New technologies enable single-cell transcriptome analysis, mapping genome-wide expression across the human body. Here, we present an extended analysis of protein-coding genes in all major human tissues and organs, combining single-cell and bulk transcriptomics. To enhance transcriptome depth, 31 tissues were analyzed using a pooling method, identifying 557 unique cell clusters, manually annotated by marker gene expression. Genes were classified by body-wide expression and validated through antibody-based profiling. All results are available in the updated open-access Single Cell Type section of the Human Protein Atlas for genome-wide exploration of genes, proteins, and their spatial distribution in cells.

Herbs are extensively utilized in Traditional Chinese Medicine (TCM) for lung and liver cancer treatment, but the mechanisms behind these herbs remain largely unknown. Here, high-throughput transcriptomic analysis technology was used to uncover molecular mechanisms of herbal treatment. Furthermore, we developed a compound recognition approach utilizing the LINCS L1000 database to identify potential treatment targets. Our results showed that among 14 herbs tested, Pulsatilla chinensis exhibited the strongest anticancer effects in A549 and Huh7 cells, followed by Bupleurum chinense, and Polyporus umbellatus. P. chinensis exerts its anticancer properties by downregulating cell cycle-related transcription factors, including E2F1 and TFDP1. Notably, the mechanisms of P. chinensis treatment differed between the two cell lines. In A549 cells, which possess wild-type p53, P. chinensis induced apoptosis through the regulation of the p53 pathway. In contrast, in Huh7 cells, which harbor mutant p53, the effect was mediated via the TNF-alpha/NF-kappa B signaling pathway. We also identified two drugs, AMG232 and Nutlin-3, that exhibited treatment effects similar to P. chinensis in A549 cells. Both drugs function as inhibitors of the MDM2-p53 interaction. Western blot analysis confirmed the alteration of the relevant proteins, aligning with our computational predictions. Furthermore, 23-hydroxybetulinic acid, a key active compound of P. chinensis, demonstrated the ability to inhibit the p53-MDM2 interaction by binding to the same pocket on the MDM2 protein.

Protein secretion is an essential process of mammalian cells. In biomanufacturing, this process can be optimized to enhance production yields and biotherapeutic quality. While cell line engineering and bioprocess optimization have yielded high protein titers for some recombinant proteins, many remain difficult to express. Here, we investigated factors influencing protein expression in Chinese hamster ovary (CHO) cells, expressing 2,135 Human Secretome Project proteins. While the abundance of mRNA from recombinant proteins explained less than 1% of observed variation in secretion titers, analysis of 218 biochemical and biophysical descriptors uncovered intrinsic protein features that account for ~15% of secretion variability, pinpointing key drivers such as molecular weight, cysteine content, and N-linked glycosylation, and establishing a roadmap for rational design of difficult-to-express proteins. We subsequently analyzed RNA-Seq data from 95 CHO cell cultures, each expressing a distinct recombinant protein, spanning a wide range of titers. Host cell transcriptomic signatures showed strong correlations with titer, thereby providing insights into cellular processes that covary with expression. Cells failing to produce proteins exhibited increased ubiquitin-mediated proteasomal degradation, including ER-associated degradation; whereas high-producing cells demonstrated enhanced lipid metabolism and a stronger response to oxidative stress, suggesting these factors may support successful recombinant protein productions. Together, using this resource, we quantified the contributions of various protein and cellular factors that correlate with the expression of diverse recombinant human proteins in a heterologous host, thereby providing insights for next-generation CHO cell engineering.

Chronic hepatic injury and inflammation from various causes can lead to fibrosis and cirrhosis, potentially predisposing to hepatocellular carcinoma. The molecular mechanisms underlying fibrosis and its progression remain incompletely understood. Using a proteo-transcriptomics approach, we analyze liver and plasma samples from 330 individuals, including 40 healthy individuals and 290 patients with histologically characterized fibrosis due to chronic viral infection, alcohol consumption, or metabolic dysfunction-associated steatotic liver disease. Our findings reveal dysregulated pathways related to extracellular matrix, immune response, inflammation, and metabolism in advanced fibrosis. We also identify 132 circulating proteins associated with advanced fibrosis, with neurofascin and growth differentiation factor 15 demonstrating superior predictive performance for advanced fibrosis(area under the receiver operating characteristic curve [AUROC] 0.89 [95% confidence interval (CI) 0.81–0.97]) compared to the fibrosis-4 model (AUROC 0.85 [95% CI 0.78–0.93]). These findings provide insights into fibrosis pathogenesis and highlight the potential for more accurate non-invasive diagnosis.

