Prognostic genome and transcriptome signatures in colorectal cancers.

Colorectal cancer is caused by a sequence of somatic genomic alterations affecting driver genes in core cancer pathways1. Here, to understand the functional and prognostic impact of cancer-causing somatic mutations, we analysed the whole genomes and transcriptomes of 1,063 primary colorectal cancers in a population-based cohort with long-term follow-up. From the 96 mutated driver genes, 9 were not previously implicated in colorectal cancer and 24 had not been linked to any cancer. Two distinct patterns of pathway co-mutations were observed, timing analyses identified nine early and three late driver gene mutations, and several signatures of colorectal-cancer-specific mutational processes were identified. Mutations in WNT, EGFR and TGFß pathway genes, the mitochondrial CYB gene and 3 regulatory elements along with 21 copy-number variations and the COSMIC SBS44 signature correlated with survival. Gene expression classification yielded five prognostic subtypes with distinct molecular features, in part explained by underlying genomic alterations. Microsatellite-instable tumours divided into two classes with different levels of hypoxia and infiltration of immune and stromal cells. To our knowledge, this study constitutes the largest integrated genome and transcriptome analysis of colorectal cancer, and interlinks mutations, gene expression and patient outcomes. The identification of prognostic mutations and expression subtypes can guide future efforts to individualize colorectal cancer therapy.

Global compositional and functional states of the human gut microbiome in health and disease.

The human gut microbiota is of increasing interest, with metagenomics a key tool for analyzing bacterial diversity and functionality in health and disease. Despite increasing efforts to expand microbial gene catalogs and an increasing number of metagenome-assembled genomes, there have been few pan-metagenomic association studies and in-depth functional analyses across different geographies and diseases. Here, we explored 6014 human gut metagenome samples across 19 countries and 23 diseases by performing compositional, functional cluster, and integrative analyses. Using interpreted machine learning classification models and statistical methods, we identified Fusobacterium nucleatum and Anaerostipes hadrus with the highest frequencies, enriched and depleted, respectively, across different disease cohorts. Distinct functional distributions were observed in the gut microbiomes of both westernized and nonwesternized populations. These compositional and functional analyses are presented in the open-access Human Gut Microbiome Atlas, allowing for the exploration of the richness, disease, and regional signatures of the gut microbiota across different cohorts.

Spatiotemporal dissection of the cell cycle with single-cell proteogenomics.

The cell cycle, over which cells grow and divide, is a fundamental process of life. Its dysregulation has devastating consequences, including cancer1-3. The cell cycle is driven by precise regulation of proteins in time and space, which creates variability between individual proliferating cells. To our knowledge, no systematic investigations of such cell-to-cell proteomic variability exist. Here we present a comprehensive, spatiotemporal map of human proteomic heterogeneity by integrating proteomics at subcellular resolution with single-cell transcriptomics and precise temporal measurements of individual cells in the cell cycle. We show that around one-fifth of the human proteome displays cell-to-cell variability, identify hundreds of proteins with previously unknown associations with mitosis and the cell cycle, and provide evidence that several of these proteins have oncogenic functions. Our results show that cell cycle progression explains less than half of all cell-to-cell variability, and that most cycling proteins are regulated post-translationally, rather than by transcriptomic cycling. These proteins are disproportionately phosphorylated by kinases that regulate cell fate, whereas non-cycling proteins that vary between cells are more likely to be modified by kinases that regulate metabolism. This spatially resolved proteomic map of the cell cycle is integrated into the Human Protein Atlas and will serve as a resource for accelerating molecular studies of the human cell cycle and cell proliferation.

The neuropeptide landscape of human prefrontal cortex.

