missense mutations generally result in type B. Among reported mutations, the p.Arg3AlafsX76 mutation is highly prevalent in the Chinese population, and the p.R608del mutation is common in Mediterranean countries. The expression profiles of SMPD1 from GTEx and single-cell RNA sequencing data of multiple fetal tissues showed that high expressions of SMPD1 can be observed in the liver, spleen, and brain tissues of adults and hepatoblasts, hematopoietic stem cells, STC2_TLX1-positive cells, mesothelial cells of the spleen, vascular endothelial cells of the cerebellum and the cerebrum of fetuses, indicating that SMPD1 dysfunction is highly likely to have a significant effect on the function of those cell types during development and the clinicians need pay attention to these organs or tissues as well during diagnosis. In addition, we also predicted 21 new pathogenic mutations in the SMPD1 gene that potentially cause the NPD, signifying that more rare cases will be detected with those mutations in SMPD1. Finally, we also analysed the function of the NPD type A cells following the extracellular milieu.

CONCLUSIONS

Our study is the first to elucidate the effects of SMPD1 mutation on cell types and at the tissue level, which provides new insights into the genotype-phenotype association and can help in the precise diagnosis of NPD.

Toward an Integrated Machine Learning Model of a Proteomics Experiment

In recent years machine learning has made extensive progress in modeling many aspects of mass spectrometry data. We brought together proteomics data generators, repository managers, and machine learning experts in a workshop with the goals to evaluate and explore machine learning applications for realistic modeling of data from multidimensional mass spectrometry-based proteomics analysis of any sample or organism. Following this sample-to-data roadmap helped identify knowledge gaps and define needs. Being able to generate bespoke and realistic synthetic data has legitimate and important uses in system suitability, method development, and algorithm benchmarking, while also posing critical ethical questions. The interdisciplinary nature of the workshop informed discussions of what is currently possible and future opportunities and challenges. In the following perspective we summarize these discussions in the hope of conveying our excitement about the potential of machine learning in proteomics and to inspire future research.

Integrated View of Baseline Protein Expression in Human Tissues

The availability of proteomics datasets in the public domain, and in the PRIDE database, in particular, has increased dramatically in recent years. This unprecedented large-scale availability of data provides an opportunity for combined analyses of datasets to get organism-wide protein abundance data in a consistent manner. We have reanalyzed 24 public proteomics datasets from healthy human individuals to assess baseline protein abundance in 31 organs. We defined tissue as a distinct functional or structural region within an organ. Overall, the aggregated dataset contains 67 healthy tissues, corresponding to 3,119 mass spectrometry runs covering 498 samples from 489 individuals. We compared protein abundances between different organs and studied the distribution of proteins across these organs. We also compared the results with data generated in analogous studies. Additionally, we performed gene ontology and pathway-enrichment analyses to identify organ-specific enriched biological processes and pathways. As a key point, we have integrated the protein abundance results into the resource Expression Atlas, where they can be accessed and visualized either individually or together with gene expression data coming from transcriptomics datasets. We believe this is a good mechanism to make proteomics data more accessible for life scientists.

Machine learning approaches to predict drug efficacy and toxicity in oncology

In recent years, there has been a surge of interest in using machine learning algorithms (MLAs) in oncology, particularly for biomedical applications such as drug discovery, drug repurposing, diagnostics, clinical trial design, and pharmaceutical production. MLAs have the potential to provide valuable insights and predictions in these areas by representing both the disease state and the therapeutic agents used to treat it. To fully utilize the capabilities of MLAs in oncology, it is important to understand the fundamental concepts underlying these algorithms and how they can be applied to assess the efficacy and toxicity of therapeutics. In this perspective, we lay out approaches to represent both the disease state and the therapeutic agents used by MLAs to derive novel insights and make relevant predictions.

