Human SRMAtlas: A Resource of Targeted Assays to Quantify the Complete Human Proteome.

The ability to reliably and reproducibly measure any protein of the human proteome in any tissue or cell type would be transformative for understanding systems-level properties as well as specific pathways in physiology and disease. Here, we describe the generation and verification of a compendium of highly specific assays that enable quantification of 99.7% of the 20,277 annotated human proteins by the widely accessible, sensitive, and robust targeted mass spectrometric method selected reaction monitoring, SRM. This human SRMAtlas provides definitive coordinates that conclusively identify the respective peptide in biological samples. We report data on 166,174 proteotypic peptides providing multiple, independent assays to quantify any human protein and numerous spliced variants, non-synonymous mutations, and post-translational modifications. The data are freely accessible as a resource at http://www.srmatlas.org/, and we demonstrate its utility by examining the network response to inhibition of cholesterol synthesis in liver cells and to docetaxel in prostate cancer lines.

Evolutionary origins and interactomes of human, young microproteins and small peptides translated from short open reading frames.

All species continuously evolve short open reading frames (sORFs) that can be templated for protein synthesis and may provide raw materials for evolutionary adaptation. We analyzed the evolutionary origins of 7,264 recently cataloged human sORFs and found that most were evolutionarily young and had emerged de novo. We additionally identified 221 previously missed sORFs potentially translated into peptides of up to 15 amino acids-all of which are smaller than the smallest human microprotein annotated to date. To investigate the bioactivity of sORF-encoded small peptides and young microproteins, we subjected 266 candidates to a mass-spectrometry-based interactome screen with motif resolution. Based on these interactomes and additional cellular assays, we can associate several candidates with mRNA splicing, translational regulation, and endocytosis. Our work provides insights into the evolutionary origins and interaction potential of young and small proteins, thereby helping to elucidate this underexplored territory of the human proteome.

circRNADisease v2.0: an updated resource for high-quality experimentally supported circRNA-disease associations.

circRNADisease v2.0 is an enhanced and reliable database that offers experimentally verified relationships between circular RNAs (circRNAs) and various diseases. It is accessible at http://cgga.org.cn/circRNADisease/ or http://cgga.org.cn:9091/circRNADisease/. The database currently includes 6998 circRNA-disease entries across multiple species, representing a remarkable 19.77-fold increase compared to the previous version. This expansion consists of a substantial rise in the number of circRNAs (from 330 to 4246), types of diseases (from 48 to 330) and covered species (from human only to 12 species). Furthermore, a new section has been introduced in the database, which collects information on circRNA-associated factors (genes, proteins and microRNAs), molecular mechanisms (molecular pathways), biological functions (proliferation, migration, invasion, etc.), tumor and/or cell line and/or patient-derived xenograft (PDX) details, and prognostic evidence in diseases. In addition, we identified 7 159 865 relationships between mutations and circRNAs among 30 TCGA cancer types. Due to notable enhancements and extensive data expansions, the circRNADisease 2.0 database has become an invaluable asset for both clinical practice and fundamental research. It enables researchers to develop a more comprehensive understanding of how circRNAs impact complex diseases.

Sugar status in preexisting leaves determines systemic stomatal development within newly developing leaves.

