Disease Signatures Research Articles

Identification of common MicroRNAs expression signatures in antiphospholipid syndrome and thromboembolic disease: A scoping review.

Antiphospholipid syndrome (APS) is an acquired autoimmune disorder characterized by distinct pathophysiological mechanisms leading to heterogeneous manifestations, including venous and arterial thrombosis. Despite the lack of specific markers of thrombosis risk in APS, some of the mechanisms responsible for thrombosis in APS may overlap with those of other thromboembolic diseases. Understanding these similarities is important for improving the assessment of thrombosis risk in APS. MicroRNAs (MiRNAs) are RNA molecules that regulate gene expression and may influence the autoimmune response and coagulation. In this scoping review we aimed to investigate shared miRNAs profiles associated with APS and other thromboembolic diseases as a means of identifying markers indicative of a pro-thrombotic profile among patients with APS. Through a comprehensive search of scientific databases, 45 relevant studies were identified out of 1020 references. miRs-124-3p, 125b-5p, 125a-5p, and 17-5p, were associated with APS and arterial thrombosis, while miRs-106a-5p, 146b-5p, 15a-5p, 222-3p, and 451a were associated with APS and venous thrombosis. Additionally, miR-126a-3p was associated with APS and both arterial and venous thrombosis. We observed that APS shares a common miRNAs signature with non-APS related thrombosis, suggesting that miRNA expression profiles may serve as markers of thrombotic risk in APS. Further validation of a pro-thrombotic miRNA signature in APS is warranted to improve risk assessment, diagnosis, and management of APS.

Identification of a novel immune infiltration-related gene signature, MCEMP1, for coronary artery disease.

This study aims to identify a novel gene signature for coronary artery disease (CAD), explore the role of immune cell infiltration in CAD pathogenesis, and assess the cell function of mast cell-expressed membrane protein 1 (MCEMP1) in human umbilical vein endothelial cells (HUVECs) treated with oxidized low-density lipoprotein (ox-LDL). To identify differentially expressed genes (DEGs) of CAD, datasets GSE24519 and GSE61145 were downloaded from the Gene Expression Omnibus (GEO) database using the R "limma" package with p < 0.05 and |log2 FC| > 1. Gene ontology (GO) and pathway analyses were conducted to determine the biological functions of DEGs. Hub genes were identified using support vector machine-recursive feature elimination (SVM-RFE) and least absolute shrinkage and selection operator (LASSO). The expression levels of these hub genes in CAD were validated using the GSE113079 dataset. CIBERSORT program was used to quantify the proportion of immune cell infiltration. Western blot assay and qRT-PCR were used to detect the expression of hub genes in ox-LDL-treated HUVECs to validate the bioinformatics results. Knockdown interference sequences for MCEMP1 were synthesized, and cell proliferation and apoptosis were examined using a CCK8 kit and Muse® Cell Analyzer, respectively. The concentrations of IL-1β, IL-6, and TNF-α were measured with respective enzyme-linked immunosorbent assay (ELISA) kits. A total of 73 DEGs (four down-regulated genes and 69 up-regulated genes) were identified in the metadata (GSE24519 and GSE61145) cohort. GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis results indicated that these DEGs might be associated with the regulation of platelet aggregation, defense response or response to bacterium, NF-kappa B signaling pathway, and lipid and atherosclerosis. Using SVM-RFE and LASSO, seven hub genes were obtained from the metadata. The upregulated expression of DIRC2 and MCEMP1 in CAD was confirmed in the GSE113079 dataset and in ox-LDL-treated HUVECs. The associations between the two hub genes (DIRC2 and MCEMP1) and the 22 types of immune cell infiltrates in CAD were found. MCEMP1 knockdown accelerated cell proliferation and suppressed cell apoptosis for ox-LDL-treated HUVECs. Additionally, MCEMP1 knockdown appeared to decrease the expression of inflammatory factors IL-1β, IL-6, and TNF-α. The results of this study indicate that MCEMP1 may play an important role in CAD pathophysiology.