Surfaceome Profiling of Cell Lines and Patient-Derived Xenografts Confirm FGFR4, NCAM1, CD276, and Highlight AGRL2, JAM3, and L1CAM as Surface Targets for Rhabdomyosarcoma

Rhabdomyosarcoma (RMS) is the most common soft tissue sarcoma in children. The prognosis for patients with high-grade and metastatic disease is still very poor, and survivors are burdened with long-lasting side effects. Therefore, more effective and less toxic therapies are needed. Surface proteins are ideal targets for antibody-based therapies, like bispecific antibodies, antibody-drug conjugates, or chimeric antigen receptor (CAR) T-cells. Specific surface targets for RMS are scarce. Here, we performed a surfaceome profiling based on differential centrifugation enrichment of surface/membrane proteins and detection by LC-MS on six fusion-positive (FP) RMS cell lines, five fusion-negative (FN) RMS cell lines, and three RMS patient-derived xenografts (PDXs). A total of 699 proteins were detected in the three RMS groups. Ranking based on expression levels and comparison to expression in normal MRC-5 fibroblasts and myoblasts, followed by statistical analysis, highlighted known RMS targets such as FGFR4, NCAM1, and CD276/B7-H3, and revealed AGRL2, JAM3, MEGF10, GPC4, CADM2, as potential targets for immunotherapies of RMS. L1CAM expression was investigated in RMS tissues, and strong L1CAM expression was observed in more than 80% of alveolar RMS tumors, making it a practicable target for antibody-based therapies of alveolar RMS.

Competing endogenous RNA (ceRNA) networks in Parkinson's disease: A systematic review

Parkinson's disease (PD) is a distinctive clinical syndrome with several causes and clinical manifestations. Aside from an infectious cause, PD is a rapidly developing neurological disorder with a global rise in frequency. Notably, improved knowledge of molecular pathways and the developing novel diagnostic methods may result in better therapy for PD patients. In this regard, the amount of research on ceRNA axes is rising, highlighting the importance of these axes in PD. CeRNAs are transcripts that cross-regulate one another via competition for shared microRNAs (miRNAs). These transcripts may be either coding RNAs (mRNAs) or non-coding RNAs (ncRNAs). This research used a systematic review to assess validated loops of ceRNA in PD. The Prisma guideline was used to conduct this systematic review, which entailed systematically examining the articles of seven databases. Out of 309 entries, forty articles met all criteria for inclusion and were summarized in the appropriate table. CeRNA axes have been described through one of the shared vital components of the axes, including lncRNAs such as NEAT1, SNHG family, HOTAIR, MALAT1, XIST, circRNAs, and lincRNAs. Understanding the multiple aspects of this regulatory structure may aid in elucidating the unknown causal causes of PD and providing innovative molecular therapeutic targets and medical fields.

Expression of factor XIII originating from synovial fibroblasts and macrophages induced by interleukin-6 signaling

BACKGROUND

Blood coagulation factor XIII (FXIII) promotes cross-linking between fibrin molecules at the final stage of the blood coagulation cascade. However, its expression in cells or tissues and function, particularly factor XIII subunit B (FXIII-B), remains controversial. Hemorrhagic FXIII deficiency following anti-interleukin-6 (IL-6) receptor antibody treatment has been reported in patients with rheumatoid arthritis (RA). Patients receiving this biologics have reduced FXIII activity when compared to the activity in those treated with other biologics. The relationship between pro-inflammatory cytokines and FXIII expression remains unknown.

METHODS

To investigate the expression pattern of FXIII in synovial tissues, immunohistochemistry, RT-qPCR, and western blotting were performed. FXIII-A expressed monocyte-derived macrophages were treated with recombinant IL-6 and anti-IL-6 receptor antibody. RNA sequencing of FXIII-B-overexpressing cells was performed to clarify the function of FXIII-B.

RESULTS

The immunohistochemical analysis of synovial tissues revealed that factor XIII subunit A (FXIII-A) was expressed in M2 macrophages, and FXIII-B was expressed in fibroblast-like synoviocytes. IL-6 stimulation upregulated FXIII-A expression in IL-4-induced monocyte-derived macrophages, and the anti-IL-6 receptor antibody suppressed FXIII-A expression. FXIII-B was more abundantly secreted in the supernatant of fibroblast-like synoviocytes compared with that of other cells. RNA sequencing showed that FXIII-B elevated the expression of genes associated with anti-apoptotic molecules and chemokines.

