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.

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.

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.

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.

iNetModels 2.0: an interactive visualization and database of multi-omics data.

It is essential to reveal the associations between various omics data for a comprehensive understanding of the altered biological process in human wellness and disease. To date, very few studies have focused on collecting and exhibiting multi-omics associations in a single database. Here, we present iNetModels, an interactive database and visualization platform of Multi-Omics Biological Networks (MOBNs). This platform describes the associations between the clinical chemistry, anthropometric parameters, plasma proteomics, plasma metabolomics, as well as metagenomics for oral and gut microbiome obtained from the same individuals. Moreover, iNetModels includes tissue- and cancer-specific Gene Co-expression Networks (GCNs) for exploring the connections between the specific genes. This platform allows the user to interactively explore a single feature's association with other omics data and customize its particular context (e.g. male/female specific). The users can also register their data for sharing and visualization of the MOBNs and GCNs. Moreover, iNetModels allows users who do not have a bioinformatics background to facilitate human wellness and disease research. iNetModels can be accessed freely at https://inetmodels.com without any limitation.

Genome-wide annotation of protein-coding genes in pig.

BACKGROUND: There is a need for functional genome-wide annotation of the protein-coding genes to get a deeper understanding of mammalian biology. Here, a new annotation strategy is introduced based on dimensionality reduction and density-based clustering of whole-body co-expression patterns. This strategy has been used to explore the gene expression landscape in pig, and we present a whole-body map of all protein-coding genes in all major pig tissues and organs. RESULTS: An open-access pig expression map ( www.rnaatlas.org ) is presented based on the expression of 350 samples across 98 well-defined pig tissues divided into 44 tissue groups. A new UMAP-based classification scheme is introduced, in which all protein-coding genes are stratified into tissue expression clusters based on body-wide expression profiles. The distribution and tissue specificity of all 22,342 protein-coding pig genes are presented. CONCLUSIONS: Here, we present a new genome-wide annotation strategy based on dimensionality reduction and density-based clustering. A genome-wide resource of the transcriptome map across all major tissues and organs in pig is presented, and the data is available as an open-access resource ( www.rnaatlas.org ), including a comparison to the expression of human orthologs.

Systems biology based drug repositioning for development of cancer therapy.

Drug repositioning is a powerful method that can assists the conventional drug discovery process by using existing drugs for treatment of a disease rather than its original indication. The first examples of repurposed drugs were discovered serendipitously, however data accumulated by high-throughput screenings and advancements in computational biology methods have paved the way for rational drug repositioning methods. As chemotherapeutic agents have notorious side effects that significantly reduce quality of life, drug repositioning promises repurposed noncancer drugs with little or tolerable adverse effects for cancer patients. Here, we review current drug-related data types and databases including some examples of web-based drug repositioning tools. Next, we describe systems biology approaches to be used in drug repositioning for effective cancer therapy. Finally, we highlight examples of mostly repurposed drugs for cancer treatment and provide an overview of future expectations in the field for development of effective treatment strategies.

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.

Plasma proteins elevated in severe asthma despite oral steroid use and unrelated to Type-2 inflammation.

RATIONALE: Asthma phenotyping requires novel biomarker discovery. OBJECTIVES: To identify plasma biomarkers associated with asthma phenotypes by application of a new proteomic panel to samples from two well-characterised cohorts of severe (SA) and mild-to-moderate (MMA) asthmatics, COPD subjects and healthy controls (HCs). METHODS: An antibody-based array targeting 177 proteins predominantly involved in pathways relevant to inflammation, lipid metabolism, signal transduction and extracellular matrix was applied to plasma from 525 asthmatics and HCs in the U-BIOPRED cohort, and 142 subjects with asthma and COPD from the validation cohort BIOAIR. Effects of oral corticosteroids (OCS) were determined by a 2-week, placebo-controlled OCS trial in BIOAIR, and confirmed by relation to objective OCS measures in U-BIOPRED. RESULTS: In U-BIOPRED, 110 proteins were significantly different, mostly elevated, in SA compared to MMA and HCs. 10 proteins were elevated in SA versus MMA in both U-BIOPRED and BIOAIR (alpha-1-antichymotrypsin, apolipoprotein-E, complement component 9, complement factor I, macrophage inflammatory protein-3, interleukin-6, sphingomyelin phosphodiesterase 3, TNF receptor superfamily member 11a, transforming growth factor-ß and glutathione S-transferase). OCS treatment decreased most proteins, yet differences between SA and MMA remained following correction for OCS use. Consensus clustering of U-BIOPRED protein data yielded six clusters associated with asthma control, quality of life, blood neutrophils, high-sensitivity C-reactive protein and body mass index, but not Type-2 inflammatory biomarkers. The mast cell specific enzyme carboxypeptidase A3 was one major contributor to cluster differentiation. CONCLUSIONS: The plasma proteomic panel revealed previously unexplored yet potentially useful Type-2-independent biomarkers and validated several proteins with established involvement in the pathophysiology of SA.