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 Z_EGFR: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.

Targeted proteomics using stable isotope labeled protein fragments enables precise and robust determination of total apolipoprotein(a) in human plasma

Lipoprotein(a), also known as Lp(a), is an LDL-like particle composed of apolipoprotein(a) (apo(a)) bound covalently to apolipoprotein B100. Plasma concentrations of Lp(a) are highly heritable and vary widely between individuals. Elevated plasma concentration of Lp(a) is considered as an independent, causal risk factor of cardiovascular disease (CVD). Targeted mass spectrometry (LC-SRM/MS) combined with stable isotope-labeled recombinant proteins provides robust and precise quantification of proteins in the blood, making LC-SRM/MS assays appealing for monitoring plasma proteins for clinical implications. This study presents a novel quantitative approach, based on proteotypic peptides, to determine the absolute concentration of apo(a) from two microliters of plasma and qualified according to guideline requirements for targeted proteomics assays. After optimization, assay parameters such as linearity, lower limits of quantification (LLOQ), intra-assay variability (CV: 4.7%) and inter-assay repeatability (CV: 7.8%) were determined and the LC-SRM/MS results were benchmarked against a commercially available immunoassay. In summary, the measurements of an apo(a) single copy specific peptide and a kringle 4 specific peptide allow for the determination of molar concentration and relative size of apo(a) in individuals.

Combined metabolic activators improve metabolic functions in the animal models of neurodegenerative diseases

BACKGROUND

Neurodegenerative diseases (NDDs), including Alzheimer's disease (AD) and Parkinson's disease (PD), are associated with metabolic abnormalities. Integrative analysis of human clinical data and animal studies have contributed to a better understanding of the molecular and cellular pathways involved in the progression of NDDs. Previously, we have reported that the combined metabolic activators (CMA), which include the precursors of nicotinamide adenine dinucleotide and glutathione can be utilized to alleviate metabolic disorders by activating mitochondrial metabolism.

METHODS

We first analysed the brain transcriptomics data from AD patients and controls using a brain-specific genome-scale metabolic model (GEM). Then, we investigated the effect of CMA administration in animal models of AD and PD. We evaluated pathological and immunohistochemical findings of brain and liver tissues. Moreover, PD rats were tested for locomotor activity and apomorphine-induced rotation.

FINDINGS

Analysis of transcriptomics data with GEM revealed that mitochondrial dysfunction is involved in the underlying molecular pathways of AD. In animal models of AD and PD, we showed significant damage in the high-fat diet groups' brain and liver tissues compared to the chow diet. The histological analyses revealed that hyperemia, degeneration and necrosis in neurons were improved by CMA administration in both AD and PD animal models. These findings were supported by immunohistochemical evidence of decreased immunoreactivity in neurons. In parallel to the improvement in the brain, we also observed dramatic metabolic improvement in the liver tissue. CMA administration also showed a beneficial effect on behavioural functions in PD rats.

INTERPRETATION

Overall, we showed that CMA administration significantly improved behavioural scores in parallel with the neurohistological outcomes in the AD and PD animal models and is a promising treatment for improving the metabolic parameters and brain functions in NDDs.

Combined metabolic activators improve cognitive functions in Alzheimer's disease patients: a randomised, double-blinded, placebo-controlled phase-II trial

BACKGROUND

Alzheimer's disease (AD) is associated with metabolic abnormalities linked to critical elements of neurodegeneration. We recently administered combined metabolic activators (CMA) to the AD rat model and observed that CMA improves the AD-associated histological parameters in the animals. CMA promotes mitochondrial fatty acid uptake from the cytosol, facilitates fatty acid oxidation in the mitochondria, and alleviates oxidative stress.

METHODS

Here, we designed a randomised, double-blinded, placebo-controlled phase-II clinical trial and studied the effect of CMA administration on the global metabolism of AD patients. One-dose CMA included 12.35 g L-serine (61.75%), 1 g nicotinamide riboside (5%), 2.55 g N-acetyl-L-cysteine (12.75%), and 3.73 g L-carnitine tartrate (18.65%). AD patients received one dose of CMA or placebo daily during the first 28 days and twice daily between day 28 and day 84. The primary endpoint was the difference in the cognitive function and daily living activity scores between the placebo and the treatment arms. The secondary aim of this study was to evaluate the safety and tolerability of CMA. A comprehensive plasma metabolome and proteome analysis was also performed to evaluate the efficacy of the CMA in AD patients.

