Arachidonic acid and docosahexaenoic acid levels correlate with the inflammation proteome in extremely preterm infants.

BACKGROUND & AIM: Clinical trials supplementing the long-chain polyunsaturated fatty acids (LCPUFAs) docosahexaenoic acid (DHA) and arachidonic acid (AA) to preterm infants have shown positive effects on inflammation-related morbidities, but the molecular mechanisms underlying these effects are not fully elucidated. This study aimed to determine associations between DHA, AA, and inflammation-related proteins during the neonatal period in extremely preterm infants. METHODS: A retrospective exploratory study of infants (n = 183) born below 28 weeks gestation from the Mega Donna Mega trial, a randomized multicenter trial designed to study the effect of DHA and AA on retinopathy of prematurity. Serial serum samples were collected after birth until postnatal day 100 (median 7 samples per infant) and analyzed for phospholipid fatty acids and proteins using targeted proteomics covering 538 proteins. Associations over time between LCPUFAs and proteins were explored using mixed effect modeling with splines, including an interaction term for time, and adjusted for gestational age, sex, and center. RESULTS: On postnatal day one, 55 proteins correlated with DHA levels and 10 proteins with AA levels. Five proteins were related to both fatty acids, all with a positive correlation. Over the first 100 days after birth, we identified 57 proteins to be associated with DHA and/or AA. Of these proteins, 41 (72%) related to inflammation. Thirty-eight proteins were associated with both fatty acids and the overall direction of association did not differ between DHA and AA, indicating that both LCPUFAs similarly contribute to up- and down-regulation of the preterm neonate inflammatory proteome. Primary examples of this were the inflammation-modulating cytokines IL-6 and CCL7, both being negatively related to levels of DHA and AA in the postnatal period. CONCLUSIONS: This study supports postnatal non-antagonistic and potentially synergistic effects of DHA and AA on the inflammation proteome in preterm infants, indicating that supplementation with both fatty acids may contribute to limiting the disease burden in this vulnerable population. CLINICAL REGISTRATION NUMBER: ClinicalTrials.gov (NCT03201588).

Transcriptome Analysis Reveals Distinct Patterns Between the Invasive and Noninvasive Pituitary Neuroendocrine Tumors.

Although most pituitary neuroendocrine tumors (PitNETs)/pituitary adenomas remain intrasellar, a significant proportion of tumors show parasellar invasive growth and 6% to 8% infiltrate the bone structures, thus affecting the prognosis. There is an unmet need to identify novel markers that can predict the parasellar growth of PitNETs. Furthermore, mechanisms that regulate bone invasiveness of PitNETs and factors related to tumor vascularization are largely unknown. We used genome-wide mRNA analysis in a cohort of 77 patients with PitNETs of different types to explore the differences in gene expression patterns between invasive and noninvasive tumors with respect to the parasellar growth and regarding the rare phenomenon of bone invasiveness. Additionally, we studied the genes correlated to the contrast enhancement quotient, a novel radiological parameter of tumor vascularization. Most of the genes differentially expressed related to the parasellar growth were genes involved in tumor invasiveness. Differentially expressed genes associated with bone invasiveness are involved in NF-κB pathway and antitumoral immune response. Lack of clear clustering regarding the parasellar and bone invasiveness may be explained by the influence of the cell lineage-related genes in this heterogeneous cohort of PitNETs. Our transcriptomics analysis revealed differences in the molecular fingerprints between invasive, including bone invasive, and noninvasive PitNETs, although without clear clustering. The contrast enhancement quotient emerged as a radiological parameter of tumor vascularization, correlating with several angiogenesis-related genes. Several of the top genes related to the PitNET invasiveness and vascularization have potential prognostic and therapeutic application requiring further research.

Sulfone-based human liver pyruvate kinase inhibitors - Design, synthesis and in vitro bioactivity.

