ABSTRACT
In preventive healthcare, the demand for systematic identification of wellness‐promoting natural compounds is rising. Quercetin, a phenolic compound found in various fruits and vegetables, is known for its antioxidant and anti‐inflammatory properties, improving lipid profiles and metabolic dysfunctions in conditions like Type 2 diabetes and NAFLD. This study applies a novel adaptation of an in‐silico drug repurposing methodology to quercetin, analyzing a gene expression signature library of over 800 diseases and 30 quercetin‐related conditions to prioritize molecular targets. Our findings revealed a strong computational link between quercetin and hypercholesterolemia. To validate this, we conducted a proof‐of‐concept clinical study using a high‐bioaccessibility quercetin formulation (QuercefitTM) in healthy adults with borderline metabolic profiles, confirming health benefits. This study highlights quercetin's known potential in managing hypercholesterolemia and demonstrates the power of computational methods in advancing natural compound discovery and repositioning. The integration of in‐silico predictions with human studies could pave the way for more precise or alternative use of bioactive compounds in dietary supplements.
Keywords: drug repurposing, flavonoid, high‐throughput method, lipid condition, metabolic condition, quercetin
Quercetin, a bioactive flavonoid abundant in fruits and vegetables, exhibits antioxidant and anti‐inflammatory effects with potential benefits in metabolic health. Using a high‐throughput in‐silico repurposing pipeline previously validated in pharmaceutical studies, 33 quercetin gene‐expression signatures were systematically compared against more than 800 disease transcriptomic profiles. Hypercholesterolemia emerged as the strongest inverse connection, supported by enrichment of pathways in lipid, bile acid, and xenobiotic metabolism, and key molecular drivers including APOE, FABP1, IGF1, and PPARα. To further showcase the computational findings, a proof‐of‐concept clinical study was conducted in metabolically challenged adults supplemented with a high‐bioaccessibility quercetin formulation (QuercefitTM). Over 90 days, participants demonstrated reductions in LDL and total cholesterol, increases in DHEA‐S, and improved quality of life without adverse effects. Together, these results underscore the alignment of computational prioritization with pilot evidence, highlighting quercetin's nutraceutical potential for lipid management and demonstrating the utility of integrative computational‐experimental approaches in natural compound repositioning.
1. Introduction
Quercetin is a powerful antioxidant and anti‐inflammatory natural compound demonstrating protective properties against free radical cell damage as well as balancing lipid levels through metabolic‐stimulating effects. Studies have shown that quercetin may confer benefits against cardiovascular disease, metabolic disorders, liver conditions, and certain types of cancers [1]. However, despite the documented beneficial effects on cellular health and various diseases primarily through experimental models (i.e., in‐vitro or animal studies) and limited human trials, the overall evidential basis for clinical applications of quercetin remains comparatively sparse.
In this study, we employed a novel application of a high‐throughput computational pipeline that has been previously validated [2] and applied in drug repurposing projects [3, 4] to scan a large library of over 800 transcriptomic disease signatures and explore the most promising candidate condition that can be targeted by quercetin. A set of 33 quercetin gene expression signatures was downloaded from the NIH LINCS Consortium database [5]. The pipeline's principal method is based on a modified connectivity mapping approach [5, 6], where each of the transcriptomic signatures of a compound is compared computationally with transcriptomic signatures of human diseases from a large library built from publicly available gene expression data [2, 3, 4, 5, 6, 7]. Utilizing metrics such as a connectivity score and adjusted p values of disease enrichment analysis, we found that unbalanced lipid‐related conditions (such as hypercholesterolemia and hyperlipidemia) were the strongest candidates implicated for quercetin. In addition, we employed the Leading Edge Enrichment Analysis for a deeper exploration of genes significantly reversed by quercetin that drive the connection to the lipid conditions. We found that quercetin driver genes such as APOE, FABP1, IGF1, IGFBP4, PPARα, and the SLC genes were implicated in xenobiotic processing as well as fatty acid or bile acid metabolic processes.