CONCLUSIONS

Our findings highlight that synovial tissue is one of the sources of FXIII production. We also have demonstrated IL-6-dependent FXIII-A expression and the novel potential functions of FXIII-B.

Exome-wide association analysis of CT imaging-derived hepatic fat in a medical biobank

Nonalcoholic fatty liver disease is common and highly heritable. Genetic studies of hepatic fat have not sufficiently addressed non-European and rare variants. In a medical biobank, we quantitate hepatic fat from clinical computed tomography (CT) scans via deep learning in 10,283 participants with whole-exome sequences available. We conduct exome-wide associations of single variants and rare predicted loss-of-function (pLOF) variants with CT-based hepatic fat and perform cross-modality replication in the UK Biobank (UKB) by linking whole-exome sequences to MRI-based hepatic fat. We confirm single variants previously associated with hepatic fat and identify several additional variants, including two (FGD5 H600Y and CITED2 S198_G199del) that replicated in UKB. A burden of rare pLOF variants in LMF2 is associated with increased hepatic fat and replicates in UKB. Quantitative phenotypes generated from clinical imaging studies and intersected with genomic data in medical biobanks have the potential to identify molecular pathways associated with human traits and disease.

Increased CD34 in pancreatic islet negatively predict islet β-cell decrease in type1 diabetes model

Islet β-cell biomarkers can reflect changes in the number and function of islet β-cells in the prediabetes or early diabetes stage. CD34 is a commonly used stem cell biomarker; however, its expression and function in pancreatic islets remain unclear. In the present study, double immunofluorescence staining, proteomic bioinformatics analysis, and correlation analysis were used to explore the potential of CD34 as an islet β-cell biomarker. Bioinformatics analysis revealed that the amino acid sequence of CD34 was conserved among multiple species and abundantly expressed on mouse and human pancreatic tissues. Immunofluorescence demonstrated that in the control rat pancreas, CD34 was expressed on glucagon-labeled islet α-cells but not on insulin-labeled islet β-cells. Furthermore, the proportion of CD34-positive cells, which were also positive for glucagon, was significantly increased in alloxan-induced diabetes models. Statistical analysis revealed that the expression of CD34 was negatively correlated with the number of insulin-labeled islet β-cells during diabetes progression in dose-dependent fashion in alloxan-induced diabetes models. Furthermore, the results suggested that the transdifferentiation of islet β-cells into islet α-cells may occur in the process of diabetes. Thus, the present study demonstrated that CD34 is expressed on islet α-cells, and its number is linearly and negatively correlated with the number of islet β-cells, suggesting that CD34 can be used as a prospective biomarker for islet β-cells in the early diagnosis of diabetes. The study also suggests the transformation of β-cells to α-cells in diabetes which provide a potential to be applied towards diabetes mechanism research.

T Cells Directed against the Metastatic Driver Chondromodulin-1 in Ewing Sarcoma: Comparative Engineering with CRISPR/Cas9 vs. Retroviral Gene Transfer for Adoptive Transfer

Ewing sarcoma (EwS) is a highly malignant sarcoma of bone and soft tissue with early metastatic spread and an age peak in early puberty. The prognosis in advanced stages is still dismal, and the long-term effects of established therapies are severe. Efficacious targeted therapies are urgently needed. Our previous work has provided preliminary safety and efficacy data utilizing T cell receptor (TCR) transgenic T cells, generated by retroviral gene transfer, targeting HLA-restricted peptides on the tumor cell derived from metastatic drivers. Here, we compared T cells engineered with either CRISPR/Cas9 or retroviral gene transfer. Firstly, we confirmed the feasibility of the orthotopic replacement of the endogenous TCR by CRISPR/Cas9 with a TCR targeting our canonical metastatic driver chondromodulin-1 (CHM1). CRISPR/Cas9-engineered T cell products specifically recognized and killed HLA-A*02:01+ EwS cell lines. The efficiency of retroviral transduction was higher compared to CRISPR/Cas9 gene editing. Both engineered T cell products specifically recognized tumor cells and elicited cytotoxicity, with CRISPR/Cas9 engineered T cells providing prolonged cytotoxic activity. In conclusion, T cells engineered with CRISPR/Cas9 could be feasible for immunotherapy of EwS and may have the advantage of more prolonged cytotoxic activity, as compared to T cells engineered with retroviral gene transfer.