Human prefrontal cortex (hPFC) is a complex brain region involved in cognitive and emotional processes and several psychiatric disorders. Here, we present an overview of the distribution of the peptidergic systems in 17 subregions of hPFC and three reference cortices obtained by microdissection and based on RNA sequencing and RNAscope methods integrated with published single-cell transcriptomics data. We detected expression of 60 neuropeptides and 60 neuropeptide receptors in at least one of the hPFC subregions. The results reveal that the peptidergic landscape in PFC consists of closely located and functionally different subregions with unique peptide/transmitter-related profiles. Neuropeptide-rich PFC subregions were identified, encompassing regions from anterior cingulate cortex/orbitofrontal gyrus. Furthermore, marked differences in gene expression exist between different PFC regions (>5-fold; cocaine and amphetamine-regulated transcript peptide) as well as between PFC regions and reference regions, for example, for somatostatin and several receptors. We suggest that the present approach allows definition of, still hypothetical, microcircuits exemplified by glutamatergic neurons expressing a peptide cotransmitter either as an agonist (hypocretin/orexin) or antagonist (galanin). Specific neuropeptide receptors have been identified as possible targets for neuronal afferents and, interestingly, peripheral blood-borne peptide hormones (leptin, adiponectin, gastric inhibitory peptide, glucagon-like peptides, and peptide YY). Together with other recent publications, our results support the view that neuropeptide systems may play an important role in hPFC and underpin the concept that neuropeptide signaling helps stabilize circuit connectivity and fine-tune/modulate PFC functions executed during health and disease.

Characterization of reduced astrocyte creatine kinase levels in Alzheimer's disease.

The creatine-phosphocreatine cycle serves as a crucial temporary energy buffering system in the brain, regulated by brain creatine kinase (CKB), in maintaining Adenosine triphosphate (ATP) levels. Alzheimer's disease (AD) has been linked to increased CKB oxidation and loss of its regulatory function, although specific pathological processes and affected cell types remain unclear. In our study, cerebral cortex samples from individuals with AD, dementia with Lewy bodies (DLB), and age-matched controls were analyzed using antibody-based methods to quantify CKB levels and assess alterations associated with disease processes. Two independently validated antibodies exclusively labeled astrocytes in the human cerebral cortex. Combining immunofluorescence (IF) and mass spectrometry (MS), we explored CKB availability in AD and DLB cases. IF and Western blot analysis demonstrated a loss of CKB immunoreactivity correlated with increased plaque load, severity of tau pathology, and Lewy body pathology. However, transcriptomics data and targeted MS demonstrated unaltered total CKB levels, suggesting posttranslational modifications (PTMs) affecting antibody binding. This aligns with altered efficiency at proteolytic cleavage sites indicated in the targeted MS experiment. These findings highlight that the proper function of astrocytes, understudied in the brain compared with neurons, is highly affected by PTMs. Reduction in ATP levels within astrocytes can disrupt ATP-dependent processes, such as the glutamate-glutamine cycle. As CKB and the creatine-phosphocreatine cycle are important in securing constant ATP availability, PTMs in CKB, and astrocyte dysfunction may disturb homeostasis, driving excitotoxicity in the AD brain. CKB and its activity could be promising biomarkers for monitoring early-stage energy deficits in AD.

Oral-gut microbiome interactions in advanced cirrhosis: characterisation of pathogenic enterotypes and salivatypes, virulence factors and antimicrobial.