To explore the efficiency of our approach and the impact of quercetin on lipid regulation, we conducted a pilot, IRB‐approved study collecting data from five healthy but metabolically challenged individuals (i.e., LDL levels 130–159 mg/dL, and two or more of the following: low‐healthy fasting high‐density lipoprotein [HDL < 50 mg/dL]; high‐healthy fasting triglycerides [TGs 150–199 mg/dL]; high‐healthy fasting blood sugar [glucose 100–126 mg/dL]; and waist circumference >40 in for men or >35 in for women), with no known underlying pathologies. None of those five participants was following a medication or supplementation protocol. The participants were given 750 mg/day of Quercetin Phytosome (Quercefit(TM), Indena SpA) for 90 days. Quercetin Phytosome is a recent formulation of quercetin that has been proven to facilitate the attainment of high plasma levels of quercetin, up to 20 times more than usually obtained following the same dose of standard quercetin [8]. Results of the supplementation showed that, while maintaining lifestyle and dietary factors, lipid levels (such as LDL and total cholesterol) as well as DHEA‐S levels (a prognostic marker for CVD risk [9]) were modified toward a more favorable health status. Participants reported similar or better quality of life, and no side effects were reported. These promising results provided evidence for the successful application of the computational methodology in the nutraceutical field, showing that lipid conditions may be favorably modified by quercetin, and further strengthened previous results on its potency, when used in the high‐bioaccessibility form of QuercefitTM.
2. Methods
2.1. Computational Methodology
The details of the computational methodology and pipeline are described extensively previously [2, 3, 4]. For this application, a total of 33 gene expression signatures of quercetin (from 15 cell lines, 4 doses, and 2 time points) were downloaded from the LINCS1000 repository as level5 data (i.e., preprocessed and normalized). A PCA plot of all samples showed one outlier, which was removed from the analysis (Figure S1). The remaining 32 quercetin gene expression signatures were then compared computationally with 828 unique disease signatures of a transcriptomic library generated from publicly available gene expression data (Figure 1) [2, 7].
FIGURE 1.
Schematic of the in‐silico approach used to prioritize conditions targeted by quercetin. Quercetin signatures were obtained for 15 cell lines, 4 doses, and 2 time points. Each quercetin signature was compared to a library of disease signatures generated from publicly available data, and a connectivity score along with adjusted p values was generated for each disease‐quercetin signature pair. Filtering Top N inverse connections and secondary analyses were carried out, such as Disease Enrichment Analysis and Leading Edge Enrichment Analysis, to prioritize conditions and identify the strongest candidate for quercetin.
The computational method is based on a modified connectivity mapping approach [5, 6], where each of the transcriptomic signatures of quercetin is compared computationally with transcriptomic signatures of human diseases from the disease library. To identify indications for all quercetin signatures, a connectivity score and adjusted p values of significance were calculated by comparing each disease signature to each quercetin signature. The connectivity score aims to summarize the transcriptomic relationship between each quercetin signature and disease, such that a strongly negative score and associated adjusted p value indicate that the specific quercetin signature will induce transcriptomic changes that may revert or “normalize” the disease signature.
In more details, the quercetin cell‐line expression data (quercetin profiles or signatures) was used as a reference database. This reference database was then queried with each individual disease signature (from the library of 828 diseases) by applying a nonparametric, rank‐based pattern‐matching strategy based on the methodology originally introduced by Lamb et al. [6] in order to generate a ranked list of potential treatments for each of the diseases of interest. Given a disease signature, we evaluated its transcriptomic relationship to each of the quercetin expression profiles by computing an enrichment score for the upregulated and the downregulated disease genes with the following logic: if the upregulated disease genes appear near the top (upregulated) of the rank‐ordered quercetin gene expression list and the downregulated disease genes fall near the bottom (downregulated) of the rank‐ordered quercetin gene expression list, we can conclude that the quercetin and the disease expression profiles are similar, and thus quercetin might cause a change in tissue expression similar to having the disease. More interestingly, if the upregulated disease genes fall near the bottom of the rank‐ordered quercetin gene expression list and the downregulated disease genes are near the top of the rank‐ordered quercetin gene expression, then the quercetin and disease have complementary expression profiles and quercetin might be a possible treatment option for the disease of interest.