Follistatin-like 1 and its paralogs in heart development and cardiovascular disease

Cardiovascular diseases (CVDs) are a group of disorders affecting the heart and blood vessels and a leading cause of death worldwide. Thus, there is a need to identify new cardiokines that may protect the heart from damage as reported in GBD 2017 Causes of Death Collaborators (2018) (The Lancet 392:1736-1788). Follistatin-like 1 (FSTL1) is a cardiokine that is highly expressed in the heart and released to the serum after cardiac injury where it is associated with CVD and predicts poor outcome. The action of FSTL1 likely depends not only on the tissue source but also post-translation modifications that are target tissue- and cell-specific. Animal studies examining the effect of FSTL1 in various models of heart disease have exploded over the past 15 years and primarily report a protective effect spanning from inhibiting inflammation via transforming growth factor, preventing remodeling and fibrosis to promoting angiogenesis and hypertrophy. A better understanding of FSTL1 and its homologs is needed to determine whether this protein could be a useful novel biomarker to predict poor outcome and death and whether it has therapeutic potential. The aim of this review is to provide a comprehensive description of the literature for this family of proteins in order to better understand their role in normal physiology and CVD.

Shared 6mer Peptides of Human and Omicron (21K and 21L) at SARS-CoV-2 Mutation Sites

We investigated the short sequences involving Omicron 21K and Omicron 21L variants to reveal any possible molecular mimicry-associated autoimmunity risks and changes in those. We first identified common 6mers of the viral and human protein sequences present for both the mutant (Omicron) and nonmutant (SARS-CoV-2) versions of the same viral sequence and then predicted the binding affinities of those sequences to the HLA supertype representatives. We evaluated change in the potential autoimmunity risk, through comparative assessment of the nonmutant and mutant viral sequences and their similar human peptides with common 6mers and affinities to the same HLA allele. This change is the lost and the new, or de novo, autoimmunity risk, associated with the mutations in the Omicron 21K and Omicron 21L variants. Accordingly, e.g., the affinity of virus-similar sequences of the Ig heavy chain junction regions shifted from the HLA-B*15:01 to the HLA-A*01:01 allele at the mutant sequences. Additionally, peptides of different human proteins sharing 6mers with SARS-CoV-2 proteins at the mutation sites of interest and with affinities to the HLA-B*07:02 allele, such as the respective SARS-CoV-2 sequences, were lost. Among all, any possible molecular mimicry-associated novel risk appeared to be prominent in HLA-A*24:02 and HLA-B*27:05 serotypes upon infection with Omicron 21L. Associated disease, pathway, and tissue expression data supported possible new risks for the HLA-B*27:05 and HLA-A*01:01 serotypes, while the risks for the HLA-B*07:02 serotypes could have been lost or diminished, and those for the HLA-A*03:01 serotypes could have been retained, for the individuals infected with Omicron variants under study. These are likely to affect the complications related to cross-reactions influencing the relevant HLA serotypes upon infection with Omicron 21K and Omicron 21L.

Proteomics as a tool to improve novel insights into skin diseases: what we know and where we should be going

Background

Biochemical processes involved in complex skin diseases (skin cancers, psoriasis, and wound) can be identified by combining proteomics analysis and bioinformatics tools, which gain a next-level insight into their pathogenesis, diagnosis, and therapeutic targets.

Methods

Articles were identified through a search of PubMed, Embase, and MEDLINE references dated to May 2022, to perform system data mining, and a search of the Web of Science (WoS) Core Collection was utilized to conduct a visual bibliometric analysis.