Deep humoral profiling coupled to interpretable machine learning unveils diagnostic markers and pathophysiology of schistosomiasis.

Schistosomiasis, a highly prevalent parasitic disease, affects more than 200 million people worldwide. Current diagnostics based on parasite egg detection in stool detect infection only at a late stage, and current antibody-based tests cannot distinguish past from current infection. Here, we developed and used a multiplexed antibody profiling platform to obtain a comprehensive repertoire of antihelminth humoral profiles including isotype, subclass, Fc receptor (FcR) binding, and glycosylation profiles of antigen-specific antibodies. Using Essential Regression (ER) and SLIDE, interpretable machine learning methods, we identified latent factors (context-specific groups) that move beyond biomarkers and provide insights into the pathophysiology of different stages of schistosome infection. By comparing profiles of infected and healthy individuals, we identified modules with unique humoral signatures of active disease, including hallmark signatures of parasitic infection such as elevated immunoglobulin G4 (IgG4). However, we also captured previously uncharacterized humoral responses including elevated FcR binding and specific antibody glycoforms in patients with active infection, helping distinguish them from those without active infection but with equivalent antibody titers. This signature was validated in an independent cohort. Our approach also uncovered two distinct endotypes, nonpatent infection and prior infection, in those who were not actively infected. Higher amounts of IgG1 and FcR1/FcR3A binding were also found to be likely protective of the transition from nonpatent to active infection. Overall, we unveiled markers for antibody-based diagnostics and latent factors underlying the pathogenesis of schistosome infection. Our results suggest that selective antigen targeting could be useful in early detection, thus controlling infection severity.

Genomic signature for oligometastatic disease in non-small cell lung cancer patients with brain metastases.

Biomarkers for extracranial oligometastatic disease remain elusive and few studies have attempted to correlate genomic data to the presence of true oligometastatic disease. Patients with non-small cell lung cancer (NSCLC) and brain metastases were identified in our departmental database. Electronic medical records were used to identify patients for whom liquid biopsy-based comprehensive genomic profiling (Guardant Health) was available. Extracranial oligometastatic disease was defined as patients having ≤5 non-brain metastases without diffuse involvement of a single organ. Widespread disease was any spread beyond oligometastatic. Fisher's exact tests were used to screen for mutations statistically associated (p<0.1) with either oligometastatic or widespread extracranial disease. A risk score for the likelihood of oligometastatic disease was generated and correlated to the likelihood of having oligometastatic disease vs widespread disease. For oligometastatic patients, a competing risk analysis was done to assess for cumulative incidence of oligometastatic progression. Cox regression was used to determine association between oligometastatic risk score and oligoprogression. 130 patients met study criteria and were included in the analysis. 51 patients (39%) had extracranial oligometastatic disease. Genetic mutations included in the Guardant panel that were associated (p<0.1) with the presence of oligometastatic disease included ATM, JAK2, MAP2K2, and NTRK1, while ARID1A and CCNE1 were associated with widespread disease. Patients with a positive, neutral and negative risk score for oligometastatic disease had a 78%, 41% and 11.5% likelihood of having oligometastatic disease, respectively (p<0.0001). Overall survival for patients with positive, neutral and negative risk scores for oligometastatic disease was 86% vs 82% vs 64% at 6 months (p=0.2). Oligometastatic risk score was significantly associated with the likelihood of oligoprogression based on the Wald chi-square test. Patients with positive, neutral and negative risk scores for oligometastatic disease had a cumulative incidence of oligometastatic progression of 77% vs 35% vs 33% at 6 months (p=0.03). Elucidation of a genomic signature for extracranial oligometastatic disease derived from non-invasive liquid biopsy appears feasible for NSCLC patients. Patients with this signature exhibited higher rates of early oligoprogression. External validation could lead to a biomarker that has the potential to direct local therapies in oligometastatic patients.