The resulting “connectivity scores” were used as quercetin‐disease scores (QDSs). In order to present the results in a more readable fashion, the scores were averaged by quercetin dose and cell line. Finally, the list of quercetin profiles was ranked for each disease from the most strongly, inversely connected to most strongly, positively connected, according to the computed similarity QDS creating our final dataset. The statistical significance of a given QDS is estimated by generating a null QDS distribution for a given QDS against 1000 randomly permuted compound signatures. Permuted compound signatures were generated by randomizing the ranked gene expression fold change for each permutation and used to derive two‐tailed p values, which were adjusted by the Benjamini–Hochberg method of controlling the false discovery rate (FDR). The cutoff for significance in the compound‐disease scores for each disease signature was set at an FDR of 5% (Supporting Information File S1).
2.2. Disease Enrichment Analysis
The disease enrichment analysis aims to identify high‐level disease categories enriched within the significant connections with quercetin and gives additional information on the overrepresentation of a particular connection within the library.
In more details, for each quercetin signature, the full list of 828 disease signatures was rank ordered according to ascending connectivity score calculated as described above. For each disease signature in the disease signature library, the relevant Disease Ontology Identifier (DOID) was collated, which E11 reflects the disease concept classification that best represents a given signature. For each DOID that has at least three corresponding disease signatures, a signed running sum enrichment score was calculated, which reflects whether that DOID is overrepresented at the extreme ends of the ranked disease list that has been ordered according to connectivity with a specific compound signature. DOID enrichments with a positive score indicate a disease with multiple individual disease signatures at the top of the ordered list, that is, diseases that are expected to be “normalized” by a given compound. The statistical significance of DEA scores was based on comparison to a distribution of 1000 permuted null scores, generated by calculating scores from randomized DOID sets that contain an equivalent number of disease signatures to the true set being evaluated. Finally, the raw p values were adjusted using the Benjamini–Hochberg method of controlling the FDR (Supporting Information File S1).
2.3. Prioritization of Disease Connections for Quercetin
Collectively, the following metrics were used for prioritization of diseases and conditions for quercetin: Top 30 inverse connections with quercetin (negative connectivity score) ranked by significance of connection (FDR) (see Inverse Significant Hits, Supporting Information File S1); Top 10 inverse connections ranked by significance of connection (FDR) and by number of significantly connected quercetin signatures (see Multi Conditions, Supporting Information File S1); Top 10 enriched quercetin‐disease connections in the entire library ranked by FDR < 0.2 (see Disease Enrichment Analysis, Supporting Information File S1). Further analysis exploring overrepresented gene sets and pathways that underpin the biological drivers of the above connections was performed with the Leading Edge Enrichment Analysis (Supporting Information File S2).
2.4. Validation Study and Data Collection
A total of five healthy individuals (Table 1) who self‐reported high‐normal LDL levels (130–159 mg/dL) and at least two out of four additional metabolic health markers in the normal but borderline range (HDL < 50 mg/dL, TG 150–199 mg/dL, glucose 100–126 mg/dL, waist circumference > 40 in for men and > 35 in for women) were recruited and provided written consent (WIRB Protocol #20220027; Clinicaltrials.gov number: NCT05297032).
TABLE 1.
Participant characteristics in the pilot clinical study of QuercefitTM supplementation.
| Study ID | Age | Sex | BMI | Race |
|---|---|---|---|---|
| P1 | 49 | F | 27.5 | White |
| P2 | 35 | F | 27.1 | White |
| P3 | 62 | M | 21.8 | White |
| P4 | 30 | M | 29.5 | American Indian/Alaskan Native |
| P5 | 47 | F | 30.5 | White |
After initial screening, interview with a study physician, consent and enrollment, each volunteer provided two fasted blood samples one before and one after 90‐day daily supplementation of three capsules of QuercefitTM (750 mg/day; supplied by Thorne HealthTech, Summerville, SC, USA). In addition, each volunteer completed bi‐weekly online questionnaires during the study period.
The blood tests included a comprehensive metabolic panel (CMP), complete blood count with differential (CBC w/diff), lipid panel, glycosylated hemoglobin, DHEA‐S, as well as apolipoprotein B and lipoprotein fractionation, and ion mobility tests. Blood was drawn via standard venipuncture following a 12‐h fast and analyzed at each participant's local Quest Lab. The questionnaires were designed to assess lifestyle trends to ensure study compliance and lifestyle habits maintenance (such as exercise, stress, sleep, diet, etc., Supporting Information File S3) and to also monitor quality of life [10] and compliance with supplementation. Trends and stats for the questionnaire scores and blood markers were calculated and plotted using R Studio version 4.2.1 (2022‐06‐23).
