OBJECTIVE:Treatment response in type 2 diabetes (T2D) varies widely among individuals. This study aimed to quantify the contributions of clinical characteristics and genetic predisposition as measured through partitioned polygenic risk scores (pPRS) to variation in glycemic response to glucose-lowering therapies. RESEARCH DESIGN AND METHOD:We analysed data from two population-based cohorts: the Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) and the UK Biobank (UKBB). GoDARTS included 41 802 patients who initiated one of six major drug classes, of whom 11 615 had available genotype data. UKBB contributed 9371 individuals, including 8293 with genetic data. The primary outcome was glycaemic response, defined as the change in HbA1c 12 months after treatment initiation. Variables included demographic and clinical factors (age, sex, BMI, baseline HbA1c, kidney and liver function markers) and 14 pPRS representing T2D-related biological pathways. Linear regression models were fitted within each cohort and drug class (metformin, sulfonylureas, TZDs, DPP4i, SGLT2i, GLP-1RA), and effect estimates were combined using fixed-effect meta-analysis. RESULT:Baseline HbA1c was most strongly associated with glycemic response (p < 0.001). Older age was consistently associated with greater HbA1c reduction, while BMI and total cholesterol demonstrated drug-class-specific associations, with higher BMI associated with improved response to TZDs and higher total cholesterol generally associated with poorer glycaemic outcomes. Meta-analysis across GoDARTS and UK Biobank showed that higher overall T2D genetic risk was associated with greater HbA1c reduction with sulfonylureas (β = -0.46 mmol/mol, p = 0.013). Specific genetic profiles were also associated with drug responses, including β-cell function clusters with sulfonylureas (β = -0.57, p = 0.002), obesity-related variants with GLP-1RA (β = -1.49, p = 0.04), liver-lipid variants with SGLT2 inhibitors (β = -0.84, p = 0.05), and bilirubin pPRS with DPP-4 inhibitors (β = -0.69, p = 0.006). CONCLUSION:Both clinical and genetic factors significantly contribute to inter-individual variability in T2D drug response. Partitioned PRSs provide mechanistic insights into drug-specific pathways and have the potential to inform precision prescribing and optimise therapeutic outcomes in routine diabetes care.

BACKGROUND:Cardiomyocyte loss occurs in acute and chronic cardiac injury, including cardiotoxicity due to chemotherapeutics like doxorubicin, and contributes to heart failure development. There is a pressing need for cardiac-specific therapeutics that target cardiomyocyte loss, preventing chemotherapy complications without compromising chemotherapeutic efficacy. OBJECTIVES:The authors employed massively parallel combinatorial genetic screening to find microRNA (miRNA) combinations that promote cardiomyocyte survival. METHODS:CombiGEM (combinatorial genetics en masse) screening in a cardiomyocyte cell line was followed by validation in the original cell type and screening in primary cardiomyocytes. The top combination was tested in mouse and developing zebrafish models of doxorubicin cardiotoxicity. RNA sequencing provided insight into possible mechanisms. RESULTS:Multiple miRNA combinations protected cardiomyocytes from doxorubicin in vitro. The most effective (miR-222+miR-455) appeared to act synergistically, and mitigated doxorubicin cardiotoxicity phenotypes in murine and zebrafish in vivo models. RNA sequencing revealed overlapping and synergistic regulation of relevant genes and biological processes in cardiomyocytes, including mitochondrial homeostasis, oxidative stress, muscle contraction, and others. CONCLUSIONS:We identified miR-222 and miR-455 as a combination with potential therapeutic applications for cardioprotection. This study furthers our knowledge of the cardiac effects of miRNAs and their combinations and demonstrates the potential of CombiGEM for cardioprotective combinatorial therapeutic discovery.