RESULTS

We showed a significant decrease of AD Assessment Scale-cognitive subscale (ADAS-Cog) score on day 84 vs day 0 (P = 0.00001, 29% improvement) in the CMA group. Moreover, there was a significant decline (P = 0.0073) in ADAS-Cog scores (improvement of cognitive functions) in the CMA compared to the placebo group in patients with higher ADAS-Cog scores. Improved cognitive functions in AD patients were supported by the relevant alterations in the hippocampal volumes and cortical thickness based on imaging analysis. Moreover, the plasma levels of proteins and metabolites associated with NAD + and glutathione metabolism were significantly improved after CMA treatment.

CONCLUSION

Our results indicate that treatment of AD patients with CMA can lead to enhanced cognitive functions and improved clinical parameters associated with phenomics, metabolomics, proteomics and imaging analysis. Trial registration ClinicalTrials.gov NCT04044131 Registered 17 July 2019, https://clinicaltrials.gov/ct2/show/NCT04044131.

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.

CaRA - A multi-purpose phage display library for selection of calcium-regulated affinity proteins

Protein activity regulated by interactions with metal ions can be utilized for many different purposes, including biological therapies and bioprocessing, among others. Calcium ions are known to interact with the frequently occurring EF-hand motif, which can alter protein activity upon binding through an induced conformational change. The calcium-binding loop of the EF-hand motif has previously been introduced into a small protein domain derived from staphylococcal Protein A in a successful effort to render antibody binding dependent on calcium. Presented here, is a combinatorial library for calcium-regulated affinity, CaRA, based on this domain. CaRA is the first alternative scaffold library designed to achieve novel target specificities with metal-dependent binding. From this library, several calcium-dependent binders could be isolated through phage display campaigns towards a set of unrelated target proteins (IgE Cε3-Cε4, TNFα, IL23, scFv, tPA, PCSK9 and HER3) useful for distinct applications. Overall, these monomeric CaRA variants showed high stability and target affinities within the nanomolar range. They displayed considerably higher melting temperatures in the presence of 1 mM calcium compared to without calcium. Further, all discovered binders proved to be calcium-dependent, with the great majority showing complete lack of target binding in the absence of calcium. As demonstrated, the CaRA library is highly capable of providing protein-binding domains with calcium-dependent behavior, independent of the type of target protein. These binding domains could subsequently be of great use in gentle protein purification or as novel therapeutic modalities.

Addressing the Protease Bias in Quantitative Proteomics

Protein quantification strategies using multiple proteases have been shown to deliver poor interprotease accuracy in label-free mass spectrometry experiments. By utilizing six different proteases with different cleavage sites, this study explores the protease bias and its effect on accuracy and precision by using recombinant protein standards. We established 557 SRM assays, using a recombinant protein standard resource, toward 10 proteins in human plasma and determined their concentration with multiple proteases. The quantified peptides of these plasma proteins spanned 3 orders of magnitude (0.02–70 μM). In total, 60 peptides were used for absolute quantification and the majority of the peptides showed high robustness. The retained reproducibility was achieved by quantifying plasma proteins using spiked stable isotope standard recombinant proteins in a targeted proteomics workflow.

Machine Learning Analysis Reveals Biomarkers for the Detection of Neurological Diseases

It is critical to identify biomarkers for neurological diseases (NLDs) to accelerate drug discovery for effective treatment of patients of diseases that currently lack such treatments. In this work, we retrieved genotyping and clinical data from 1,223 UK Biobank participants to identify genetic and clinical biomarkers for NLDs, including Alzheimer's disease (AD), Parkinson's disease (PD), motor neuron disease (MND), and myasthenia gravis (MG). Using a machine learning modeling approach with Monte Carlo randomization, we identified a panel of informative diagnostic biomarkers for predicting AD, PD, MND, and MG, including classical liver disease markers such as alanine aminotransferase, alkaline phosphatase, and bilirubin. A multinomial model trained on accessible clinical markers could correctly predict an NLD diagnosis with an accuracy of 88.3%. We also explored genetic biomarkers. In a genome-wide association study of AD, PD, MND, and MG patients, we identified single nucleotide polymorphisms (SNPs) implicated in several craniofacial disorders such as apnoea and branchiootic syndrome. We found evidence for shared genetic risk loci among NLDs, including SNPs in cancer-related genes and SNPs known to be associated with non-brain cancers such as Wilms tumor, leukemia, and colon cancer. This indicates overlapping genetic characterizations among NLDs which challenges current clinical definitions of the neurological disorders. Taken together, this work demonstrates the value of data-driven approaches to identify novel biomarkers in the absence of any known or promising biomarkers.

Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays

Spatially resolved transcriptomic technologies are promising tools to study complex biological processes such as mammalian embryogenesis. However, the imbalance between resolution, gene capture, and field of view of current methodologies precludes their systematic application to analyze relatively large and three-dimensional mid- and late-gestation embryos. Here, we combined DNA nanoball (DNB)-patterned arrays and in situ RNA capture to create spatial enhanced resolution omics-sequencing (Stereo-seq). We applied Stereo-seq to generate the mouse organogenesis spatiotemporal transcriptomic atlas (MOSTA), which maps with single-cell resolution and high sensitivity the kinetics and directionality of transcriptional variation during mouse organogenesis. We used this information to gain insight into the molecular basis of spatial cell heterogeneity and cell fate specification in developing tissues such as the dorsal midbrain. Our panoramic atlas will facilitate in-depth investigation of longstanding questions concerning normal and abnormal mammalian development.

Dynamics of the normal gut microbiota: A longitudinal one-year population study in Sweden

Temporal dynamics of the gut microbiota potentially limit the identification of microbial features associated with health status. Here, we used whole-genome metagenomic and 16S rRNA gene sequencing to characterize the intra- and inter-individual variations of gut microbiota composition and functional potential of a disease-free Swedish population (n = 75) over one year. We found that 23% of the total compositional variance was explained by intra-individual variation. The degree of intra-individual compositional variability was negatively associated with the abundance of Faecalibacterium prausnitzii (a butyrate producer) and two Bifidobacterium species. By contrast, the abundance of facultative anaerobes and aerotolerant bacteria such as Escherichia coli and Lactobacillus acidophilus varied extensively, independent of compositional stability. The contribution of intra-individual variance to the total variance was greater for functional pathways than for microbial species. Thus, reliable quantification of microbial features requires repeated samples to address the issue of intra-individual variations of the gut microbiota.

Multiomics Analysis Reveals the Impact of Microbiota on Host Metabolism in Hepatic Steatosis

Metabolic dysfunction-associated fatty liver disease (MAFLD) is a complex disease involving alterations in multiple biological processes regulated by the interactions between obesity, genetic background, and environmental factors including the microbiome. To decipher hepatic steatosis (HS) pathogenesis by excluding critical confounding factors including genetic variants and diabetes, 56 heterogenous MAFLD patients are characterized by generating multiomics data including oral and gut metagenomics as well as plasma metabolomics and inflammatory proteomics data. The dysbiosis in the oral and gut microbiome is explored and the host-microbiome interactions based on global metabolic and inflammatory processes are revealed. These multiomics data are integrated using the biological network and HS's key features are identified using multiomics data. HS is finally predicted using these key features and findings are validated in a follow-up cohort, where 22 subjects with varying degree of HS are characterized.

Next Generation Plasma Proteomics Identifies High-Precision Biomarker Candidates for Ovarian Cancer

Simple Summary

Ovarian cancer is the eighth most common cancer among women and has a 5-year survival of only 30–50%. The survival is close to 90% for patients in stage I but only 20% for patients in stage IV. The presently available biomarkers have insufficient sensitivity and specificity for early detection and there is an urgent need to identify novel biomarkers. The aim of our study was to broadly measure protein biomarkers to find tests for the early detection of ovarian cancer. We found that combinations of 4–7 protein biomarkers can provide highly accurate detection of early- and late-stage ovarian cancer compared to benign conditions. The performance of the tests was then validated in a second independent cohort.