3. Results
3.1. The Computational Pipeline Identifies Strong Connections Between Quercetin and Lipid Conditions
The details of the computational pipeline have been described earlier, and this approach has been successfully applied to other drug repurposing projects [2, 3, 4]. In this work, we applied a disease‐agnostic approach of the pipeline to explore meaningful connections between diverse disease conditions and quercetin. A set of 32 quercetin gene expression signatures (after one outlier signature removal) was compared computationally with a total of 828 unique disease gene expression signatures from publicly available data in a precomputed library (Figure 1).
The pipeline provided all conditions connected with quercetin. Within the Top 30 most significantly, inversely connected conditions (adjusted p value < 0.2, Figure 2A and Supporting Information File S1) we identified connectivity against signatures linked with liver, lipid, and metabolic health such as NASH, hypercholesterolemia, and Type 2 diabetes were found among the Top 30 most significant (adjusted p value < 0.2, Figure 2A and Supporting Information File S1). More specifically, hypercholesterolemia was connected significantly and inversely with 10 out of the 32 quercetin experimental conditions (Figure 2B and Supporting Information File S1). The disease enrichment analysis provided further evidence that conditions affecting lipid levels are globally relevant to quercetin by ranking hypercholesterolemia with the highest enrichment score across all significantly connected conditions with quercetin (Figure 2C and Supporting Information File S1). To examine the molecular networks that mediate the significant connections between quercetin and hypercholesterolemia, we performed a Leading Edge Enrichment Analysis. Among the genes that drove the connections, we identified enrichments for xenobiotic metabolism, detoxification, and other metabolic pathways such as fatty acid and bile acid metabolism (most significant pathways and leading edge genes of the Top 3 pathways, in Figure 2D, E and Supporting Information File S2).
FIGURE 2.
(A) Table of the Top 30 most significant inverse disease connections with quercetin signatures. A connectivity score that represents the strength of connection and an adjusted p value that represents significance are given per disease‐quercetin connection. Liver, lipid, and metabolic conditions such as NASH, hypercholesterolemia/hyperlipidemia, and Type 2 diabetes were among the most significant. (B) Table of significant inverse disease connections with quercetin signatures ranked by the number of significantly connected quercetin signatures. A higher number shows that a condition is more globally connected with different quercetin signatures. Hypercholesterolemia was connected to 10 quercetin signatures. (C) Disease Enrichment Analysis results. Hypercholesterolemia was the most significantly connected disease theme with quercetin across the entire library. (D, E) Leading Edge Enrichment Analysis results. Top enriched pathways (D) and leading genes (E) that establish the molecular connection of quercetin to hypercholesterolemia were associated with fatty acid, bile acid, and xenobiotic metabolism. Important molecular drivers implicated in lipid regulation are highlighted in dark yellow (E).
The summary tables and visualization of the results from the computational pipeline that were used for prioritizing liver, lipid, and metabolic conditions, and more specifically, hypercholesterolemia, as the strongest candidate for quercetin, are summarized in Figure 2.
3.2. QuercefitTM Improves Lipid Levels and Quality of Life in a Study of Metabolically Challenged Individuals
The results of the pilot trial showed that 90 days of QuercefitTM supplementation at the highest recommended daily dosage were able to lower LDL, total cholesterol, and total to HDL cholesterol levels in three out of the five individuals, and increased DHEA‐S in four out of five individuals who participated in the study. Although no differences were found at a significant level due to the limited population size (n = 5), a paired Wilcoxon rank‐sum test was performed to compare mean differences before and after supplementation in all blood markers (Figure S2 and Supporting Information File S3).
Finally, the quality‐of‐life metric remained at similar or ameliorated levels with QuercefitTM supplementation in four out of five participants. In all participants, the trend showed an overall quality of life improvement albeit at a p value greater than 0.05 comparing “End” to “Baseline” scores (paired Wilcoxon rank‐sum test, Figure 3B and Supporting Information File S3). No adverse events were reported, confirming the good safety and tolerability profile of QuercefitTM.
FIGURE 3.