Stomata are pores found in the epidermis of stems or leaves that modulate both plant gas exchange and water/nutrient uptake. The development and function of plant stomata are regulated by a diverse range of environmental cues. However, how carbohydrate status in preexisting leaves might determine systemic stomatal formation within newly developing leaves has remained obscure. The glucose (Glc) sensor HEXOKINASE1 (HXK1) has been reported to decrease the stability of an ethylene/Glc signaling transcriptional regulator, EIN3 (ETHYLENE INSENSITIVE3). EIN3 in turn directly represses the expression of SUC2 (sucrose transporter 2), encoding a master transporter of sucrose (Suc). Further, KIN10, a nuclear regulator involved in energy homeostasis, has been reported to repress the transcription factor SPCH (SPEECHLESS), a master regulator of stomatal development. Here, we demonstrate that the Glc status of preexisting leaves determines systemic stomatal development within newly developing leaves by the HXK1-¦EIN3-¦SUC2 module. Further, increasing Glc levels in preexisting leaves results in a HXK1-dependent decrease of EIN3 and increase of SUC2, triggering the perception, amplification and relay of HXK1-dependent Glc signaling and thereby triggering Suc transport from mature to newly developing leaves. The HXK1-¦EIN3-¦SUC2 molecular module thereby drives systemic Suc transport from preexisting leaves to newly developing leaves. Subsequently, increasing Suc levels within newly developing leaves promotes stomatal formation through the established KIN10⟶ SPCH module. Our findings thus show how a carbohydrate signal in preexisting leaves is sensed, amplified and relayed to determine the extent of systemic stomatal development within newly developing leaves.

Detection and editing of the updated Arabidopsis plastid- and mitochondrial-encoded proteomes through PeptideAtlas.

Arabidopsis (Arabidopsis thaliana) ecotype Col-0 has plastid and mitochondrial genomes encoding over 100 proteins. Public databases (e.g. Araport11) have redundancy and discrepancies in gene identifiers for these organelle-encoded proteins. RNA editing results in changes to specific amino acid residues or creation of start and stop codons for many of these proteins, but the impact of RNA editing at the protein level is largely unexplored due to the complexities of detection. Here, we assembled the nonredundant set of identifiers, their correct protein sequences, and 452 predicted nonsynonymous editing sites of which 56 are edited at lower frequency. We then determined accumulation of edited and/or unedited proteoforms by searching ∼259 million raw tandem MS spectra from ProteomeXchange, which is part of PeptideAtlas (www.peptideatlas.org/builds/arabidopsis/). We identified all mitochondrial proteins and all except 3 plastid-encoded proteins (NdhG/Ndh6, PsbM, and Rps16), but no proteins predicted from the 4 ORFs were identified. We suggest that Rps16 and 3 of the ORFs are pseudogenes. Detection frequencies for each edit site and type of edit (e.g. S to L/F) were determined at the protein level, cross-referenced against the metadata (e.g. tissue), and evaluated for technical detection challenges. We detected 167 predicted edit sites at the proteome level. Minor frequency sites were edited at low frequency at the protein level except for cytochrome C biogenesis 382 at residue 124 (Ccb382-124). Major frequency sites (>50% editing of RNA) only accumulated in edited form (>98% to 100% edited) at the protein level, with the exception of Rpl5-22. We conclude that RNA editing for major editing sites is required for stable protein accumulation.

The ProteomeXchange consortium at 10 years: 2023 update.

Mass spectrometry (MS) is by far the most used experimental approach in high-throughput proteomics. The ProteomeXchange (PX) consortium of proteomics resources (http://www.proteomexchange.org) was originally set up to standardize data submission and dissemination of public MS proteomics data. It is now 10 years since the initial data workflow was implemented. In this manuscript, we describe the main developments in PX since the previous update manuscript in Nucleic Acids Research was published in 2020. The six members of the Consortium are PRIDE, PeptideAtlas (including PASSEL), MassIVE, jPOST, iProX and Panorama Public. We report the current data submission statistics, showcasing that the number of datasets submitted to PX resources has continued to increase every year. As of June 2022, more than 34 233 datasets had been submitted to PX resources, and from those, 20 062 (58.6%) just in the last three years. We also report the development of the Universal Spectrum Identifiers and the improvements in capturing the experimental metadata annotations. In parallel, we highlight that data re-use activities of public datasets continue to increase, enabling connections between PX resources and other popular bioinformatics resources, novel research and also new data resources. Finally, we summarise the current state-of-the-art in data management practices for sensitive human (clinical) proteomics data.