Background Many cardiovascular diseases are associated with chronic kidney disease (CKD) and their burden as comorbidities is high. There is a pressing need to understand the molecular mechanisms by which cardiovascular diseases drive the development of CKD in multimorbidity. Methods and Results We performed an analysis of the risk of CKD onset across the full spectrum of cardiovascular disease phenotypes using UK Biobank primary and secondary care data (N = 129,824). We identified 17,347 cases of new onset CKD stage 3–5 within patients with pre-existing type 2 diabetes (T2D) (N = 13,409) and without diabetes (N = 116,415). We identified 8 cardiovascular disease phenotypes significantly associated with CKD onset including heart failure, atrial fibrillation, myocardial infarction, hypertension, ischaemic heart disease, peripheral vascular disease, hypotension, and angina pectoris. We performed a genome-wide association study (GWAS) to identify genetic variants enriched within identified high-risk CKD cardiovascular disease backgrounds and found 9 novel significant (p < 5 x 10-8) genetic loci associated with CKD in varying cardiovascular disease backgrounds. We used epigenomic databases and kidney eQTL data to link identified lead SNPs to likely target genes and found 1 novel CKD-associated gene in a background of hypertension with a clear SNP-to-gene assignment. We corroborated the identified gene-CKD association using single-cell RNA-seq kidney data and found the novel CKD-associated gene to have a highly specific cell-type expression within kidney podocytes. Conclusions We identified 9 novel CKD-associated loci that were enriched within high-risk-CKD, cardiovascular disease backgrounds. We identified a novel CKD-associated gene against a background of hypertension and found this gene to be highly enriched in podocytes in kidneys. This work has identified novel genetic and molecular factors associated with high-risk cardiovascular to CKD trajectories.

Peripartum cardiomyopathy (PPCM) is an idiopathic form of pregnancy-induced heart failure associated with preeclampsia. Circulating factors in late pregnancy are thought to contribute to both diseases, suggesting a common underlying pathophysiological process. However, what drives this process remains unclear. Using serum proteomics, we identified the senescence-associated secretory phenotype (SASP), a marker of cellular senescence associated with biological aging, as the most highly up-regulated pathway in young women with PPCM or preeclampsia. Placentas from women with preeclampsia displayed multiple markers of amplified senescence and tissue aging, as well as overall increased gene expression of 28 circulating proteins that contributed to SASP pathway enrichment in serum samples from patients with preeclampsia or PPCM. The most highly expressed placental SASP factor, activin A, was associated with cardiac dysfunction or heart failure severity in women with preeclampsia or PPCM. In a murine model of PPCM induced by cardiomyocyte-specific deletion of the gene encoding peroxisome proliferator–activated receptor γ coactivator-1α, inhibiting activin A signaling in the early postpartum period with a monoclonal antibody to the activin type II receptor improved heart function. In addition, attenuating placental senescence with the senolytic compound fisetin in late pregnancy improved cardiac function in these animals. These findings link senescence biology to cardiac dysfunction in pregnancy and help to elucidate the pathogenesis underlying cardiovascular diseases of pregnancy.

AbstractOsteoarthritis (OA) is increasing in prevalence and has a severe impact on patients’ lives. However, our understanding of biomarkers driving OA risk remains limited. We developed a model predicting the five-year risk of OA diagnosis, integrating retrospective clinical, lifestyle and biomarker data from the UK Biobank (19,120 patients with OA, ROC-AUC: 0.72, 95%CI (0.71–0.73)). Higher age, BMI and prescription of non-steroidal anti-inflammatory drugs contributed most to increased OA risk prediction ahead of diagnosis. We identified 14 subgroups of OA risk profiles. These subgroups were validated in an independent set of patients evaluating the 11-year OA risk, with 88% of patients being uniquely assigned to one of the 14 subgroups. Individual OA risk profiles were characterised by personalised biomarkers. Omics integration demonstrated the predictive importance of key OA genes and pathways (e.g., GDF5 and TGF-β signalling) and OA-specific biomarkers (e.g., CRTAC1 and COL9A1). In summary, this work identifies opportunities for personalised OA prevention and insights into its underlying pathogenesis.