Non-alcoholic fatty liver disease (NAFLD) is a prevalent pathological condition characterised by the accumulation of fat in the liver. Almost one-third of the global population is affected by NAFLD, making it a significant health concern. However, despite its prevalence, there is currently no approved drug specifically designed for the treatment of NAFLD. To address this critical gap, researchers have been investigating potential targets for NAFLD drug development. One promising candidate is the liver isoform of pyruvate kinase (PKL). In recent studies, Urolithin C, an allosteric inhibitor of PKL, has emerged as a potential lead compound for therapeutic intervention. Building upon this knowledge, our team has conducted a comprehensive structure-activity relationship of Urolithin C. In this work, we have employed a scaffold-hopping approach, modifying the urolithin structure by replacing the urolithin carbonyl with a sulfone moiety. Our structure-activity relationship analysis has identified the sulfone group as particularly favourable for potent PKL inhibition. Additionally, we have found that the presence of catechol moieties on the two aromatic rings further improves the inhibitory activity. The most promising inhibitor from this new series displayed nanomolar inhibition, boasting an IC50 value of 0.07 µM. This level of potency rivals that of urolithin D and significantly surpasses the effectiveness of urolithin C by an order of magnitude. To better understand the molecular interactions underlying this inhibition, we obtained the crystal structure of one of the inhibitors complexed with PKL. This structural insight served as a valuable reference point, aiding us in the design of inhibitors.

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.

Substance P, NPY, CCK and their receptors in five brain regions in major depressive disorder with transcriptomic analysis of locus coeruleus neurons.

Major depressive disorder (MDD) is a serious disease and a burden to patients, families and society. Rodent experiments and human studies suggest that several neuropeptide systems are involved in mood regulation. The aim of this study is two-fold: (i) to monitor, with qPCR, transcript levels of the substance P/tachykinin (TAC), NPY and CCK systems in bulk samples from control and suicide subjects, targeting five postmortem brain regions including locus coeruleus (LC); and (ii) to analyse expression of neuropeptide family transcripts in LC neurons of 'normal' postmortem brains by using laser capture microdissection with Smart-Seq2 RNA sequencing. qPCR revealed distinct regional expression patterns in male and female controls with higher levels for the TAC system in the dorsal raphe nucleus and LC, versus higher transcripts levels of the NPY and CCK systems in prefrontal cortex. In suicide patients, TAC, TAC receptors and a few NPY family transcript levels were increased mainly in prefrontal cortex and LC. The second study on 'normal' noradrenergic LC neurons revealed expression of transcripts for GAL, NPY, TAC1, CCK, and TACR1 and many other peptides (e.g. Cerebellin4 and CARTPT) and receptors (e.g. Adcyap1R1 and GPR173). These data and our previous results on suicide brains indicates that the tachykinin and galanin systems may be valid targets for developing antidepressant medicines. Moreover, the perturbation of neuropeptide systems in MDD patients, and the detection of further neuropeptide and receptor transcripts in LC, shed new light on signalling in noradrenergic LC neurons and on mechanisms possibly associated with mood disorders.

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.

Absolute Quantification of Pan-Cancer Plasma Proteomes Reveals Unique Signature in Multiple Myeloma.

Mass spectrometry based on data-independent acquisition (DIA) has developed into a powerful quantitative tool with a variety of implications, including precision medicine. Combined with stable isotope recombinant protein standards, this strategy provides confident protein identification and precise quantification on an absolute scale. Here, we describe a comprehensive targeted proteomics approach to profile a pan-cancer cohort consisting of 1800 blood plasma samples representing 15 different cancer types. We successfully performed an absolute quantification of 253 proteins in multiplex. The assay had low intra-assay variability with a coefficient of variation below 20% (CV = 17.2%) for a total of 1013 peptides quantified across almost two thousand injections. This study identified a potential biomarker panel of seven protein targets for the diagnosis of multiple myeloma patients using differential expression analysis and machine learning. The combination of markers, including the complement C1 complex, JCHAIN, and CD5L, resulted in a prediction model with an AUC of 0.96 for the identification of multiple myeloma patients across various cancer patients. All these proteins are known to interact with immunoglobulins.

Systematic transcriptional analysis of human cell lines for gene expression landscape and tumor representation.

Cell lines are valuable resources as model for human biology and translational medicine. It is thus important to explore the concordance between the expression in various cell lines vis-à-vis human native and disease tissues. In this study, we investigate the expression of all human protein-coding genes in more than 1,000 human cell lines representing 27 cancer types by a genome-wide transcriptomics analysis. The cell line gene expression is compared with the corresponding profiles in various tissues, organs, single-cell types and cancers. Here, we present the expression for each cell line and give guidance for the most appropriate cell line for a given experimental study. In addition, we explore the cancer-related pathway and cytokine activity of the cell lines to aid human biology studies and drug development projects. All data are presented in an open access cell line section of the Human Protein Atlas to facilitate the exploration of all human protein-coding genes across these cell lines.

Characterization of an in vitro steatosis model simulating activated de novo lipogenesis in MAFLD patients.