Meta-Analysis of Rice Phosphoproteomics Data to Understand Variation in Cell Signaling Across the Rice Pan-Genome.

Phosphorylation is the most studied post-translational modification, and has multiple biological functions. In this study, we have reanalyzed publicly available mass spectrometry proteomics data sets enriched for phosphopeptides from Asian rice (Oryza sativa). In total we identified 15,565 phosphosites on serine, threonine, and tyrosine residues on rice proteins. We identified sequence motifs for phosphosites, and link motifs to enrichment of different biological processes, indicating different downstream regulation likely caused by different kinase groups. We cross-referenced phosphosites against the rice 3,000 genomes, to identify single amino acid variations (SAAVs) within or proximal to phosphosites that could cause loss of a site in a given rice variety and clustered the data to identify groups of sites with similar patterns across rice family groups. The data has been loaded into UniProt Knowledge-Base─enabling researchers to visualize sites alongside other data on rice proteins, e.g., structural models from AlphaFold2, PeptideAtlas, and the PRIDE database─enabling visualization of source evidence, including scores and supporting mass spectra.

Detection of the Arabidopsis Proteome and Its Post-translational Modifications and the Nature of the Unobserved (Dark) Proteome in PeptideAtlas.

This study describes a new release of the Arabidopsis thaliana PeptideAtlas proteomics resource (build 2023-10) providing protein sequence coverage, matched mass spectrometry (MS) spectra, selected post-translational modifications (PTMs), and metadata. 70 million MS/MS spectra were matched to the Araport11 annotation, identifying ∼0.6 million unique peptides and 18,267 proteins at the highest confidence level and 3396 lower confidence proteins, together representing 78.6% of the predicted proteome. Additional identified proteins not predicted in Araport11 should be considered for the next Arabidopsis genome annotation. This release identified 5198 phosphorylated proteins, 668 ubiquitinated proteins, 3050 N-terminally acetylated proteins, and 864 lysine-acetylated proteins and mapped their PTM sites. MS support was lacking for 21.4% (5896 proteins) of the predicted Araport11 proteome: the "dark" proteome. This dark proteome is highly enriched for E3 ligases, transcription factors, and for certain (e.g., CLE, IDA, PSY) but not other (e.g., THIONIN, CAP) signaling peptides families. A machine learning model trained on RNA expression data and protein properties predicts the probability that proteins will be detected. The model aids in discovery of proteins with short half-life (e.g., SIG1,3 and ERF-VII TFs) and for developing strategies to identify the missing proteins. PeptideAtlas is linked to TAIR, tracks in JBrowse, and several other community proteomics resources.

Universal Spectrum Identifier for mass spectra.

Mass spectra provide the ultimate evidence to support the findings of mass spectrometry proteomics studies in publications, and it is therefore crucial to be able to trace the conclusions back to the spectra. The Universal Spectrum Identifier (USI) provides a standardized mechanism for encoding a virtual path to any mass spectrum contained in datasets deposited to public proteomics repositories. USI enables greater transparency of spectral evidence, with more than 1 billion USI identifications from over 3 billion spectra already available through ProteomeXchange repositories.

NKAIN1, as an oncogene, promotes the proliferation and metastasis of breast cancer, affecting its prognosis.

Na, K-ATPase interaction (NKAIN) is a transmembrane protein family, which can interact with Na, K-ATPase ß1 subunit. NKAIN1 plays an important role in alcohol-dependent diseases such as endometrial and prostate cancers. However, the relationship between NKAIN1 and human breast cancer has not been studied. Hence, this study aimed to explore the relationship between NKAIN1 expression and breast cancer. Data used in this study were mainly from the Cancer Genome Atlas, including differential expression analysis, Kaplan-Meier survival analysis, receiver operating characteristic curve analysis, multiple Cox regression analysis, co-expression gene analysis, and gene set enrichment analysis. Analyses were performed using reverse transcription-quantitative polymerase chain reaction, western blot analysis, and immunohistochemistry on 46 collected samples. The knockdown or overexpression of NKAIN1 in vitro in MCF-7 and MDA-MB-231 cell lines altered the proliferation and migration abilities of tumor cells. In vivo experiments further confirmed that NKAIN1 knockdown effectively inhibited the proliferation and migration of cancer cells. Therefore, our study identified NKAIN1 as an oncogene that is highly expressed in breast cancer tissues. The findings highlight the potential of NKAIN1 as a molecular biomarker of breast cancer.