The Foundational Data Initiative for Parkinson Disease (FOUNDIN-PD) is an international collaboration producing fundamental resources for Parkinson disease (PD). FOUNDIN-PD generated a multi-layered molecular dataset in a cohort of induced pluripotent stem cell (iPSC) lines differentiated to dopaminergic (DA) neurons, a major affected cell type in PD. The lines were derived from the Parkinson’s Progression Markers Initiative study, which included participants with PD carrying monogenic PD variants, variants with intermediate effects, and variants identified by genome-wide association studies and unaffected individuals. We generated genetic, epigenetic, regulatory, transcriptomic, and longitudinal cellular imaging data from iPSC-derived DA neurons to understand molecular relationships between disease-associated genetic variation and proximate molecular events. These data reveal that iPSC-derived DA neurons provide a valuable cellular context and foundational atlas for modeling PD genetic risk. We have integrated these data into a FOUNDIN-PD data browser as a resource for understanding the molecular pathogenesis of PD.

Background & Aims Non-alcoholic fatty liver disease (NAFLD) has a prevalence of ∼25% worldwide, with significant public health consequences yet few effective treatments. Human genetics can help elucidate novel biology and identify targets for new therapeutics. Genetic variants in mitochondrial amidoxime-reducing component 1 (MTARC1) have been associated with NAFLD and liver-related mortality; however, its pathophysiological role and the cell type(s) mediating these effects remain unclear. We aimed to investigate how MTARC1 exerts its effects on NAFLD by integrating human genetics with in vitro and in vivo studies of mARC1 knockdown. Methods Analyses including multi-trait colocalisation and Mendelian randomisation were used to assess the genetic associations of MTARC1. In addition, we established an in vitro long-term primary human hepatocyte model with metabolic readouts and used the Gubra Amylin NASH (GAN)-diet non-alcoholic steatohepatitis mouse model treated with hepatocyte-specific N-acetylgalactosamine (GalNAc)–siRNA to understand the in vivo impacts of MTARC1. Results We showed that genetic variants within the MTARC1 locus are associated with liver enzymes, liver fat, plasma lipids, and body composition, and these associations are attributable to the same causal variant (p.A165T, rs2642438 G>A), suggesting a shared mechanism. We demonstrated that increased MTARC1 mRNA had an adverse effect on these traits using Mendelian randomisation, implying therapeutic inhibition of mARC1 could be beneficial. In vitro mARC1 knockdown decreased lipid accumulation and increased triglyceride secretion, and in vivo GalNAc–siRNA-mediated knockdown of mARC1 lowered hepatic but increased plasma triglycerides. We found alterations in pathways regulating lipid metabolism and decreased secretion of 3-hydroxybutyrate upon mARC1 knockdown in vitro and in vivo. Conclusions Collectively, our findings from human genetics, and in vitro and in vivo hepatocyte-specific mARC1 knockdown support the potential efficacy of hepatocyte-specific targeting of mARC1 for treatment of NAFLD. Impact and implications We report that genetically predicted increases in MTARC1 mRNA associate with poor liver health. Furthermore, knockdown of mARC1 reduces hepatic steatosis in primary human hepatocytes and a murine NASH model. Together, these findings further underscore the therapeutic potential of targeting hepatocyte MTARC1 for NAFLD.

Background:Glioblastoma (GBM) is a highly aggressive and invasive brain tumor associated with high patient mortality. A large fraction of GBM tumors have been identified as epidermal growth factor receptor (EGFR) amplified and ~50% also are EGFRvIII mutant positive. In a previously reported multicenter phase II study, we have described the response of recurrent GBM (rGBM) patients to dacomitinib, an EGFR tyrosine kinase inhibitor (TKI). As a continuation of that report, we leverage the tumor cargo-encapsulating extracellular vesicles (EVs) and explore their genetic composition as carriers of tumor biomarker. Methods:Serum samples were longitudinally collected from EGFR-amplified rGBM patients who clinically benefitted from dacomitinib therapy (responders) and those who did not (nonresponders), as well as from a healthy cohort of individuals. The serum EV transcriptome was evaluated to map the RNA biotype distribution and distinguish GBM disease. Results:Using long RNA sequencing, we show enriched detection of over 10 000 coding RNAs from serum EVs. The EV transcriptome yielded a unique signature that facilitates differentiation of GBM patients from healthy donors. Further analysis revealed genetic enrichment that enables stratification of responders from nonresponders prior to dacomitinib treatment as well as following administration. Conclusion:This study demonstrates that genetic composition analysis of serum EVs may aid in therapeutic stratification to identify patients with dacomitinib-responsive GBM.