Results

An increased trend line revealed that the number of publications related to proteomics utilized in skin diseases has sharply increased recent years, reaching a peak in 2021. The hottest fields focused on are skin cancer (melanoma), inflammation skin disorder (psoriasis), and skin wounds. After deduplication and title, abstract, and full-text screening, a total of 486 of the 7,822 outcomes met the inclusion/exclusion criteria for detailed data mining in the field of skin disease tooling with proteomics, with regard to skin cancer. According to the data, cell death, metabolism, skeleton, immune, and inflammation enrichment pathways are likely the major part and hotspots of proteomic analysis found in skin diseases. Also, the focuses of proteomics in skin disease are from superficial presumption to depth mechanism exploration within more comprehensive validation, from basic study to a combination or guideline for clinical applications. Furthermore, we chose skin cancer as a typical example, compared with other skin disorders. In addition to finding key pathogenic proteins and differences between diseases, proteomic analysis is also used for therapeutic evaluation or can further obtain in-depth mechanisms in the field of skin diseases.

Conclusion

Proteomics has been regarded as an irreplaceable technology in the study of pathophysiological mechanism and/or therapeutic targets of skin diseases, which could provide candidate key proteins for the insight into the biological information after gene transcription. However, depth pathogenesis and potential clinical applications need further studies with stronger evidence within a wider range of skin diseases.

NIMA-related kinase 9 regulates the phosphorylation of the essential myosin light chain in the heart

To adapt to changing hemodynamic demands, regulatory mechanisms modulate actin-myosin-kinetics by calcium-dependent and -independent mechanisms. We investigate the posttranslational modification of human essential myosin light chain (ELC) and identify NIMA-related kinase 9 (NEK9) to interact with ELC. NEK9 is highly expressed in the heart and the interaction with ELC is calcium-dependent. Silencing of NEK9 results in blunting of calcium-dependent ELC-phosphorylation. CRISPR/Cas9-mediated disruption of NEK9 leads to cardiomyopathy in zebrafish. Binding to ELC is mediated via the protein kinase domain of NEK9. A causal relationship between NEK9 activity and ELC-phosphorylation is demonstrated by genetic sensitizing in-vivo. Finally, we observe significantly upregulated ELC-phosphorylation in dilated cardiomyopathy patients and provide a unique map of human ELC-phosphorylation-sites. In summary, NEK9-mediated ELC-phosphorylation is a calcium-dependent regulatory system mediating cardiac contraction and inotropy.

Depletion of PARP10 inhibits the growth and metastatic potential of oral squamous cell carcinoma

Background: Although poly (ADP-ribose) polymerase family member 10 (PARP10) has been implicated in the progression of multiple cancer types, its role in oral squamous cell carcinoma (OSCC) remains unknown. This study aimed to examine the function of PARP10 in OSCC and investigate the underlying mechanisms.

Methods: The expression of PARP10 in OSCC was investigated in OSCC patient cohorts. Kaplan-Meier curve analysis was performed to assess the association between PARP10 and prognosis in OSCC. Correlation between PARP10 expression and the related variables was analyzed by χ² test. CKK-8, transwell assay, western blot, immunohistochemistry, immunofluorescence, and bioinformatic analysis, were applied to clarify the role of PARP10 in OSCC.

Results: PARP10 was found to be markedly elevated in OSCC tissues. The upregulation of PARP10 predicted shorter overall survival and disease-specific survival and was significantly correlated with several malignant features. Moreover, depletion of PARP10 markedly inhibited the proliferation, migration, and invasion of OSCC cells, and promoted OSCC cell apoptosis, and resulted in alterations of relevant proteins. Furthermore, a positive correlation was observed between the expression of PARP10 and Ki67, PARP1, MMP2, and VEGF. In addition, depletion of PARP10 impaired the PI3K-AKT and MAPK signaling pathways.

Conclusion: PARP10 is involved in the progression of OSCC via regulation of PI3K-AKT and MAPK signaling pathways.