The Arabidopsis PeptideAtlas: Harnessing worldwide proteomics data to create a comprehensive community proteomics resource.

We developed a resource, the Arabidopsis PeptideAtlas (www.peptideatlas.org/builds/arabidopsis/), to solve central questions about the Arabidopsis thaliana proteome, such as the significance of protein splice forms and post-translational modifications (PTMs), or simply to obtain reliable information about specific proteins. PeptideAtlas is based on published mass spectrometry (MS) data collected through ProteomeXchange and reanalyzed through a uniform processing and metadata annotation pipeline. All matched MS-derived peptide data are linked to spectral, technical, and biological metadata. Nearly 40 million out of ∼143 million MS/MS (tandem MS) spectra were matched to the reference genome Araport11, identifying ∼0.5 million unique peptides and 17,858 uniquely identified proteins (only isoform per gene) at the highest confidence level (false discovery rate 0.0004; 2 non-nested peptides ≥9 amino acid each), assigned canonical proteins, and 3,543 lower-confidence proteins. Physicochemical protein properties were evaluated for targeted identification of unobserved proteins. Additional proteins and isoforms currently not in Araport11 were identified that were generated from pseudogenes, alternative start, stops, and/or splice variants, and small Open Reading Frames; these features should be considered when updating the Arabidopsis genome. Phosphorylation can be inspected through a sophisticated PTM viewer. PeptideAtlas is integrated with community resources including TAIR, tracks in JBrowse, PPDB, and UniProtKB. Subsequent PeptideAtlas builds will incorporate millions more MS/MS data.

Broadband lens and graphene/silicon photodiode for wide spectral imaging.

We designed a broadband lens along with a graphene/silicon photodiode for wide spectral imaging ranging from ultraviolet to near-infrared wavelengths. By using five spherical glass lenses, the broadband lens, with the modulation transfer function of 0.38 at 100 lp/mm, corrects aberrations ranging from 340 to 1700 nm. Our design also includes a broadband graphene/silicon Schottky photodiode with the highest responsivity of 0.63 A/W ranging from ultraviolet to near-infrared. By using the proposed broadband lens and the broadband graphene/silicon photodiode, several single-pixel imaging designs in ultraviolet, visible, and near-infrared wavelengths are demonstrated. Experimental results show the advantages of integrating the lens with the photodiode and the potential to realize broadband imaging with a single set of lens and a detector.

miR-20a: a key regulator of orthodontic tooth movement via BMP2 signaling pathway modulation.

AIM: In this study, we aimed to establish a rat tooth movement model to assess miR-20's ability in enhancing the BMP2 signaling pathway and facilitate alveolar bone remodeling. METHOD: 60 male SD rats had nickel titanium spring devices placed between their left upper first molars and incisors, with the right side serving as the control. Forces were applied at varying durations (18h, 24h, 30h, 36h, 42h, 1d, 3d, 5d, 7d, 14d), and their bilateral maxillary molars and surrounding alveolar bones were retrieved for analysis. Fluorescent quantitative PCR was conducted to assess miR-20a, BMP2, Runx2, Bambi and Smad6 gene expression in alveolar bone, and western blot was performed to determine the protein levels of BMP2, Runx2, Bambi, and Smad6 after mechanical loading. RESULT: We successfully established an orthodontic tooth movement model in SD rats and revealed upregulated miR-20a expression and significantly increased BMP2 and Runx2 gene expression and protein synthesis in alveolar bone during molar tooth movement. Although Bambi and Smad6 gene expression did not significantly increase, their protein synthesis was found to decrease significantly. CONCLUSION: MiR-20a was found to be involved in rat tooth movement model alveolar bone remodeling, wherein it promoted remodeling by reducing Bambi and Smad6 protein synthesis through the BMP2 signaling pathway.