Abstract

Background: Ovarian cancer is the eighth most common cancer among women and has a 5-year survival of only 30–50%. The survival is close to 90% for patients in stage I but only 20% for patients in stage IV. The presently available biomarkers have insufficient sensitivity and specificity for early detection and there is an urgent need to identify novel biomarkers. Methods: We employed the Explore PEA technology for high-precision analysis of 1463 plasma proteins and conducted a discovery and replication study using two clinical cohorts of previously untreated patients with benign or malignant ovarian tumours (N = 111 and N = 37). Results: The discovery analysis identified 32 proteins that had significantly higher levels in malignant cases as compared to benign diagnoses, and for 28 of these, the association was replicated in the second cohort. Multivariate modelling identified three highly accurate models based on 4 to 7 proteins each for separating benign tumours from early-stage and/or late-stage ovarian cancers, all with AUCs above 0.96 in the replication cohort. We also developed a model for separating the early-stage from the late-stage achieving an AUC of 0.81 in the replication cohort. These models were based on eleven proteins in total (ALPP, CXCL8, DPY30, IL6, IL12, KRT19, PAEP, TSPAN1, SIGLEC5, VTCN1, and WFDC2), notably without MUCIN-16. The majority of the associated proteins have been connected to ovarian cancer but not identified as potential biomarkers. Conclusions: The results show the ability of using high-precision proteomics for the identification of novel plasma protein biomarker candidates for the early detection of ovarian cancer.

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.

Circulating proteins reveal prior use of menopausal hormonal therapy and increased risk of breast cancer

Accessible risk predictors are crucial for improving the early detection and prognosis of breast cancer. Blood samples are widely available and contain proteins that provide important information about human health and disease, however, little is still known about the contribution of circulating proteins to breast cancer risk prediction. We profiled EDTA plasma samples collected before diagnosis from the Swedish KARMA breast cancer cohort to evaluate circulating proteins as molecular predictors. A data-driven analysis strategy was applied to the molecular phenotypes built on 700 circulating proteins to identify and annotate clusters of women. The unsupervised analysis of 183 future breast cancer cases and 366 age-matched controls revealed five stable clusters with distinct proteomic plasma profiles. Among these women, those in the most stable cluster (N = 19; mean Jaccard index: 0.70 ± 0.29) were significantly more likely to have used menopausal hormonal therapy (MHT), get a breast cancer diagnosis, and were older compared to the remaining clusters. The circulating proteins associated with this cluster (FDR < 0.001) represented physiological processes related to cell junctions (F11R, CLDN15, ITGAL), DNA repair (RBBP8), cell replication (TJP3), and included proteins found in female reproductive tissue (PTCH1, ZP4). Using a data-driven approach on plasma proteomics data revealed the potential long-lasting molecular effects of menopausal hormonal therapy (MHT) on the circulating proteome, even after women had ended their treatment. This provides valuable insights concerning proteomics efforts to identify molecular markers for breast cancer risk prediction.

Longitudinal Serum Metabolomics in Extremely Premature Infants: Relationships With Gestational Age, Nutrition, and Morbidities

An increasing number of extremely premature infants survive the neonatal period and beyond. Little is known about the maturation of the preterm infant’s metabolome and its relation to the development of morbidities. Using 1H-NMR, we investigated the serum metabolic profile of 87 infants born at a gestational age (GA) <28 weeks [mean GA (SD) 25.4 (1.4) weeks] in samples longitudinally collected from birth to term equivalent age. The infant metabolome was analyzed in relation to GA, postnatal age, nutrition, and preterm morbidities. At postnatal day 1, low GA correlated with high levels of 3-hydroxyisobutyrate, acetate, acetoacetate, acetone, formate, glucose, and valine. Nearly all quantified metabolites displayed postnatal concentration changes. For example, the two phospholipid-related metabolites myo-inositol and ethanolamine displayed a similar decline from birth over the first weeks of life, irrespectively of GA. The proportion of enteral/parenteral energy intake in the first 28 days significantly correlated with mean levels of 52% of the analyzed metabolites. Low enteral energy intake was associated with high serum levels of 3-hydroxyisobutyrate, creatinine, glucose, glycerol, histidine, lactate, leucine, lysine, methionine, ornithine, phenylalanine, proline, threonine, and uridine. There were also significant correlations between high enteral intake and high serum levels of isoleucine and tyrosine. Retinopathy of prematurity (ROP) and bronchopulmonary dysplasia (BPD) outcomes were not significantly associated with metabolite levels in the neonatal period after correcting for multiple testing. In conclusion, the serum metabolome of extremely premature infants changes substantially in the neonatal period, largely driven by the gradual transfer from total parenteral nutrition to full enteral feeding. Further studies are needed to disentangle the intricate relationships between the metabolome, nutritional management, GA, and the development of preterm morbidities.