Loss of Ecrg4 improves calcium oxalate nephropathy

Kidney stone is one of the most frequent urinary tract diseases, affecting 10% of the population and displaying a high recurrence rate. Kidney stones are the result of salt supersaturation, including calcium and oxalate. We have previously identified Esophageal cancer-related gene 4 (Ecrg4) as being modulated by hypercalciuria. Ecrg4 was initially described as a tumor suppressor gene in the esophagus. Lately, it was shown to be involved as well in apoptosis, cell senescence, cell migration, inflammation and cell responsiveness to chemotherapy. To the best of our knowledge, nothing is known about ECRG4's function in the renal tissue and its relationship with calciuria. We hypothesized that the increased expression of Ecrg4 mRNA is triggered by hypercalciuria and might modulate intratubular calcium-oxalate precipitation. In this study, we have first (i) validated the increased Ecrg4 mRNA in several types of hypercalciuric mouse models, then (ii) described the Ecrg4 mRNA expression along the nephron and (iii) assessed ECRG4's putative role in calcium oxalate nephropathy. For this, Ecrg4 KO mice were challenged with a kidney stone-inducing diet, rich in calcium and oxalate precursor. Taken together, our study demonstrates that Ecrg4's expression is restricted mainly to the distal part of the nephron and that the Ecrg4 KO mice develop less signs of tubular obstruction and less calcium-oxalate deposits. This promotes Ecrg4 as a modulator of renal crystallization and may open the way to new therapeutic possibilities against calcium oxalate nephropathy.

Characterization of the functional effects of ferredoxin 1 as a cuproptosis biomarker in cancer

Background: Cuproptosis is a recently discovered form of programmed cell death. Ferredoxin 1 (FDX1) is a key gene that mediates this process. However, the role of FDX1 in human tumors is not clear.

Methods: We comprehensively analyzed the differential expression and genetic alterations of FDX1 using multiomics data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database. Subsequently, we explored the association between FDX1 and tumor parameters such as genomic instability, RNA methylation modifications, immune infiltration and pathway activity. In addition, we performed functional enrichment analysis and assessed the sensitivity potential of FDX1-related drugs. Finally, we experimentally verified the functional effects of FDX1.

Results: The analysis revealed differential expression of FDX1 in a variety of tumors. By analyzing the association of FDX1 expression with genomic instability, immune cell infiltration, signaling pathway etc. We explored the role of FDX1 in regulating cell activity. Also, we evaluated the function of FDX1 in biologic process and drug sensitivity. Our experimental results demonstrated that FDX1 exerts its antitumor effects through cuproptosis in liver hepatocellular carcinoma and non-small cell lung cancer cell lines.

Conclusion: Our study reveals the functional effects of FDX1 in tumors and deepens the understanding of the effects of FDX1. We validated the inhibitory effect of FDX1 in copper induced cell-death, confirming the role of FDX1 as a cuproptosis biomarker.

Multi-Omics Interdisciplinary Research Integration to Accelerate Dementia Biomarker Development (MIRIADE)

Proteomics studies have shown differential expression of numerous proteins in dementias but have rarely led to novel biomarker tests for clinical use. The Marie Curie MIRIADE project is designed to experimentally evaluate development strategies to accelerate the validation and ultimate implementation of novel biomarkers in clinical practice, using proteomics-based biomarker development for main dementias as experimental case studies. We address several knowledge gaps that have been identified in the field. First, there is the technology-translation gap of different technologies for the discovery (e.g., mass spectrometry) and the large-scale validation (e.g., immunoassays) of biomarkers. In addition, there is a limited understanding of conformational states of biomarker proteins in different matrices, which affect the selection of reagents for assay development. In this review, we aim to understand the decisions taken in the initial steps of biomarker development, which is done via an interim narrative update of the work of each ESR subproject. The results describe the decision process to shortlist biomarkers from a proteomics to develop immunoassays or mass spectrometry assays for Alzheimer's disease, Lewy body dementia, and frontotemporal dementia. In addition, we explain the approach to prepare the market implementation of novel biomarkers and assays. Moreover, we describe the development of computational protein state and interaction prediction models to support biomarker development, such as the prediction of epitopes. Lastly, we reflect upon activities involved in the biomarker development process to deduce a best-practice roadmap for biomarker development.