Pelvic floor muscle strength and influencing factors based on vaginal manometry among healthy women at different life stages: A multicentre cross-sectional study.

OBJECTIVE: To assess pelvic floor muscle (PFM) strength and influencing factors among healthy women at different life stages. DESIGN: Multicentre cross-sectional study. SETTING: Fourteen hospitals in China. POPULATION: A total of 5040 healthy women allocated to the following groups (with 1680 women per group): premenopausal nulliparous, premenopausal parous and postmenopausal. METHODS: The PFM strength was evaluated by vaginal manometry. Multivariate logistic regression was used to determine the influencing factors for low PFM strength. MAIN OUTCOME MEASURES: Maximum voluntary contraction pressure (MVCP). RESULTS: The median MVCP values were 36, 35 and 35 cmH2O in premenopausal nulliparous (aged 19-51 years), premenopausal parous (aged 22-61 years), and postmenopausal (aged 40-86 years) women, respectively. In the premenopausal nulliparous group, physical work (odds ratio, OR 2.05) was the risk factor for low PFM strength, which may be related to the chronic increased abdominal pressure caused by physical work. In the premenopausal parous group, the number of vaginal deliveries (OR 1.28) and diabetes (OR 2.70) were risk factors for low PFM strength, whereas sexual intercourse (<2 times per week vs. none, OR 0.55; ≥2 times per week vs. none, OR 0.56) and PFM exercise (OR 0.50) may have protective effects. In the postmenopausal group, the number of vaginal deliveries (OR 1.32) and family history of pelvic organ prolapse (POP) (OR 1.83) were risk factors for low PFM strength. CONCLUSIONS: Physical work, vaginal delivery, diabetes and a family history of POP are all risk factors for low PFM strength, whereas PFM exercises and sexual life can have a protective effect. The importance of these factors varies at different stages of a woman's life.

Analyzing the correlation between quinolone-resistant Escherichia coli resistance rates and climate factors: A comprehensive analysis across 31 Chinese provinces.

BACKGROUND: The increasing problem of bacterial resistance, particularly with quinolone-resistant Escherichia coli (QnR eco) poses a serious global health issue. METHODS: We collected data on QnR eco resistance rates and detection frequencies from 2014 to 2021 via the China Antimicrobial Resistance Surveillance System, complemented by meteorological and socioeconomic data from the China Statistical Yearbook and the China Meteorological Data Service Centre (CMDC). Comprehensive nonparametric testing and multivariate regression models were used in the analysis. RESULT: Our analysis revealed significant regional differences in QnR eco resistance and detection rates across China. Along the Hu Huanyong Line, resistance rates varied markedly: 49.35 in the northwest, 54.40 on the line, and 52.30 in the southeast (P = 0.001). Detection rates also showed significant geographical variation, with notable differences between regions (P < 0.001). Climate types influenced these rates, with significant variability observed across different climates (P < 0.001). Our predictive model for resistance rates, integrating climate and healthcare factors, explained 64.1% of the variance (adjusted R-squared = 0.641). For detection rates, the model accounted for 19.2% of the variance, highlighting the impact of environmental and healthcare influences. CONCLUSION: The study found higher resistance rates in warmer, monsoon climates and areas with more public health facilities, but lower rates in cooler, mountainous, or continental climates with more rainfall. This highlights the strong impact of climate on antibiotic resistance. Meanwhile, the predictive model effectively forecasts these resistance rates using China's diverse climate data. This is crucial for public health strategies and helps policymakers and healthcare practitioners tailor their approaches to antibiotic resistance based on local environmental conditions. These insights emphasize the importance of considering regional climates in managing antibiotic resistance.