Activated de novo lipogenesis (DNL) is the critical pathway involved in the progression of metabolic-associated fatty liver disease (MAFLD). We present an in vitro steatosis model for MAFLD that induces steatosis through activated DNL. This model utilizes insulin and LXR receptor ligand T0901317, eliminating the need for fatty acid treatment. Significant increases in triglycerides (TAGs) and expression of DNL-related transcription factors were observed. Transcriptomic analysis revealed distinct gene expression profiles between the DNL and conventional oleic acid (OA)-induced steatosis model. DNL steatosis model exhibited elevated pathways related to glycolysis, cholesterol homeostasis, and bile acid metabolism, reflecting its clinical relevance to MAFLD. Moreover, C75 and JNK-IN-5A compounds effectively reduced TAG accumulation and steatosis-related protein expression in the DNL model, whereas they had no significant impact on TAG accumulation in the OA model. In conclusion, we introduce an ideal model for steatosis study, which could help in understanding the MAFLD mechanisms.

An easy-to-use high-throughput selection system for the discovery of recombinant protein binders from alternative scaffold libraries.

Selection by phage display is a popular and widely used technique for the discovery of recombinant protein binders from large protein libraries for therapeutic use. The protein library is displayed on the surface of bacteriophages which are amplified using bacteria, preferably Escherichia coli, to enrich binders in several selection rounds. Traditionally, the so-called panning procedure during which the phages are incubated with the target protein, washed and eluted is done manually, limiting the throughput. High-throughput systems with automated panning already in use often require high-priced equipment. Moreover, the bottleneck of the selection process is usually the screening and characterization. Therefore, having a high-throughput panning procedure without a scaled screening platform does not necessarily increase the discovery rate. Here, we present an easy-to-use high-throughput selection system with automated panning using cost-efficient equipment integrated into a workflow with high-throughput sequencing and a tailored screening step using biolayer-interferometry. The workflow has been developed for selections using two recombinant libraries, ADAPT (Albumin-binding domain-derived affinity proteins) and CaRA (Calcium-regulated affinity) and has been evaluated for three new targets. The newly established semi-automated system drastically reduced the hands-on time and increased robustness while the selection outcome, when compared to manual handling, was very similar in deep sequencing analysis and generated binders in the nanomolar affinity range. The developed selection system has shown to be highly versatile and has the potential to be applied to other binding domains for the discovery of new protein binders.

The development of blood protein profiles in extremely preterm infants follows a stereotypic evolution pattern.

BACKGROUND: Preterm birth is the leading cause of neonatal mortality and morbidity. Early diagnosis and interventions are critical to improving the clinical outcomes of extremely premature infants. Blood protein profiling during the first months of life in preterm infants can shed light on the role of early extrauterine development and provide an increased understanding of maturation after extremely preterm birth and the underlying mechanisms of prematurity-related disorders. METHODS: We have investigated the blood protein profiles during the first months of life in preterm infants on the role of early extrauterine development. The blood protein levels were analyzed using next generation blood profiling on 1335 serum samples, collected longitudinally at nine time points from birth to full-term from 182 extremely preterm infants. RESULTS: The protein analysis reveals evident predestined serum evolution patterns common for all included infants. The majority of the variations in blood protein expression are associated with the postnatal age of the preterm infants rather than any other factors. There is a uniform protein pattern on postnatal day 1 and after 30 weeks postmenstrual age (PMA), independent of gestational age (GA). However, during the first month of life, GA had a significant impact on protein variability. CONCLUSIONS: The unified pattern of protein development for all included infants suggests an age-dependent stereotypic development of blood proteins after birth. This knowledge should be considered in neonatal settings and might alter the clinical approach within neonatology, where PMA is today the most dominant age variable.

Being born too early can affect a baby's health. We looked at how babies born extremely preterm, meaning more than 12 weeks earlier than a full-term baby, develop. We looked at the proteins present in their blood from the day they were born until their original due date. Our study of 182 extremely preterm babies born at different points in the pregnancy (gestational ages) found that the proteins present in their blood changed in a similar way over time. This means that the age of a baby after birth, and not how early they were born, mostly affects the proteins in their blood. These findings help us understand how extremely preterm babies develop after birth, which could lead to improvements to their healthcare during the first few weeks of their life.

Sequential sequencing by synthesis and the next-generation sequencing revolution.