Quantitative proteomics of patient fibroblasts reveal biomarkers and diagnostic signatures of mitochondrial disease.

BACKGROUNDMitochondrial diseases belong to the group of inborn errors of metabolism (IEM), with a prevalence of 1 in 2,000-5,000 individuals. They are the most common form of IEM, but, despite advances in next-generation sequencing technologies, almost half of the patients are left genetically undiagnosed.METHODSWe investigated a cohort of 61 patients with defined mitochondrial disease to improve diagnostics, identify biomarkers, and correlate metabolic pathways to specific disease groups. Clinical presentations were structured using human phenotype ontology terms, and mass spectrometry-based proteomics was performed on primary fibroblasts. Additionally, we integrated 6 patients carrying variants of uncertain significance (VUS) to test proteomics as a diagnostic expansion.RESULTSProteomic profiles from patient samples could be classified according to their biochemical and genetic characteristics, with the expression of 5 proteins (GPX4, MORF4L1, MOXD1, MSRA, and TMED9) correlating with the disease cohort, thus acting as putative biomarkers. Pathway analysis showed a deregulation of inflammatory and mitochondrial stress responses. This included the upregulation of glycosphingolipid metabolism and mitochondrial protein import, as well as the downregulation of arachidonic acid metabolism. Furthermore, we could assign pathogenicity to a VUS in MRPS23 by demonstrating the loss of associated mitochondrial ribosome subunits.CONCLUSIONWe established mass spectrometry-based proteomics on patient fibroblasts as a viable and versatile tool for diagnosing patients with mitochondrial disease.FUNDINGThe NovoNordisk Foundation, Knut and Alice Wallenberg Foundation, Wellcome Centre for Mitochondrial Research, UK Medical Research Council, and the UK NHS Highly Specialised Service for Rare Mitochondrial Disorders of Adults and Children.

Eicosatetraynoic Acid Regulates Pro-Fibrotic Pathways in an Induced Pluripotent Stem Cell Derived Macrophage:Human Intestinal Organoid Model of Crohn's Disease.

We previously identified small molecules predicted to reverse an ileal gene signature for future Crohn's Disease (CD) strictures. Here we used a new human intestinal organoid (HIO) model system containing macrophages to test a lead candidate, eicosatetraynoic acid (ETYA). Induced pluripotent stem cell lines (iPSC) were derived from CD patients and differentiated into macrophages and HIOs. Macrophages and macrophage:HIO co-cultures were exposed to lipopolysaccharide (LPS) with and without ETYA pre-treatment. Cytospin and flow cytometry characterized macrophage morphology and activation markers, and RNA sequencing defined the global pattern of macrophage gene expression. TaqMan Low Density Array, Luminex multiplex assay, immunohistologic staining, and sirius red polarized light microscopy were performed to measure macrophage cytokine production and HIO pro-fibrotic gene expression and collagen content. iPSC-derived macrophages exhibited morphology similar to primary macrophages and expressed inflammatory macrophage cell surface markers including CD64 and CD68. LPS-stimulated macrophages expressed a global pattern of gene expression enriched in CD ileal inflammatory macrophages and matrisome secreted products, and produced cytokines and chemokines including CCL2, IL1B, and OSM implicated in refractory disease. ETYA suppressed CD64 abundance and pro-fibrotic gene expression pathways in LPS stimulated macrophages. Co-culture of LPS-primed macrophages with HIO led to up-regulation of fibroblast activation genes including ACTA2 and COL1A1, and an increase in HIO collagen content. ETYA pre-treatment prevented pro-fibrotic effects of LPS-primed macrophages. ETYA inhibits pro-fibrotic effects of LPS-primed macrophages upon co-cultured HIO. This model may be used in future untargeted screens for small molecules to treat refractory CD.

Signature Search Polestar: a comprehensive drug repurposing method evaluation assistant for customized oncogenic signature.