BACKGROUND & AIMS: Cirrhosis complications are often triggered by bacterial infections with multidrug-resistant organisms. Alterations in the gut and oral microbiome in decompensated cirrhosis (DC) influence clinical outcomes. We interrogated: (i) gut and oral microbiome community structures, (ii) virulence factors (VFs) and antimicrobial resistance genes (ARGs) and (iii) oral-gut microbial overlap in patients with differing cirrhosis severity. METHODS: 15 healthy controls (HC), 26 stable cirrhosis (SC), 46 DC, 14 acute-on-chronic liver failure (ACLF) and 14 with severe infection without cirrhosis (NLS) participated. Metagenomic sequencing was undertaken on paired saliva (S) and faecal (F) samples. 'Salivatypes' and 'enterotypes' based on genera clustering were assessed against cirrhosis severity and clinical parameters. VFs and ARGs were evaluated in oral and gut niches, and distinct resistotypes identified. RESULTS: Salivatypes and enterotypes revealed a greater proportion of pathobionts with concomitant reduction in autochthonous genera with increasing cirrhosis severity and hyperammonaemia. Increasing overlap between oral and gut microbiome communities was observed in DC and ACLF vs SC and HCs, independent of antimicrobial, beta-blocker and acid suppressant therapies. Two distinct gut microbiome clusters [ENT2/ENT3] harboured genes encoding for the phosphoenolpyruvate:sugar phosphotransferase system (PTS) system and other VFs in DC and ACLF. Substantial ARGs (oral: 1,218 and gut: 672) were detected [575 common to both sites]. The cirrhosis resistome was distinct, with three oral and four gut resistotypes identified, respectively. DISCUSSION: The degree of oral-gut microbial community overlap, frequency of VFs and ARGs all increment significantly with cirrhosis severity, with progressive dominance of pathobionts and loss of commensals. Despite similar antimicrobial exposure, patients with DC and ACLF have reduced microbial richness compared to NLS, supporting the additive pathobiological effect of cirrhosis. IMPACT AND IMPLICATIONS: This research underscores the crucial role of microbiome alterations in the progression of cirrhosis in an era of escalating multidrug resistant infections, highlighting the association and potential impact of increased oral-gut microbial overlap, virulence factors, and antimicrobial resistance genes on clinical outcomes. These findings are particularly significant for patients with decompensated cirrhosis and acute-on-chronic liver failure, as they reveal the intricate relationship between microbiome alterations and cirrhosis complications. This is relevant in the context of multidrug-resistant organisms and reduced oral-gut microbial diversity that exacerbate cirrhosis severity, drive hepatic decompensation and complicate treatment. For practical applications, these insights could guide for cirrhosis patients the development of targeted microbiome-based therapeutics and personalised antimicrobial regimens to mitigate infectious complications to improve their clinical outcomes.

Plasma proteomics for prediction of subclinical coronary artery calcifications in primary prevention.

BACKGROUND AND AIMS: Recent developments in high-throughput proteomic technologies enable the discovery of novel biomarkers of coronary atherosclerosis. The aims of this study were to test if plasma protein subsets could detect coronary artery calcifications (CAC) in asymptomatic individuals and if they add predictive value beyond traditional risk factors. METHODS: Using proximity extension assays, 1,342 plasma proteins were measured in 1,827 individuals from the Impaired Glucose Tolerance and Microbiota (IGTM) study and 883 individuals from the Swedish Cardiopulmonary BioImage Study (SCAPIS) aged 50-64 years without history of ischaemic heart disease and with CAC assessed by computed tomography. After data-driven feature selection, extreme gradient boosting machine learning models were trained on the IGTM cohort to predict the presence of CAC using combinations of proteins and traditional risk factors. The trained models were validated in SCAPIS. RESULTS: The best plasma protein subset (44 proteins) predicted CAC with an area under the curve (AUC) of 0.691 in the validation cohort. However, this was not better than prediction by traditional risk factors alone (AUC = 0.710, P = .17). Adding proteins to traditional risk factors did not improve the predictions (AUC = 0.705, P = .6). Most of these 44 proteins were highly correlated with traditional risk factors. CONCLUSIONS: A plasma protein subset that could predict the presence of subclinical CAC was identified but it did not outperform nor improve a model based on traditional risk factors. Thus, support for this targeted proteomics platform to predict subclinical CAC beyond traditional risk factors was not found.

Open MoA: revealing the mechanism of action (MoA) based on network topology and hierarchy.

MOTIVATION: Many approaches in systems biology have been applied in drug repositioning due to the increased availability of the omics data and computational biology tools. Using a multi-omics integrated network, which contains information of various biological interactions, could offer a more comprehensive inspective and interpretation for the drug mechanism of action (MoA). RESULTS: We developed a computational pipeline for dissecting the hidden MoAs of drugs (Open MoA). Our pipeline computes confidence scores to edges that represent connections between genes/proteins in the integrated network. The interactions showing the highest confidence score could indicate potential drug targets and infer the underlying molecular MoAs. Open MoA was also validated by testing some well-established targets. Additionally, we applied Open MoA to reveal the MoA of a repositioned drug (JNK-IN-5A) that modulates the PKLR expression in HepG2 cells and found STAT1 is the key transcription factor. Overall, Open MoA represents a first-generation tool that could be utilized for predicting the potential MoA of repurposed drugs and dissecting de novo targets for developing effective treatments. AVAILABILITY AND IMPLEMENTATION: Source code is available at https://github.com/XinmengLiao/Open_MoA.