Blood protein profiles related to preterm birth and retinopathy of prematurity

BACKGROUND

Nearly one in ten children is born preterm. The degree of immaturity is a determinant of the infant's health. Extremely preterm infants have higher morbidity and mortality than term infants. One disease affecting extremely preterm infants is retinopathy of prematurity (ROP), a multifactorial neurovascular disease that can lead to retinal detachment and blindness. The advances in omics technology have opened up possibilities to study protein expressions thoroughly with clinical accuracy, here used to increase the understanding of protein expression in relation to immaturity and ROP.

METHODS

Longitudinal serum protein profiles the first months after birth in 14 extremely preterm infants were integrated with perinatal and ROP data. In total, 448 unique protein targets were analyzed using Proximity Extension Assays.

RESULTS

We found 20 serum proteins associated with gestational age and/or ROP functioning within mainly angiogenesis, hematopoiesis, bone regulation, immune function, and lipid metabolism. Infants with severe ROP had persistent lower levels of several identified proteins during the first postnatal months.

CONCLUSIONS

The study contributes to the understanding of the relationship between longitudinal serum protein levels and immaturity and abnormal retinal neurovascular development. This is essential for understanding pathophysiological mechanisms and to optimize diagnosis, treatment and prevention for ROP.

IMPACT

Longitudinal protein profiles of 14 extremely preterm infants were analyzed using a novel multiplex protein analysis platform combined with perinatal data. Proteins associated with gestational age at birth and the neurovascular disease ROP were identified. Among infants with ROP, longitudinal levels of the identified proteins remained largely unchanged during the first postnatal months. The main functions of the proteins identified were angiogenesis, hematopoiesis, immune function, bone regulation, lipid metabolism, and central nervous system development. The study contributes to the understanding of longitudinal serum protein patterns related to gestational age and their association with abnormal retinal neuro-vascular development.

A network-based approach reveals the dysregulated transcriptional regulation in non-alcoholic fatty liver disease

Non-alcoholic fatty liver disease (NAFLD) is a leading cause of chronic liver disease worldwide. We performed network analysis to investigate the dysregulated biological processes in the disease progression and revealed the molecular mechanism underlying NAFLD. Based on network analysis, we identified a highly conserved disease-associated gene module across three different NAFLD cohorts and highlighted the predominant role of key transcriptional regulators associated with lipid and cholesterol metabolism. In addition, we revealed the detailed metabolic differences between heterogeneous NAFLD patients through integrative systems analysis of transcriptomic data and liver-specific genome-scale metabolic model. Furthermore, we identified transcription factors (TFs), including SREBF2, HNF4A, SREBF1, YY1, and KLF13, showing regulation of hepatic expression of genes in the NAFLD-associated modules and validated the TFs using data generated from a mouse NAFLD model. In conclusion, our integrative analysis facilitates the understanding of the regulatory mechanism of these perturbed TFs and their associated biological processes.

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Disease-associated gene modules are conserved across multiple NAFLD cohorts

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The central genes in disease-associated modules are key enzymes in cholesterol synthesis

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YY1 and KLF13 are potential key transcriptional regulators of NAFLD development

Rapid and sensitive detection of SARS-CoV-2 infection using quantitative peptide enrichment LC-MS analysis