The burgeoning high-throughput technologies have led to a significant surge in the scale of pharmacotranscriptomic datasets, especially for oncology. Signature search methods (SSMs), utilizing oncogenic signatures formed by differentially expressed genes through sequencing, have been instrumental in anti-cancer drug screening and identifying mechanisms of action without relying on prior knowledge. However, various studies have found that different SSMs exhibit varying performance across pharmacotranscriptomic datasets. In addition, the size of the oncogenic signature can also significantly impact the result of drug repurposing. Therefore, finding the optimal SSMs and customized oncogenic signature for a specific disease remains a challenge. To address this, we introduce Signature Search Polestar (SSP), a webserver integrating the largest pharmacotranscriptomic datasets of anti-cancer drugs from LINCS L1000 with five state-of-the-art SSMs (XSum, CMap, GSEA, ZhangScore, XCos). SSP provides three main modules: Benchmark, Robustness, and Application. Benchmark uses two indices, Area Under the Curve and Enrichment Score, based on drug annotations to evaluate SSMs at different oncogenic signature sizes. Robustness, applicable when drug annotations are insufficient, uses a performance score based on drug self-retrieval for evaluation. Application provides three screening strategies, single method, SS_all, and SS_cross, allowing users to freely utilize optimal SSMs with tailored oncogenic signature for drug repurposing. SSP is free at https://web.biotcm.net/SSP/. The current version of SSP is archived in https://doi.org/10.6084/m9.figshare.26524741.v1, allowing users to directly use or customize their own SSP webserver.

Socioeconomic status across the early life course predicts gene expression signatures of disease and senescence

BackgroundSocioeconomic status (SES) is associated with many chronic diseases, indicators of senescence and mortality. However, the changing salience of SES in the prediction of adult health is not well understood....

Metabolism of tRNAs and rRNAs shape immunoactive signatures in chronic obstructive pulmonary disease and pulmonary infections

Metabolism of tRNAs and rRNAs shape immunoactive signatures in chronic obstructive pulmonary disease and pulmonary infections

Integration and validation of host transcript signatures, including a novel 3-transcript tuberculosis signature, to enable one-step multiclass diagnosis of childhood febrile disease

BackgroundWhole blood host transcript signatures show great potential for diagnosis of infectious and inflammatory illness, with most published signatures performing binary classification tasks. Barriers to clinical implementation include validation studies, and development of strategies that enable simultaneous, multiclass diagnosis of febrile illness based on gene expression.MethodsWe validated five distinct diagnostic signatures for paediatric infectious diseases in parallel using a single NanoString nCounter® experiment. We included a novel 3-transcript signature for childhood tuberculosis, and four published signatures which differentiate bacterial infection, viral infection, or Kawasaki disease from other febrile illnesses. Signature performance was assessed using receiver operating characteristic curve statistics. We also explored conceptual frameworks for multiclass diagnostic signatures, including additional transcripts found to be significantly differentially expressed in previous studies. Relaxed, regularised logistic regression models were used to derive two novel multiclass signatures: a mixed One-vs-All model (MOVA), running multiple binomial models in parallel, and a full-multiclass model. In-sample performance of these models was compared using radar-plots and confusion matrix statistics.ResultsSamples from 91 children were included in the study: 23 bacterial infections (DB), 20 viral infections (DV), 14 Kawasaki disease (KD), 18 tuberculosis disease (TB), and 16 healthy controls. The five signatures tested demonstrated cross-platform performance similar to their primary discovery-validation cohorts. The signatures could differentiate: KD from other diseases with area under ROC curve (AUC) of 0.897 [95% confidence interval: 0.822–0.972]; DB from DV with AUC of 0.825 [0.691–0.959] (signature-1) and 0.867 [0.753–0.982] (signature-2); TB from other diseases with AUC of 0.882 [0.787–0.977] (novel signature); TB from healthy children with AUC of 0.910 [0.808–1.000]. Application of signatures outside of their designed context reduced performance. In-sample error rates for the multiclass models were 13.3% for the MOVA model and 0.0% for the full-multiclass model. The MOVA model misclassified DB cases most frequently (18.7%) and TB cases least (2.7%).ConclusionsOur study demonstrates the feasibility of NanoString technology for cross-platform validation of multiple transcriptomic signatures in parallel. This external cohort validated performance of all five signatures, including a novel sparse TB signature. Two exploratory multi-class models showed high potential accuracy across four distinct diagnostic groups.