Fast and robust recombinant protein production utilizing episomal stable pools in WAVE bioreactors.

Protein reagents are essential resources for several stages of drug discovery projects from structural biology and assay development through lead optimization. Depending on the aim of the project different amounts of pure protein are required. Small-scale expressions are initially used to determine the reachable levels of production and quality before scaling up protein reagent supply. Commonly, amounts of several hundreds of milligrams to grams are needed for different experiments, including structural investigations and activity evaluations, which require rather large cultivation volumes. This implies that cultivation of large volumes of either transiently transfected cells or stable pools/stable cell lines is needed. Hence, a production process that is scalable, speeds up the development projects, and increases the robustness of protein reagent quality throughout scales. Here we present a protein production pipeline with high scalability. We show that our protocols for protein production in Chinese hamster ovary cells allow for a seamless and efficient scale-up with robust product quality and high performance. The flexible scale of the production process, as shown here, allows for shorter lead times in drug discovery projects where there is a reagent demand for a specific protein or a set of target proteins.

Longitudinal Exposomics in a Multiomic Wellness Cohort Reveals Distinctive and Dynamic Environmental Chemical Mixtures in Blood.

Chemical exposomes can now be comprehensively measured in human blood, but knowledge of their variability and longitudinal stability is required for robust application in cohort studies. Here, we applied high-resolution chemical exposomics to plasma of 46 adults, each sampled 6 times over 2 years in a multiomic cohort, resulting in 276 individual exposomes. In addition to quantitative analysis of 83 priority target analytes, we discovered and semiquantified substances that have rarely or never been reported in humans, including personal care products, pesticide transformation products, and polymer additives. Hierarchical cluster analysis for 519 confidently annotated substances revealed unique and distinctive coexposures, including clustered pesticides, poly(ethylene glycols), chlorinated phenols, or natural substances from tea and coffee; interactive heatmaps were publicly deposited to support open exploration of the complex (meta)data. Intraclass correlation coefficients (ICC) for all annotated substances demonstrated the relatively low stability of the exposome compared to that of proteome, microbiome, and endogenous small molecules. Implications are that the chemical exposome must be measured more frequently than other omics in longitudinal studies and four longitudinal exposure types are defined that can be considered in study design. In this small cohort, mixed-effect models nevertheless revealed significant associations between testosterone and perfluoroalkyl substances, demonstrating great potential for longitudinal exposomics in precision health research.

Design and synthesis of novel JNK inhibitors targeting liver pyruvate kinase for the treatment of non-alcoholic fatty liver disease and hepatocellular carcinoma.

Non-alcoholic fatty liver disease (NAFLD) comprises a broad range of liver disease including hepatocellular carcinoma (HCC) with is no FDA-approved drug. Liver pyruvate kinase (PKL) is a major regulator of metabolic flux and ATP generation in liver presenting a potential target for the treatment of NAFLD. Based on our recent finding of JNK-5A's effectiveness in inhibiting PKLR expression through a drug repositioning pipeline, this study aims to improve its efficacy further. We synthesized a series of JNK-5A analogues with targeted modifications, guided by molecular docking studies. These compounds were evaluated for their activities on PKL expression, cell viability, triacylglyceride (TAG) levels, and the expressions of steatosis-related proteins in the human HepG2 cell line. Subsequently, the efficacy of these compounds was assessed in reducing TAG level and toxicity. Compounds 40 (SET-151) and 41 (SET-152) proved to be the most efficient in reducing TAG levels (11.51 ± 0.90 % and 10.77 ± 0.67 %) and demonstrated lower toxicity (61.60 ± 5.00 % and 43.87 ± 1.42 %) in HepG2 cells. Additionally, all synthesized compounds were evaluated for their anti-cancer properties revealing that compound 74 (SET-171) exhibited the highest toxicity in cell viability with IC50 values of 8.82 µM and 2.97 µM in HepG2 and Huh7 cell lines, respectively. To summarize, compounds 40 (SET-151) and 41 (SET-152) show potential for treating NAFLD, while compound 74 (SET-171) holds potential for HCC therapy.