Reliable, robust, large-scale molecular testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is essential for monitoring the ongoing coronavirus disease 2019 (COVID-19) pandemic. We have developed a scalable analytical approach to detect viral proteins based on peptide immuno-affinity enrichment combined with liquid chromatography-mass spectrometry (LC-MS). This is a multiplexed strategy, based on targeted proteomics analysis and read-out by LC-MS, capable of precisely quantifying and confirming the presence of SARS-CoV-2 in phosphate-buffered saline (PBS) swab media from combined throat/nasopharynx/saliva samples. The results reveal that the levels of SARS-CoV-2 measured by LC-MS correlate well with their correspondingreal-time polymerase chain reaction (RT-PCR) read-out (r = 0.79). The analytical workflow shows similar turnaround times as regular RT-PCR instrumentation with a quantitative read-out of viral proteins corresponding to cycle thresholds (Ct) equivalents ranging from 21 to 34. Using RT-PCR as a reference, we demonstrate that the LC-MS-based method has 100% negative percent agreement (estimated specificity) and 95% positive percent agreement (estimated sensitivity) when analyzing clinical samples collected from asymptomatic individuals with a Ct within the limit of detection of the mass spectrometer (Ct ≤ 30). These results suggest that a scalable analytical method based on LC-MS has a place in future pandemic preparedness centers to complement current virus detection technologies.

Identification of Endothelial Proteins in Plasma Associated With Cardiovascular Risk Factors

Supplemental Digital Content is available in the text.

Objective:

Endothelial cell (EC) dysfunction is a well-established response to cardiovascular disease risk factors, such as smoking and obesity. Risk factor exposure can modify EC signaling and behavior, leading to arterial and venous disease development. Here, we aimed to identify biomarker panels for the assessment of EC dysfunction, which could be useful for risk stratification or to monitor treatment response.

Approach and Results:

We used affinity proteomics to identify EC proteins circulating in plasma that were associated with cardiovascular disease risk factor exposure. Two hundred sixteen proteins, which we previously predicted to be EC-enriched across vascular beds, were measured in plasma samples (N=1005) from the population-based SCAPIS (Swedish Cardiopulmonary Bioimage Study) pilot. Thirty-eight of these proteins were associated with body mass index, total cholesterol, low-density lipoprotein, smoking, hypertension, or diabetes. Sex-specific analysis revealed that associations predominantly observed in female- or male-only samples were most frequently with the risk factors body mass index, or total cholesterol and smoking, respectively. We show a relationship between individual cardiovascular disease risk, calculated with the Framingham risk score, and the corresponding biomarker profiles.

Conclusions:

EC proteins in plasma could reflect vascular health status.

Revealing the Molecular Mechanisms of Alzheimer's Disease Based on Network Analysis

The complex pathology of Alzheimer's disease (AD) emphasises the need for comprehensive modelling of the disease, which may lead to the development of efficient treatment strategies. To address this challenge, we analysed transcriptome data of post-mortem human brain samples of healthy elders and individuals with late-onset AD from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP) and Mayo Clinic (MayoRNAseq) studies in the AMP-AD consortium. In this context, we conducted several bioinformatics and systems medicine analyses including the construction of AD-specific co-expression networks and genome-scale metabolic modelling of the brain in AD patients to identify key genes, metabolites and pathways involved in the progression of AD. We identified AMIGO1 and GRPRASP2 as examples of commonly altered marker genes in AD patients. Moreover, we found alterations in energy metabolism, represented by reduced oxidative phosphorylation and ATPase activity, as well as the depletion of hexanoyl-CoA, pentanoyl-CoA, (2E)-hexenoyl-CoA and numerous other unsaturated fatty acids in the brain. We also observed that neuroprotective metabolites (e.g., vitamins, retinoids and unsaturated fatty acids) tend to be depleted in the AD brain, while neurotoxic metabolites (e.g., β-alanine, bilirubin) were more abundant. In summary, we systematically revealed the key genes and pathways related to the progression of AD, gained insight into the crucial mechanisms of AD and identified some possible targets that could be used in the treatment of AD.

Combined Metabolic Activators Decrease Liver Steatosis by Activating Mitochondrial Metabolism in Hamsters Fed with a High-Fat Diet