Integrative in silico approaches to analyse microRNA-mediated responses in human diseases.

Advancements in sequencing technologies have facilitated omics level information generation for various diseases in human. High-throughput technologies have become a powerful tool to understand differential expression studies and transcriptional network analysis. An understanding of complex transcriptional networks in human diseases requires integration of datasets representing different RNA species including microRNA (miRNA) and messenger RNA (mRNA). This review emphasises on conceptual explanation of generalized workflow and methodologies to the miRNA mediated responses in human diseases by using different in silico analysis. Although,there have been many prior explorations in miRNA-mediated responses in human diseases, the advantages, limitations and overcoming the limitation through different statistical techniques have not yet been discussed. This review focuses on miRNAs as important gene regulators in human diseases, methodologies for miRNA-target gene prediction and data driven methods for enrichment and network analysis for miRnome-targetome interactions. Additionally, it proposes an integrative workflow to analyse structural components of networks obtained from high-throughput data. This review explains how to apply the existing methods to analyse miRNA-mediated responses in human diseases. It addresses unique characteristics of different analysis, its limitations and its statistical solutions influencing the choice of methods for the analysis through a workflow. Moreover, it provides an overview of promising common integrative approaches to comprehend miRNA-mediated gene regulatory events in biological processes in humans. The proposed methodologies and workflow shall help in the analysis of multi-source data to identify molecular signatures of various human diseases.

Brain Proteome Profiling Reveals Common and Divergent Signatures in Parkinson's Disease, Multiple System Atrophy, and Progressive Supranuclear Palsy.

The molecular pathogenesis of degenerative parkinsonisms, including Parkinson's disease (PD), progressive supranuclear palsy (PSP), and Multiple system atrophy (MSA), remains largely unknown. To gain novel insight into molecular processes associated with these diseases, we conducted a proteome-wide expression study in prefrontal cortex tissue from a cohort of 181 individuals, comprising PD (N = 73), PSP (N = 18), MSA (N = 17) and healthy control (N = 73). Using marker gene profiles, we first assess the cellular composition of the samples and, subsequently, identify distinct protein signatures for each disease, while correcting for cell composition. Our findings indicate that all three diseases are characterized by a structural and/or functional loss of deep cortical neurons, while PD exhibits an additional decrease in somatostatin-expressing interneurons, as well as in endothelial cells. Differential protein expression analysis identified multiple proteins and pathways with disease-specific expression, some of which have previously been associated with parkinsonism or neurodegeneration in general. Notably, we observed a strong mitochondrial signature which was present in both PD and PSP, albeit of a different composition and most pronounced in PSP, but not in MSA where immunological/inflammation-related pathways dominated. Additionally, we identified protein signatures associated with the severity of α-synuclein pathology in PD and showed that these are highly enriched in an upregulation of mitochondrial processes, specifically related to oxidative phosphorylation and in particular respiratory complexes I and IV. We identify multiple novel signatures of protein expression, associated with PD, PSP, and MSA, as well as with the severity of α-synuclein pathology in the PD brain.

Serum proteomics reveal APOE-ε4-dependent and APOE-ε4-independent protein signatures in Alzheimer's disease.