Discovery of a Therapeutic Agent for Glioblastoma Using a Systems Biology-Based Drug Repositioning Approach.

Glioblastoma (GBM), a highly malignant tumour of the central nervous system, presents with a dire prognosis and low survival rates. The heterogeneous and recurrent nature of GBM renders current treatments relatively ineffective. In our study, we utilized an integrative systems biology approach to uncover the molecular mechanisms driving GBM progression and identify viable therapeutic drug targets for developing more effective GBM treatment strategies. Our integrative analysis revealed an elevated expression of CHST2 in GBM tumours, designating it as an unfavourable prognostic gene in GBM, as supported by data from two independent GBM cohorts. Further, we pinpointed WZ-4002 as a potential drug candidate to modulate CHST2 through computational drug repositioning. WZ-4002 directly targeted EGFR (ERBB1) and ERBB2, affecting their dimerization and influencing the activity of adjacent genes, including CHST2. We validated our findings by treating U-138 MG cells with WZ-4002, observing a decrease in CHST2 protein levels and a reduction in cell viability. In summary, our research suggests that the WZ-4002 drug candidate may effectively modulate CHST2 and adjacent genes, offering a promising avenue for developing efficient treatment strategies for GBM patients.

Discovery of Cell-Permeable Allosteric Inhibitors of Liver Pyruvate Kinase: Design and Synthesis of Sulfone-Based Urolithins.

Metabolic dysfunction-associated fatty liver disease (MAFLD) presents a significant global health challenge, characterized by the accumulation of liver fat and impacting a considerable portion of the worldwide population. Despite its widespread occurrence, effective treatments for MAFLD are limited. The liver-specific isoform of pyruvate kinase (PKL) has been identified as a promising target for developing MAFLD therapies. Urolithin C, an allosteric inhibitor of PKL, has shown potential in preliminary studies. Expanding upon this groundwork, our study delved into delineating the structure-activity relationship of urolithin C via the synthesis of sulfone-based urolithin analogs. Our results highlight that incorporating a sulfone moiety leads to substantial PKL inhibition, with additional catechol moieties further enhancing this effect. Despite modest improvements in liver cell lines, there was a significant increase in inhibition observed in HepG2 cell lysates. Specifically, compounds 15d, 9d, 15e, 18a, 12d, and 15a displayed promising IC50 values ranging from 4.3 µM to 18.7 µM. Notably, compound 15e not only demonstrated a decrease in PKL activity and triacylglycerol (TAG) content but also showed efficient cellular uptake. These findings position compound 15e as a promising candidate for pharmacological MAFLD treatment, warranting further research and studies.

Hepatic Innervations and Nonalcoholic Fatty Liver Disease.

Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disorder. Increased sympathetic (noradrenergic) nerve tone has a complex role in the etiopathomechanism of NAFLD, affecting the development/progression of steatosis, inflammation, fibrosis, and liver hemodynamical alterations. Also, lipid sensing by vagal afferent fibers is an important player in the development of hepatic steatosis. Moreover, disorganization and progressive degeneration of liver sympathetic nerves were recently described in human and experimental NAFLD. These structural alterations likely come along with impaired liver sympathetic nerve functionality and lack of adequate hepatic noradrenergic signaling. Here, we first overview the anatomy and physiology of liver nerves. Then, we discuss the nerve impairments in NAFLD and their pathophysiological consequences in hepatic metabolism, inflammation, fibrosis, and hemodynamics. We conclude that further studies considering the spatial-temporal dynamics of structural and functional changes in the hepatic nervous system may lead to more targeted pharmacotherapeutic advances in NAFLD.

Display of a naïve affibody library on staphylococci for selection of binders by means of flow cytometry sorting.