Primary cilia are critical organelles found on most human cells. Their dysfunction is linked to hereditary ciliopathies with a wide phenotypic spectrum. Despite their significance, the specific roles of cilia in different cell types remain poorly understood due to limitations in analyzing ciliary protein composition. We employed antibody-based spatial proteomics to expand the Human Protein Atlas to primary cilia. Our analysis identified the subciliary locations of 715 proteins across three cell lines, examining 128,156 individual cilia. We found that 69% of the ciliary proteome is cell-type specific, and 78% exhibited single-cilia heterogeneity. Our findings portray cilia as sensors tuning their proteome to effectively sense the environment and compute cellular responses. We reveal 91 cilia proteins and found a genetic candidate variant in CREB3 in one clinical case with features overlapping ciliopathy phenotypes. This open, spatial cilia atlas advances research on cilia and ciliopathies.

Background: Understanding how genetics and environmental factors shape human metabolic profiles is crucial for advancing metabolic health. Variability in metabolic profiles, influenced by genetic makeup, lifestyle, and environmental exposures, plays a critical role in disease susceptibility and progression. Methods: We conducted a two-year longitudinal study involving 101 clinically healthy individuals aged 50 to 65, integrating genomics, metabolomics, lipidomics, proteomics, clinical measurements, and lifestyle questionnaire data from repeat sampling. We evaluated the influence of both external and internal factors, including genetic predispositions, lifestyle factors, and physiological conditions, on individual metabolic profiles. Additionally, we developed an integrative metabolite-protein network to analyze protein-metabolite associations under both genetic and environmental regulations. Results: Our findings highlighted the significant role of genetics in determining metabolic variability, identifying 22 plasma metabolites as genetically predetermined. Environmental factors such as seasonal variation, weight management, smoking, and stress also significantly influenced metabolite levels. The integrative metabolite-protein network comprised 5,649 significant protein-metabolite pairs and identified 87 causal metabolite-protein associations under genetic regulation, validated by showing a high replication rate in an independent cohort. This network revealed stable and unique protein-metabolite profiles for each individual, emphasizing metabolic individuality. Notably, our results demonstrated the importance of plasma proteins in capturing individualized metabolic variabilities. Key proteins related to individual metabolic profiles were identified and validated in the UK Biobank, showing great potential for metabolic risk assessment. Conclusions: Our study provides longitudinal insights into how genetic and environmental factors shape human metabolic profiles, revealing unique and stable individual metabolic profiles. Plasma proteins emerged as key indicators for capturing the variability in human metabolism and assessing metabolic risks. These findings offer valuable tools for personalized medicine and the development of diagnostics for metabolic diseases.

Generating longitudinal and multi-layered big biological data is crucial for effectively implementing artificial intelligence (AI) and systems biology approaches in characterising whole-body biological functions in health and complex disease states. Big biological data consists of multi-omics, clinical, wearable device, and imaging data, and information on diet, drugs, toxins, and other environmental factors. Given the significant advancements in omics technologies, human metabologenomics, and computational capabilities, several multi-omics studies are underway. Here, we first review the recent application of AI and systems biology in integrating and interpreting multi-omics data, highlighting their contributions to the creation of digital twins and the discovery of novel biomarkers and drug targets. Next, we review the multi-omics datasets generated worldwide to reveal interactions across multiple biological layers of information over time, which enhance precision health and medicine. Finally, we address the need to incorporate big biological data into clinical practice, supporting the development of a clinical decision support system essential for AI-driven hospitals and creating the foundation for an AI and systems biology-based healthcare model.

The human microbiome is increasingly recognized for its crucial role in the development and progression of neurodegenerative diseases. While the gut-brain axis has been extensively studied, the contribution of the oral microbiome and gut-oral tropism in neurodegeneration has been largely overlooked. Cognitive impairment (CI) is common in neurodegenerative diseases and develops on a spectrum. In Parkinson’s Disease (PD) patients, CI is one of the most common non-motor symptoms but its mechanistic development across the spectrum remains unclear, complicating early diagnosis of at-risk individuals. Here, we generated 228 shotgun metagenomics samples of the gut and oral microbiomes across PD patients with mild cognitive impairment (PD-MCI) or dementia (PDD), and a healthy cohort, to study the role of gut and oral microbiomes on CI in PD. In addition to revealing compositional and functional signatures, the role of pathobionts, and dysregulated metabolic pathways of the oral and gut microbiome in PD-MCI and PDD, we also revealed the importance of oral-gut translocation in increasing abundance of virulence factors in PD and CI. The oral-gut virulence was further integrated with saliva metaproteomics and demonstrated their potential role in dysfunction of host immunity and brain endothelial cells. Our findings highlight the significance of the oral-gut-brain axis and underscore its potential for discovering novel biomarkers for PD and CI.