A deeper understanding of the molecular processes underlying late-onset Alzheimer's disease (LOAD) could aid in biomarker and drug target discovery. Using high-throughput serum proteomics in the prospective population-based Age, Gene/Environment Susceptibility-Reykjavik Study (AGES) cohort of 5,127 older Icelandic adults (mean age, 76.6 ± 5.6 years), we identified 303 proteins associated with incident LOAD over a median follow-up of 12.8 years. Over 40% of these proteins were associated with LOAD independently of APOE-ε4 carrier status, were implicated in neuronal processes and overlapped with LOAD protein signatures in brain and cerebrospinal fluid. We identified 17 proteins whose associations with LOAD were strongly dependent on APOE-ε4 carrier status, with mostly consistent associations in cerebrospinal fluid. Remarkably, four of these proteins (TBCA, ARL2, S100A13 and IRF6) were downregulated by APOE-ε4 yet upregulated due to LOAD, a finding replicated in external cohorts and possibly reflecting a response to disease onset. These findings highlight dysregulated pathways at the preclinical stages of LOAD, including those both independent of and dependent on APOE-ε4 status.

Omics-derived biological modules reflect metabolic brain changes in Alzheimer's disease.

Brain glucose hypometabolism, indexed by the fluorodeoxyglucose positron emission tomography ([18F]FDG-PET) imaging, is a metabolic signature of Alzheimer's disease (AD). However, the underlying biological pathways involved in these metabolic changes remain elusive. Here, we integrated [18F]FDG-PET images with blood and hippocampal transcriptomic data from cognitively unimpaired (CU, n=445) and cognitively impaired (CI, n=749) individuals using modular dimension reduction techniques and voxel-wise linear regression analysis. Our results showed that multiple transcriptomic modules are associated with brain [18F]FDG-PET metabolism, with the top hits being a protein serine/threonine kinase activity gene cluster (peak-t(223)=4.86, P value<0.001) and zinc-finger-related regulatory units (peak-t(223)=3.90, P value<0.001). By integrating transcriptomics with PET imaging data, we identified that serine/threonine kinase activity-associated genes and zinc-finger-related regulatory units are highly associated with brain metabolic changes in AD. We conducted an integrated analysis of system-based transcriptomics and fluorodeoxyglucose positron emission tomography ([18F]FDG-PET) at the voxel level in Alzheimer's disease (AD). The biological process of serine/threonine kinase activity was the most associated with [18F]FDG-PET in the AD brain. Serine/threonine kinase activity alterations are associated with brain vulnerable regions in AD [18F]FDG-PET. Zinc-finger transcription factor targets were associated with AD brain [18F]FDG-PET metabolism.

Saliva, Plasma, and Multifluid Metabolomic Signatures of Periodontal Disease, Type 2 Diabetes Progression, and Markers of Glycemia and Dyslipidemia Among Puerto Rican Adults With Overweight and Obesity.

Evidence from cohort studies indicates a bidirectional relationship between periodontal disease and type 2 diabetes (T2D), but the underlying mechanisms remain unknown. In this study, we aimed to (1) identify saliva, plasma, and multifluid metabolomic signatures associated with periodontal disease and (2) determine if these signatures predict T2D progression and cardiometabolic biomarkers at year 3. We included participants from the SOALS (San Juan Overweight Adult Longitudinal Study) (n=911). Metabolites from saliva (k=635) and plasma (k=1051) were quantified using liquid chromatography-mass spectrometry. We applied elastic net regression with 10-fold cross-validation to identify baseline metabolomic signatures of periodontal disease. Multivariable Cox proportional hazards regression and linear regression were used to evaluate the association with T2D progression and biomarker concentrations. Metabolomic profilesincluded highly weighted metabolites related to lysine and pyrimidine metabolism. Periodontal disease or its 3 metabolomic signatures were not associated with T2D progression in 3 years. Prospectively, 1-SD increments in the multifluid and saliva metabolomic signatures were associated with higher low-density lipoprotein (multifluid: 12.9±5.70, P=0.02; saliva: 13.3±5.11, P=0.009). A 1-SD increment in the plasma metabolomic signature was also associated with Homeostatic Model Assessment for Insulin Resistance (2.67±1.14, P=0.02) and triglyceride (0.52±0.18, P=0.002). Although metabolomic signatures of periodontal disease could not predict T2D progression, they were associated with low-density lipoprotein, triglyceride, and Homeostatic Model Assessment for Insulin Resistance levels at year 3.