(A) Lipid and hormonal changes after the 90‐day QuercefitTM supplementation in the pilot study. Improvements in low‐density lipoprotein (LDL), total cholesterol, and total‐to‐high‐density lipoprotein (HDL) cholesterol were observed in four out of five participants. An increase in DHEA‐S was also observed in four out of five participants (in bold are the markers of noteworthy changes). (B) The quality‐of‐life metric trend during the 90‐day QuercefitTM supplementation in the pilot study. Although some fluctuations occurred in some individuals during the study, comparing baseline with end, the quality of life stayed the same or improved in four out of five participants (for detailed data trends and p values, see Supporting Information File S3 and Figure S2).
4. Discussion
Previous studies have demonstrated a prominent role of quercetin against cardiovascular, liver, lipid, and metabolic diseases, supporting quercetin's strong physiological activity in reducing oxidative damage, inhibiting low‐density lipoprotein oxidation and platelet aggregation [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]. However, quercetin has not yet been evaluated in a systematic way for prioritizing its targeted diseases at the transcriptomic level. To overcome this limitation, we applied a previously validated drug‐repurposing methodology [2, 3, 4] on a flavanol compound and assessed 32 gene expression signatures of quercetin against 828 unique gene expression signatures of diseases from a precomputed library of publicly available data [2, 3, 4, 5, 6, 7]. Collectively, the results of the computational method showed that lipid conditions, and more specifically, hypercholesterolemia, were the strongest scientific wellness target for quercetin.
The effects of quercetin on lipid modulation have been previously documented in a few human clinical trials [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] and in experimental in‐vitro and animal models. Quercetin has been shown to protect against dyslipidemia in rats [13], lower lipid levels in mice [14], ameliorate lipopolysaccharide‐induced inflammation and oxidative stress in zebrafish [15], and attenuate lipid accumulation in muscle cells [16]. Similar beneficial effects of quercetin have also been demonstrated in other conditions such as Type 2 diabetes [17], metabolic syndrome [18], obesity [19], hepatological diseases such as NAFLD [20], allergic immune reactions such as asthma [21, 22], and neurological and cognitive conditions such as Alzheimer's disease [23] or bipolar disorder [24].
Consistent with the literature, the prioritization methodology used in the current work showed significant connections of quercetin with conditions related to metabolic imbalances (e.g., Type 2 diabetes, hyperlipidemia, and hypercholesterolemia), liver dysfunction (e.g., NASH, alcoholic hepatitis), allergic reactions (e.g., asthma), and cognitive decline (e.g., dementia, bipolar disorder). Nonetheless, the most consistent disease theme for quercetin across all types of analyses pointed toward lipid‐associated conditions and hypercholesterolemia. A deeper analysis of the genes driving the strong connection of quercetin against hypercholesterolemia revealed enriched pathways in xenobiotic metabolism possibly related to liver detoxification (previously shown in mice [25]); bile acid and fatty acid metabolism (evidence for direct effect previously shown in rats [26]); and adipogenesis and energy related signaling such as mTOR and oxidative phosphorylation (pathways shown to be modulated by quercetin in various experimental models [16, [27, 28, 29, 30]).
Investigating further the genes significantly reversed by quercetin and enriched within the three most significant pathways discovered with the computational approach, a more prominent role of quercetin on affecting fatty acid metabolism and lipid regulation was revealed. Quercetin significantly upregulated: the gene for the major catabolic protein of TG‐rich lipoprotein constituents APOE; the regulator for acid uptake, transport, and metabolism FABP1; the gluconeogenesis regulatory enzyme FBP1; the cellular respiration enzyme responsible for detoxification and lipogenesis in hepatic cells, IDH1; and signaling molecules for lipid metabolic downstream pathways such as IGF1, IGFBP4, and PPARα including SLC glucose transporter genes. Quercetin also downregulated LDHA that encodes for an enzyme involved in anaerobic glycolysis, but found elevated in pancreatic among other types of cancer. Some of those genes have been shown to be targeted by quercetin in animal and experimental models [31, 32, 33]. In particular, the activation of PPARα with subsequent upregulation of SLC27, or long chain fatty acid transporters, which stimulate bile salt production via bile acyl‐CoA synthetase, has been discussed previously as a key mechanism of the lipid lowering effects for Vitamin E or other nutraceuticals via hepatic, intestinal cholesterol level regulation, or increased bile salt formation [34].