Parkinson’s disease is primarily marked by mitochondrial dysfunction and metabolic abnormalities. We recently reported that the combined metabolic activators improved the immunohistochemical parameters and behavioural functions in Parkinson’s disease and Alzheimer’s disease animal models and the cognitive functions in Alzheimer’s disease patients. These metabolic activators serve as the precursors of nicotinamide adenine dinucleotide and glutathione, and they can be used to activate mitochondrial metabolism and eventually treat mitochondrial dysfunction. Here, we designed a randomized, double-blinded, placebo-controlled phase II study in Parkinson’s disease patients with 84 days combined metabolic activator administration. A single dose of combined metabolic activator contains L-serine (12.35 g), N-acetyl-L-cysteine (2.55 g), nicotinamide riboside (1 g) and L-carnitine tartrate (3.73 g). Patients were administered either one dose of combined metabolic activator or a placebo daily for the initial 28 days, followed by twice-daily dosing for the next 56 days. The main goal of the study was to evaluate the clinical impact on motor functions using the Unified Parkinson’s Disease Rating Scale and to determine the safety and tolerability of combined metabolic activator. A secondary objective was to assess cognitive functions utilizing the Montreal Cognitive Assessment and to analyse brain activity through functional MRI. We also performed comprehensive plasma metabolomics and proteomics analysis for detailed characterization of Parkinson’s disease patients who participated in the study. Although no improvement in motor functions was observed, cognitive function was shown to be significantly improved (P < 0.0000) in Parkinson’s disease patients treated with the combined metabolic activator group over 84 days, whereas no such improvement was noted in the placebo group (P > 0.05). Moreover, a significant reduction (P = 0.001) in Montreal Cognitive Assessment scores was observed in the combined metabolic activator group, with no decline (P > 0.05) in the placebo group among severe Parkinson’s disease patients with lower baseline Montreal Cognitive Assessment scores. We showed that improvement in cognition was associated with critical brain network alterations based on functional MRI analysis, especially relevant to areas with cognitive functions in the brain. Finally, through a comprehensive multi-omics analysis, we elucidated the molecular mechanisms underlying cognitive improvements observed in Parkinson’s disease patients. Our results show that combined metabolic activator administration leads to enhanced cognitive function and improved metabolic health in Parkinson’s disease patients as recently shown in Alzheimer’s disease patients.

Excessive consumption of sucrose, in the form of sugar-sweetened beverages, has been implicated in the pathogenesis of metabolic dysfunction-associated fatty liver disease (MAFLD) and other related metabolic syndromes. The c-Jun N-terminal kinase (JNK) pathway plays a crucial role in response to dietary stressors, and it was demonstrated that the inhibition of the JNK pathway could potentially be used in the treatment of MAFLD. However, the intricate mechanisms underlying these interventions remain incompletely understood given their multifaceted effects across multiple tissues. In this study, we challenged rats with sucrose-sweetened water and investigated the potential effects of JNK inhibition by employing network analysis based on the transcriptome profiling obtained from hepatic and extrahepatic tissues, including visceral white adipose tissue, skeletal muscle, and brain. Our data demonstrate that JNK inhibition by JNK-IN-5A effectively reduces the circulating triglyceride accumulation and inflammation in rats subjected to sucrose consumption. Coexpression analysis and genome-scale metabolic modeling reveal that sucrose overconsumption primarily induces transcriptional dysfunction related to fatty acid and oxidative metabolism in the liver and adipose tissues, which are largely rectified after JNK inhibition at a clinically relevant dose. Skeletal muscle exhibited minimal transcriptional changes to sucrose overconsumption but underwent substantial metabolic adaptation following the JNK inhibition. Overall, our data provides novel insights into the molecular basis by which JNK inhibition exerts its metabolic effect in the metabolically active tissues. Furthermore, our findings underpin the critical role of extrahepatic metabolism in the development of diet-induced steatosis, offering valuable guidance for future studies focused on JNK-targeting for effective treatment of MAFLD.