Network proteomics of the Lewy body dementia brain reveals presynaptic signatures distinct from Alzheimer’s disease

Lewy body dementia (LBD), a class of disorders comprising Parkinson’s disease dementia (PDD) and dementia with Lewy bodies (DLB), features substantial clinical and pathological overlap with Alzheimer’s disease (AD). The identification of biomarkers unique to LBD pathophysiology could meaningfully advance its diagnosis, monitoring, and treatment. Using quantitative mass spectrometry (MS), we measured over 9,000 proteins across 138 dorsolateral prefrontal cortex (DLPFC) tissues from a University of Pennsylvania autopsy collection comprising control, Parkinson’s disease (PD), PDD, and DLB diagnoses. We then analyzed co-expression network protein alterations in those with LBD, validated these disease signatures in two independent LBD datasets, and compared these findings to those observed in network analyses of AD cases. The LBD network revealed numerous groups or “modules” of co-expressed proteins significantly altered in PDD and DLB, representing synaptic, metabolic, and inflammatory pathophysiology. A comparison of validated LBD signatures to those of AD identified distinct differences between the two diseases. Notably, synuclein-associated presynaptic modules were elevated in LBD but decreased in AD relative to controls. We also found that glial-associated matrisome signatures consistently elevated in AD were more variably altered in LBD, ultimately stratifying those LBD cases with low versus high burdens of concurrent beta-amyloid deposition. In conclusion, unbiased network proteomic analysis revealed diverse pathophysiological changes in the LBD frontal cortex distinct from alterations in AD. These results highlight the LBD brain network proteome as a promising source of biomarkers that could enhance clinical recognition and management.

Resolution of tuberculosis blood RNA signatures fails to discriminate persistent sputum culture positivity after eight weeks of anti-tuberculous treatment.

Concerted efforts aim to reduce the burden of 6 months anti-tuberculous treatment for tuberculosis (TB). Treatment cessation at 8 weeks is effective for most but incurs increased risk of disease relapse. We tested the hypothesis that blood RNA signatures or C-reactive protein (CRP) measurements discriminate 8-week sputum culture status, as a prerequisite for a biomarker to stratify risk of relapse following treatment cessation at this time point. We identified blood RNA signatures of TB disease or cure by systematic review. We evaluated these signatures and CRP measurements in a pulmonary TB cohort, pre-treatment, at 2 and 8 weeks of treatment, and sustained cure after treatment completion. We tested biomarker discrimination of 8-week sputum culture status using area under the receiver operating characteristic curve (AUROC) analysis and, secondarily, assessed correlation of biomarker scores with time-to-culture positivity at 8 weeks of treatment. 12 blood RNA signatures were reproduced in the dataset from 44 individuals with sputum culture positive pulmonary TB. These normalised over time from TB treatment initiation. 11/44 cases with blood RNA, CRP and sputum culture results were sputum culture positive at 8 weeks of treatment. None of the contemporary blood RNA signatures discriminated sputum culture status at this time point or correlated with bacterial load. CRP achieved modest discrimination with AUROC of 0.69 (95% confidence interval 0.52-0.87). Selected TB blood RNA signatures and CRP do not provide biomarkers of microbiological clearance to support TB treatment cessation at 8 weeks. Resolution of blood transcriptional host-responses in sputum culture-positive individuals suggests Mycobacterium tuberculosis may colonise the respiratory tract without triggering a detectable immune response.

Artificial Intelligence and Disease Signature Pathways: Driving Innovation to Elucidate Underlying Pathogenic Mechanisms.

Artificial Intelligence and Disease Signature Pathways: Driving Innovation to Elucidate Underlying Pathogenic Mechanisms.