To assess those findings and provide further proof of concept on the strong molecular connection of quercetin to lipid modulation, a validation study followed. Despite the small number of recruited participants—mainly due to restrictions and technical difficulties encountered during the COVID pandemic—the study showed promising results. Healthy volunteers with borderline lipid levels or other metabolic imbalances, who were not taking any medication or lipid‐lowering supplements, consumed 750 mg/day of Quercetin Phytosome (QuercefitTM) for 90 days. Blood tests were done before and after the 90‐day QuercefitTM supplementation to assess changes in lipids or other markers. Although maintaining lifestyle habits (diet, sleep, stress, and exercise), most participants showed improvement of TGs, LDL, and total cholesterol as well as their quality of life. Due to the small size of this pilot study, it is hard to generalize any conclusions, but the amelioration in lipid levels provides additional value to the promising results of clinical trials already conducted at a larger population level. Therefore, although at preliminary yet strongly encouraging stages, these results demonstrate the powerful effect of quercetin in its high bioaccessibility form (i.e., Phytosome technology) toward favorable lipid modulation in healthy individuals. They further support the value of prioritization in the computationally driven disease connections and present quercetin as one of the most promising natural compounds to target lipid‐related metabolic conditions.
5. Conclusions
This study highlights the power of computational predictions in identifying promising therapeutic targets for natural compounds like quercetin. By leveraging advanced in‐silico methodologies, we were able to prioritize lipid‐related metabolic conditions, particularly hypercholesterolemia, as key areas where quercetin may exert its beneficial effects. The alignment of these computational predictions with both existing literature and preliminary clinical outcomes demonstrates the effectiveness of this approach in guiding targeted research and clinical exploration. This work sets a precedent for the application of computational tools in the systematic evaluation and prioritization of natural compounds for specific health conditions.
Ethics Statement
This study was performed under IRB protocol regulations (WIRB Protocol #20220027; Clinicaltrials.gov number: NCT05297032).
Consent
All participants provided written consent.
Conflicts of Interest
The authors declare no conflicts of interest.
Supporting information
Supporting Information: Figure S1. A PCA plot of the 33 quercetin gene expression signatures in various cell lines, various dosages, and timepoints. The NPC_10μμ_24hr signature was removed from further analysis as an outlier.
Supporting Information: Figure S2. Box and whisker plots of all blood metrics measured in participants of the pilot study, before and after the 90‐day QuercefitTM supplementation.
Supporting File 3: mnfr70253‐sup‐0003.xlsx
Supporting File 4: mnfr70253‐sup‐0004.xlsx
Supporting File 5: mnfr70253‐sup‐0005.xlsx
Lili L., Smith S., Kunces L., et al. “An Application of a Large‐Scale, Data‐Driven Approach to Prioritize Compound‐Targeted Conditions in the Nutraceutical Space: Case Study of QuercefitTM (Quercetin Phytosome) Toward Unbalanced Lipid Conditions in Metabolically Challenged Adults.” Molecular Nutrition & Food Research 69, no. 22 (2025): e70253. 10.1002/mnfr.70253
Stephen Phipps and Bodi Zhang contributed equally to this study.
Funding: This study included authors funded by Indena SpA and Thorne HealthTech, Inc. The URL's to sponsors’ websites for the ingredient and the formulation/supply of the ingredient: https://www.indena.com/us/# and https://www.thorne.com/.
Data Availability Statement
Data and methods can be found in the Supporting Information. Further information regarding the data and methods is available from the corresponding author on reasonable request.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supporting Information: Figure S1. A PCA plot of the 33 quercetin gene expression signatures in various cell lines, various dosages, and timepoints. The NPC_10μμ_24hr signature was removed from further analysis as an outlier.
Supporting Information: Figure S2. Box and whisker plots of all blood metrics measured in participants of the pilot study, before and after the 90‐day QuercefitTM supplementation.
Supporting File 3: mnfr70253‐sup‐0003.xlsx
Supporting File 4: mnfr70253‐sup‐0004.xlsx
Supporting File 5: mnfr70253‐sup‐0005.xlsx
Data Availability Statement
Data and methods can be found in the Supporting Information. Further information regarding the data and methods is available from the corresponding author on reasonable request.