CTLA-4 blockade induces tumor pyroptosis via CD8+ T cells in head and neck squamous cell carcinoma.

Immune checkpoint blockade (ICB) treatment has demonstrated excellent medical effects in oncology, and it is one of the most sought after immunotherapies for tumors. However, there are several issues with ICB therapy, including low response rates and a lack of effective efficacy predictors. Gasdermin-mediated pyroptosis is a typical inflammatory death mode. We discovered that increased expression of gasdermin protein was linked to a favorable tumor immune microenvironment and prognosis in head and neck squamous cell carcinoma (HNSCC). We used the mouse HNSCC cell lines 4MOSC1 (responsive to CTLA-4 blockade) and 4MOSC2 (resistant to CTLA-4 blockade) orthotopic models and demonstrated that CTLA-4 blockade treatment induced gasdermin-mediated pyroptosis of tumor cells, and gasdermin expression positively correlated to the effectiveness of CTLA-4 blockade treatment. We found that CTLA-4 blockade activated CD8+ T cells and increased the levels of interferon γ (IFN-γ) and tumor necrosis factor α (TNF-α) cytokines in the tumor microenvironment. These cytokines synergistically activated the STAT1/IRF1 axis to trigger tumor cell pyroptosis and the release of large amounts of inflammatory substances and chemokines. Collectively, our findings revealed that CTLA-4 blockade triggered tumor cells pyroptosis via the release of IFN-γ and TNF-α from activated CD8+ T cells, providing a new perspective of ICB.

Renal Metabolomics Study and Critical Pathway Validation of Shenkang Injection in the Treatment of Chronic Renal Failure.

To reveal the mechanism of Shenkang injection (SKI) in the treatment of chronic renal failure, and verify the key pathway. In this work, an untargeted metabolomics approach was performed by LC-MS coupled with multivariate statistical analysis to provide new insights into therapeutic mechanism of SKI. Hematoxylin-eosin (H&E) Staining and Immunohistochemistry were used to evaluate the effects of drug treatment, Western blot was used to verify the critical pathway. Then, a total of 44 potential biomarkers of chronic renal failure (CRF) were identified and reversed regulation, including 2,8-dihydroxypurine, 5-methoxytryptophan, uric acid, acetylcarnitine, taurine, etc. Mainly concerned with arginine and proline metabolism, purine metabolism, histidine metabolism, etc. Pathological examination showed that the renal interstitium of SKI group was significantly improved, with fewer inflammatory cells and thinner vascular walls compared with the model group. Immunohistochemical results showed that the expression of α-smooth muscle actin (α-SMA) was decreased, and the expression of E-cadherin was increased in CRF model group, and the two indicators were reversed regulation in SKI injection, indicating that the degree of fibrosis was relieved. Critical signaling pathway phosphatidylinositol 3-kinase (PI3K)/protein kinase B (Akt) and nuclear factor-kappaB (NF-κB) protein expressions were significantly inhibited. This study was the first to employ metabolomics to elucidate the underlying mechanisms of SKI in chronic renal failure. The results would provide some support for clinical application of traditional Chinese medicines in clinic.

CT-based radiomics analysis of different machine learning models for differentiating gnathic fibrous dysplasia and ossifying fibroma.