The impact of next-generation sequencing (NGS) cannot be overestimated. The technology has transformed the field of life science, contributing to a dramatic expansion in our understanding of human health and disease and our understanding of biology and ecology. The vast majority of the major NGS systems today are based on the concept of 'sequencing by synthesis' (SBS) with sequential detection of nucleotide incorporation using an engineered DNA polymerase. Based on this strategy, various alternative platforms have been developed, including the use of either native nucleotides or reversible terminators and different strategies for the attachment of DNA to a solid support. In this review, some of the key concepts leading to this remarkable development are discussed.

Next generation pan-cancer blood proteome profiling using proximity extension assay.

A comprehensive characterization of blood proteome profiles in cancer patients can contribute to a better understanding of the disease etiology, resulting in earlier diagnosis, risk stratification and better monitoring of the different cancer subtypes. Here, we describe the use of next generation protein profiling to explore the proteome signature in blood across patients representing many of the major cancer types. Plasma profiles of 1463 proteins from more than 1400 cancer patients are measured in minute amounts of blood collected at the time of diagnosis and before treatment. An open access Disease Blood Atlas resource allows the exploration of the individual protein profiles in blood collected from the individual cancer patients. We also present studies in which classification models based on machine learning have been used for the identification of a set of proteins associated with each of the analyzed cancers. The implication for cancer precision medicine of next generation plasma profiling is discussed.

Elevated plasma complement factor H related 5 protein is associated with venous thromboembolism.

Venous thromboembolism (VTE) is a common, multi-causal disease with potentially serious short- and long-term complications. In clinical practice, there is a need for improved plasma biomarker-based tools for VTE diagnosis and risk prediction. Here we show, using proteomics profiling to screen plasma from patients with suspected acute VTE, and several case-control studies for VTE, how Complement Factor H Related 5 protein (CFHR5), a regulator of the alternative pathway of complement activation, is a VTE-associated plasma biomarker. In plasma, higher CFHR5 levels are associated with increased thrombin generation potential and recombinant CFHR5 enhanced platelet activation in vitro. GWAS analysis of ~52,000 participants identifies six loci associated with CFHR5 plasma levels, but Mendelian randomization do not demonstrate causality between CFHR5 and VTE. Our results indicate an important role for the regulation of the alternative pathway of complement activation in VTE and that CFHR5 represents a potential diagnostic and/or risk predictive plasma biomarker.

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.

The acute effect of different NAD+ precursors included in the combined metabolic activators.

NAD+ and glutathione precursors are currently used as metabolic modulators for improving the metabolic conditions associated with various human diseases, including non-alcoholic fatty liver disease, neurodegenerative diseases, mitochondrial myopathy, and age-induced diabetes. Here, we performed a one-day double blinded, placebo-controlled human clinical study to assess the safety and acute effects of six different Combined Metabolic Activators (CMAs) with 1 g of different NAD+ precursors based on global metabolomics analysis. Our integrative analysis showed that the NAD+ salvage pathway is the main source for boosting the NAD+ levels with the administration of CMAs without NAD+ precursors. We observed that incorporation of nicotinamide (Nam) in the CMAs can boost the NAD+ products, followed by niacin (NA), nicotinamide riboside (NR) and nicotinamide mononucleotide (NMN), but not flush free niacin (FFN). In addition, the NA administration led to a flushing reaction, accompanied by decreased phospholipids and increased bilirubin and bilirubin derivatives, which could be potentially risky. In conclusion, this study provided a plasma metabolomic landscape of different CMA formulations, and proposed that CMAs with Nam, NMN as well as NR can be administered for boosting NAD+ levels to improve altered metabolic conditions.

Multiplexed selectivity screening of anti-GPCR antibodies.

G protein-coupled receptors (GPCRs) control critical cellular signaling pathways. Therapeutic agents including anti-GPCR antibodies (Abs) are being developed to modulate GPCR function. However, validating the selectivity of anti-GPCR Abs is challenging because of sequence similarities among individual receptors within GPCR subfamilies. To address this challenge, we developed a multiplexed immunoassay to test >400 anti-GPCR Abs from the Human Protein Atlas targeting a customized library of 215 expressed and solubilized GPCRs representing all GPCR subfamilies. We found that ~61% of Abs tested were selective for their intended target, ~11% bound off-target, and ~28% did not bind to any GPCR. Antigens of on-target Abs were, on average, significantly longer, more disordered, and less likely to be buried in the interior of the GPCR protein than the other Abs. These results provide important insights into the immunogenicity of GPCR epitopes and form a basis for designing therapeutic Abs and for detecting pathological auto-Abs against GPCRs.

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.