Aberrant DNA methylation associated with the development of metabolic dysfunction-associated fatty liver disease

The literature review deals with DNA methylation, a key epigenetic mechanism that controls the activity of gene transcription, plays a decisive role in the formation of genomic imprinting, gene silencing, X-chromosome inactivation, RNA splicing, DNA repair, cell differentiation and cell reprogramming, and also determines the occurrence and development of liver steatotic lesions and metabolic disorders. Methylation of DNA cytosine dinucleotide (CpG) can be represented in two types: de novo CpG methylation, which is carried out by 5mC DNA writers — DNA-(cytosine-5)-methyltransferase (DNMT) 3a and 3b, and suppor­ting DNA methylation, which is performed by DNMT1 during DNA replication. It has been found that the maintenance DNA methylation allows the preservation of the methylation pattern characteristic of progenitor cells in the cells of the new generation, and the DNA methylation of the gene body is associated with its increased expression. Active demethylation of 5mC is carried out by TET dioxygenases, including three enzymatic representatives: TET1, TET2 and TET3. It has been demonstrated that aberrant methylation of DNA nucleotides is directly related to the activity of lipid synthesis, the degree of oxidative stress, the development of liver steatosis, low-grade inflammation, insulin resistance, and the progression of liver fibrosis. The authors presented in detail the functions and features of DNA methyltransferases, erasers, and readers of 5mC sites; possible violations of the balance of activity of writers and erasers of 5mC DNA; DNA methylation landscape and patterns; clinical significance of DNA methylation signatures in metabolic dysfunction-associated fatty liver disease. Global hypomethylation of genome, at least 55 genes, is observed in patients with metabolic dysfunction-associated fatty liver disease. The authors emphasize that the use of DNA methylation signatures is a promising direction for early diagnosis and prognosis of the course of metabolic dysfunction-associated fatty liver disease, while the study of molecular components of DNA methylation mechanisms involved in the regulation of gene expression, the dependence of their activity on exposure to the exposome will allow to persona­lize and improve recommendations for lifestyle and diet modification in patients with metabolic dysfunction-associated fatty liver disease.

Molecular Signatures of Neurodegenerative Diseases Identified by Proteomic and Phosphoproteomic Analyses in Aging Mouse Brain

A central hallmark of neurodegenerative diseases is theirreversible accumulation of misfolded proteins in the brain by aberrant phosphorylation. Understanding the mechanisms underlying protein phosphorylation and its role in pathological protein aggregation within the context of aging is crucial for developing therapeutic strategies aimed at preventing or reversing such diseases. Here, we applied multi-protease digestion and quantitative mass spectrometry to compare and characterize dysregulated proteins and phosphosites in the mouse brain proteome using three different age groups: young-adult (3-4months), middle-age (10months), and old mice (19-21months). Proteins associated with senescence, neurodegeneration, inflammation, cell cycle regulation, the p53 hallmark pathway, and cytokine signaling showed significant age-dependent changes in abundances and level of phosphorylation. Several proteins implicated in Alzheimer's disease (AD) and Parkinson's disease (PD) including tau (Mapt), Nefh, and Dpysl2 (also known as Crmp2) were hyperphosphorylated in old mice brain suggesting their susceptibility to the diseases. Cdk5 and Gsk3b, which are known to phosphorylate Dpysl2 at multiple specific sites, had also increased phosphorylation levels in old mice suggesting a potential crosstalk between them to contribute to AD. Hapln2, which promotes α-synuclein aggregation in patients with PD, was one of the proteins with highest abundance in old mice. CD9, which regulates senescence through the PI3K-AKT-mTOR-p53 signaling was upregulated in old mice and its regulation was correlated with the activation of phosphorylated AKT1. Overall, the findings identify a significant association between aging and the dysregulation of proteins involved in various pathways linked to neurodegenerative diseases with potential therapeutic implications.