OBJECTIVE: In this study, our aim was to develop and validate the effectiveness of diverse radiomic models for distinguishing between gnathic fibrous dysplasia (FD) and ossifying fibroma (OF) before surgery. MATERIALS AND METHODS: We enrolled 220 patients with confirmed FD or OF. We extracted radiomic features from nonenhanced CT images. Following dimensionality reduction and feature selection, we constructed radiomic models using logistic regression, support vector machine, random forest, light gradient boosting machine, and eXtreme gradient boosting. We then identified the best radiomic model using receiver operating characteristic (ROC) curve analysis. After combining radiomics features with clinical features, we developed a comprehensive model. ROC curve and decision curve analysis (DCA) demonstrated the models' robustness and clinical value. RESULTS: We extracted 1834 radiomic features from CT images, reduced them to eight valuable features, and achieved high predictive efficiency, with area under curves (AUC) exceeding 0.95 for all the models. Ultimately, our combined model, which integrates radiomic and clinical data, displayed superior discriminatory ability (AUC: training cohort 0.970; test cohort 0.967). DCA highlighted its optimal clinical efficacy. CONCLUSION: Our combined model effectively differentiates between FD and OF, offering a noninvasive and efficient approach to clinical decision-making.

Development and validation of a nomogram model for all-cause mortality risk in patients with chronic heart failure and atrial fibrillation.

BACKGROUND: As the global aging process continues to accelerate, heart failure (HF) has become an important cause of increased morbidity and mortality in elderly patients. Chronic atrial fibrillation (AF) is a major risk factor for HF. Patients with HF combined with AF are more difficult to treat and have a worse prognosis. The aim of this study was to explore the risk factors for 1-year mortality in patients with HF combined with AF and to develop a risk prediction assessment model. METHODS: We recruited hospitalized patients with HF and AF who received standardized care in the Department of Cardiology at Shengjing Hospital of China Medical University from January 2013 to December 2018. The patients were randomly divided into modeling and internal validation groups using a random number generator at a 1:1 ratio. Multivariate Cox regression analysis was used to identify risk factors for all-cause mortality during a one-year follow-up period. Then, a nomogram was constructed based on the weights of each index and validated. Receiver operating characteristic curve, the area under the curve (AUC), decision curve, and calibration curve analyses for survival were used to evaluate the model's predictive and clinical validities and calibration. RESULTS: We included 3,406 patients who met the eligibility criteria; 1,703 cases each were included in the modeling and internal validation groups. Eight statistically significant predictors were identified: age, sex, New York Heart Association cardiac function class III or IV, a history of myocardial infarction, and the albumin, triglycerides, N-terminal pro-b-type natriuretic peptide, and blood urea nitrogen levels. The AUCs were 0.793 (95% confidence interval: 0.763-0.823) and 0.794 (95% confidence interval: 0.763-0.823) in the modeling and validation cohorts, respectively. CONCLUSIONS: We present a predictive model for all-cause mortality in patients with coexisting HF and AF comprising eight key factors. This model gives clinicians a simple assessment tool that may improve the clinical management of these patients.

Synthetic robust perfect adaptation achieved by negative feedback coupling with linear weak positive feedback.

Unlike their natural counterparts, synthetic genetic circuits are usually fragile in the face of environmental perturbations and genetic mutations. Several theoretical robust genetic circuits have been designed, but their performance under real-world conditions has not yet been carefully evaluated. Here, we designed and synthesized a new robust perfect adaptation circuit composed of two-node negative feedback coupling with linear positive feedback on the buffer node. As a key feature, the linear positive feedback was fine-tuned to evaluate its necessity. We found that the desired function was robustly achieved when genetic parameters were varied by systematically perturbing all interacting parts within the topology, and the necessity of the completeness of the topological structures was evaluated by destroying key circuit features. Furthermore, different environmental perturbances were imposed onto the circuit by changing growth rates, carbon metabolic strategies and even chassis cells, and the designed perfect adaptation function was still achieved under all conditions. The successful design of a robust perfect adaptation circuit indicated that the top-down design strategy is capable of predictably guiding bottom-up engineering for robust genetic circuits. This robust adaptation circuit could be integrated as a motif into more complex circuits to robustly implement more sophisticated and critical biological functions.