Within the field of combinatorial protein engineering there is a great demand for robust high-throughput selection platforms that allow for unbiased protein library display, affinity-based screening, and amplification of selected clones. We have previously described the development of a staphylococcal display system used for displaying both alternative-scaffolds and antibody-derived proteins. In this study, the objective was to generate an improved expression vector for displaying and screening a high-complexity naïve affibody library, and to facilitate downstream validation of isolated clones. A high-affinity normalization tag, consisting of two ABD-moieties, was introduced to simplify off-rate screening procedures. In addition, the vector was furnished with a TEV protease substrate recognition sequence upstream of the protein library which enables proteolytic processing of the displayed construct for improved binding signal. In the library design, 13 of the 58 surface-exposed amino acid positions were selected for full randomization (except proline and cysteine) using trinucleotide technology. The genetic library was successfully transformed to Staphylococcus carnosus cells, generating a protein library exceeding 109 members. De novo selections against three target proteins (CD14, MAPK9 and the affibody ZEGFR:2377) were successfully performed using magnetic bead-based capture followed by flow-cytometric sorting, yielding affibody molecules binding their respective target with nanomolar affinity. Taken together, the results demonstrate the feasibility of the staphylococcal display system and the proposed selection procedure to generate new affibody molecules with high affinity.

Serendipitous Identification of a Covalent Activator of Liver Pyruvate Kinase.

Enzymes are effective biological catalysts that accelerate almost all metabolic reactions in living organisms. Synthetic modulators of enzymes are useful tools for the study of enzymatic reactions and can provide starting points for the design of new drugs. Here, we report on the discovery of a class of biologically active compounds that covalently modifies lysine residues in human liver pyruvate kinase (PKL), leading to allosteric activation of the enzyme (EC50 =0.29 µM). Surprisingly, the allosteric activation control point resides on the lysine residue K282 present in the catalytic site of PKL. These findings were confirmed by structural data, MS/MS experiments, and molecular modelling studies. Altogether, our study provides a molecular basis for the activation mechanism and establishes a framework for further development of human liver pyruvate kinase covalent activators.

Multi-omics approaches for revealing the complexity of cardiovascular disease.

The development and progression of cardiovascular disease (CVD) can mainly be attributed to the narrowing of blood vessels caused by atherosclerosis and thrombosis, which induces organ damage that will result in end-organ dysfunction characterized by events such as myocardial infarction or stroke. It is also essential to consider other contributory factors to CVD, including cardiac remodelling caused by cardiomyopathies and co-morbidities with other diseases such as chronic kidney disease. Besides, there is a growing amount of evidence linking the gut microbiota to CVD through several metabolic pathways. Hence, it is of utmost importance to decipher the underlying molecular mechanisms associated with these disease states to elucidate the development and progression of CVD. A wide array of systems biology approaches incorporating multi-omics data have emerged as an invaluable tool in establishing alterations in specific cell types and identifying modifications in signalling events that promote disease development. Here, we review recent studies that apply multi-omics approaches to further understand the underlying causes of CVD and provide possible treatment strategies by identifying novel drug targets and biomarkers. We also discuss very recent advances in gut microbiota research with an emphasis on how diet and microbial composition can impact the development of CVD. Finally, we present various biological network analyses and other independent studies that have been employed for providing mechanistic explanation and developing treatment strategies for end-stage CVD, namely myocardial infarction and stroke.

Addressing the heterogeneity in liver diseases using biological networks.

The abnormalities in human metabolism have been implicated in the progression of several complex human diseases, including certain cancers. Hence, deciphering the underlying molecular mechanisms associated with metabolic reprogramming in a disease state can greatly assist in elucidating the disease aetiology. An invaluable tool for establishing connections between global metabolic reprogramming and disease development is the genome-scale metabolic model (GEM). Here, we review recent work on the reconstruction of cell/tissue-type and cancer-specific GEMs and their use in identifying metabolic changes occurring in response to liver disease development, stratification of the heterogeneous disease population and discovery of novel drug targets and biomarkers. We also discuss how GEMs can be integrated with other biological networks for generating more comprehensive cell/tissue models. In addition, we review the various biological network analyses that have been employed for the development of efficient treatment strategies. Finally, we present three case studies in which independent studies converged on conclusions underlying liver disease.