Although the prevalence of non-alcoholic fatty liver disease (NAFLD) continues to increase, there is no effective treatment approved for this condition. We previously showed, in high-fat diet (HFD)-fed mice, that the supplementation of combined metabolic activators (CMA), including nicotinamide riboside (NAD+ precursor) and the potent glutathione precursors serine and N-acetyl-l-cysteine (NAC), significantly decreased fatty liver by promoting fat oxidation in mitochondria. Afterwards, in a one-day proof-of-concept human supplementation study, we observed that this CMA, including also L-carnitine tartrate (LCT), resulted in increased fatty acid oxidation and de novo glutathione synthesis. However, the underlying molecular mechanisms associated with supplementation of CMA have not been fully elucidated. Here, we demonstrated in hamsters that the chronic supplementation of this CMA (changing serine for betaine) at two doses significantly decreased hepatic steatosis. We further generated liver transcriptomics data and integrated these data using a liver-specific genome-scale metabolic model of liver tissue. We systemically determined the molecular changes after the supplementation of CMA and found that it activates mitochondria in the liver tissue by modulating global lipid, amino acid, antioxidant and folate metabolism. Our findings provide extra evidence about the beneficial effects of a treatment based on this CMA against NAFLD.

Combined metabolic activators therapy ameliorates liver fat in nonalcoholic fatty liver disease patients

Nonalcoholic fatty liver disease (NAFLD) refers to excess fat accumulation in the liver. In animal experiments and human kinetic study, we found that administration of combined metabolic activators (CMAs) promotes the oxidation of fat, attenuates the resulting oxidative stress, activates mitochondria, and eventually removes excess fat from the liver. Here, we tested the safety and efficacy of CMA in NAFLD patients in a placebo-controlled 10-week study. We found that CMA significantly decreased hepatic steatosis and levels of aspartate aminotransferase, alanine aminotransferase, uric acid, and creatinine, whereas found no differences on these variables in the placebo group after adjustment for weight loss. By integrating clinical data with plasma metabolomics and inflammatory proteomics as well as oral and gut metagenomic data, we revealed the underlying molecular mechanisms associated with the reduced hepatic fat and inflammation in NAFLD patients and identified the key players involved in the host-microbiome interactions. In conclusion, we showed that CMA can be used to develop a pharmacological treatment strategy in NAFLD patients.

Systems Analysis Reveals Ageing-Related Perturbations in Retinoids and Sex Hormones in Alzheimer's and Parkinson's Diseases

Neurodegenerative diseases, including Alzheimer's (AD) and Parkinson's diseases (PD), are complex heterogeneous diseases with highly variable patient responses to treatment. Due to the growing evidence for ageing-related clinical and pathological commonalities between AD and PD, these diseases have recently been studied in tandem. In this study, we analysed transcriptomic data from AD and PD patients, and stratified these patients into three subclasses with distinct gene expression and metabolic profiles. Through integrating transcriptomic data with a genome-scale metabolic model and validating our findings by network exploration and co-analysis using a zebrafish ageing model, we identified retinoids as a key ageing-related feature in all subclasses of AD and PD. We also demonstrated that the dysregulation of androgen metabolism by three different independent mechanisms is a source of heterogeneity in AD and PD. Taken together, our work highlights the need for stratification of AD/PD patients and development of personalised and precision medicine approaches based on the detailed characterisation of these subclasses.

Combined Metabolic Activators Accelerates Recovery in Mild-to-Moderate COVID-19

COVID-19 is associated with mitochondrial dysfunction and metabolic abnormalities, including the deficiencies in nicotinamide adenine dinucleotide (NAD+ ) and glutathione metabolism. Here it is investigated if administration of a mixture of combined metabolic activators (CMAs) consisting of glutathione and NAD+ precursors can restore metabolic function and thus aid the recovery of COVID-19 patients. CMAs include l-serine, N-acetyl-l-cysteine, nicotinamide riboside, and l-carnitine tartrate, salt form of l-carnitine. Placebo-controlled, open-label phase 2 study and double-blinded phase 3 clinical trials are conducted to investigate the time of symptom-free recovery on ambulatory patients using CMAs. The results of both studies show that the time to complete recovery is significantly shorter in the CMA group (6.6 vs 9.3 d) in phase 2 and (5.7 vs 9.2 d) in phase 3 trials compared to placebo group. A comprehensive analysis of the plasma metabolome and proteome reveals major metabolic changes. Plasma levels of proteins and metabolites associated with inflammation and antioxidant metabolism are significantly improved in patients treated with CMAs as compared to placebo. The results show that treating patients infected with COVID-19 with CMAs lead to a more rapid symptom-free recovery, suggesting a role for such a therapeutic regime in the treatment of infections leading to respiratory problems.