Orthogonal proteomics methods warrant the development of Duchenne muscular dystrophy biomarkers.

BACKGROUND: Molecular components in blood, such as proteins, are used as biomarkers to detect or predict disease states, guide clinical interventions and aid in the development of therapies. While multiplexing proteomics methods promote discovery of such biomarkers, their translation to clinical use is difficult due to the lack of substantial evidence regarding their reliability as quantifiable indicators of disease state or outcome. To overcome this challenge, a novel orthogonal strategy was developed and used to assess the reliability of biomarkers and analytically corroborate already identified serum biomarkers for Duchenne muscular dystrophy (DMD). DMD is a monogenic incurable disease characterized by progressive muscle damage that currently lacks reliable and specific disease monitoring tools. METHODS: Two technological platforms are used to detect and quantify the biomarkers in 72 longitudinally collected serum samples from DMD patients at 3 to 5 timepoints. Quantification of the biomarkers is achieved by detection of the same biomarker fragment either through interaction with validated antibodies in immuno-assays or through quantification of peptides by Parallel Reaction Monitoring Mass Spectrometry assay (PRM-MS). RESULTS: Five, out of ten biomarkers previously identified by affinity-based proteomics methods, were confirmed to be associated with DMD using the mass spectrometry-based method. Two biomarkers, carbonic anhydrase III and lactate dehydrogenase B, were quantified with two independent methods, sandwich immunoassays and PRM-MS, with Pearson correlations of 0.92 and 0.946 respectively. The median concentrations of CA3 and LDHB in DMD patients was elevated in comparison to those in healthy individuals by 35- and 3-fold, respectively. Levels of CA3 vary between 10.26 and 0.36 ng/ml in DMD patients whereas those of LDHB vary between 15.1 and 0.8 ng/ml. CONCLUSIONS: These results demonstrate that orthogonal assays can be used to assess the analytical reliability of biomarker quantification assays, providing a means to facilitate the translation of biomarkers to clinical practice. This strategy also warrants the development of the most relevant biomarkers, markers that can be reliably quantified with different proteomics methods.

Polymorphisms in alpha 7 nicotinic acetylcholine receptor gene, CHRNA7, and its partially duplicated gene, CHRFAM7A, associate with increased inflammatory response in human peripheral mononuclear cells.

The vagus nerve can, via the alpha 7 nicotinic acetylcholine receptor (α7nAChR), regulate inflammation. The gene coding for the α7nAChR, CHRNA7, can be partially duplicated, that is, CHRFAM7A, which is reported to impair the anti-inflammatory effect mediated via the α7nAChR. Several single nucleotide polymorphisms (SNPs) have been described in both CHRNA7 and CHRFAM7A, however, the functional role of these SNPs for immune responses remains to be investigated. In the current study, we set out to investigate whether genetic variants of CHRNA7 and CHRFAM7A can influence immune responses. By investigating data available from the Swedish SciLifeLab SCAPIS Wellness Profiling (S3WP) study, in combination with droplet digital PCR and freshly isolated PBMCs from the S3WP participants, challenged with lipopolysaccharide (LPS), we show that CHRNA7 and CHRFAM7A are expressed in human PBMCs, with approximately four times higher expression of CHRFAM7A compared with CHRNA7. One SNP in CHRFAM7A, rs34007223, is positively associated with hsCRP in healthy individuals. Furthermore, gene ontology (GO)-terms analysis of plasma proteins associated with gene expression of CHRNA7 and CHRFAM7A demonstrated an involvement for these genes in immune responses. This was further supported by in vitro data showing that several SNPs in both CHRNA7 and CHRFAM7A are significantly associated with cytokine response. In conclusion, genetic variants of CHRNA7 and CHRFAM7A alters cytokine responses. Furthermore, given that CHRFAM7A SNP rs34007223 is associated with inflammatory marker hsCRP in healthy individuals suggests that CHRFAM7A may have a more pronounced role in regulating inflammatory processes in humans than previously been recognized.