BackgroundFrequent digital monitoring of cognition is a promising approach for assessing endpoints in prevention and treatment trials of Alzheimer’s disease and related dementias (ADRD). This study evaluated the feasibility of the MIND GamePack© for recurrent semi-passive assessment of cognition across a longitudinal interval.MethodsThe MIND GamePack consists of four iPad-based games selected to be both familiar and enjoyable: Word Scramble, Block Drop, FreeCell, and Memory Match. Participants were asked to play 20 min/day for 5 days (100 min) for 4 months. Feasibility of use by older adults was assessed by measuring gameplay time and game performance. We also evaluated compliance through semi-structured surveys. A linear generalized estimating equation (GEE) model was used to analyze changes in gameplay time, and a regression tree model was employed to estimate the days it took for game performance to plateau. Subjective and environmental factors associated with gameplay time and performance were examined, including daily self-reported questions of memory and thinking ability, mood, sleep, energy, current location, and distractions prior to gameplay.ResultsTwenty-six cognitively-unimpaired older adults participated (mean age ± SD = 71.9 ± 8.6; 73% female). Gameplay time remained stable throughout the 4-months, with an average compliance rate of 91% ± 11% (1946 days of data across all participants) and weekly average playtime of 210 ± 132 min per participant. We observed an initial learning curve of improving game performance which on average, plateaued after 22–39 days, depending on the game. Higher levels of self-reported memory and thinking ability were associated with more gameplay time and sessions.ConclusionMIND GamePack is a feasible and well-designed semi-passive cognitive assessment platform which may provide complementary data to traditional neuropsychological testing in research on aging and dementia.

The cellular response to stress is an important determinant of disease pathogenesis. Uncovering the molecular fingerprints of distinct stress responses may identify novel biomarkers and key signaling pathways for different diseases. Emerging evidence shows that transfer RNA-derived small RNAs (tDRs) play pivotal roles in stress responses. However, RNA modifications present on tDRs are barriers to accurately quantifying tDRs using traditional small RNA sequencing. Here, AlkB-facilitated methylation sequencing is used to generate a comprehensive landscape of cellular and extracellular tDR abundances in various cell types during different stress responses. Extracellular tDRs are found to have distinct fragmentation signatures from intracellular tDRs and these tDR signatures are better indicators of different stress responses than miRNAs. These distinct extracellular tDR fragmentation patterns and signatures are also observed in plasma from patients on cardiopulmonary bypass. It is additionally demonstrated that angiogenin and RNASE1 are themselves regulated by stressors and contribute to the stress-modulated abundance of sub-populations of cellular and extracellular tDRs. Finally, a sub-population of extracellular tDRs is identified for which AGO2 appears to be required for their expression. Together, these findings provide a detailed profile of stress-responsive tDRs and provide insight about tDR biogenesis and stability in response to cellular stressors.

The extracellular RNA communication consortium (ERCC) is an NIH-funded program aiming to promote the development of new technologies, resources, and knowledge about exRNAs and their carriers. After Phase 1 (2013-2018), Phase 2 of the program (ERCC2, 2019-2023) aims to fill critical gaps in knowledge and technology to enable rigorous and reproducible methods for separation and characterization of both bulk populations of exRNA carriers and single EVs. ERCC2 investigators are also developing new bioinformatic pipelines to promote data integration through the exRNA atlas database. ERCC2 has established several Working Groups (Resource Sharing, Reagent Development, Data Analysis and Coordination, Technology Development, nomenclature, and Scientific Outreach) to promote collaboration between ERCC2 members and the broader scientific community. We expect that ERCC2's current and future achievements will significantly improve our understanding of exRNA biology and the development of accurate and efficient exRNA-based diagnostic, prognostic, and theranostic biomarker assays.

Dynamic profiling of changes in gene expression in response to stressors in specific microenvironments without requiring cellular destruction remains challenging. Current methodologies that seek to interrogate gene expression at a molecular level require sampling of cellular transcriptome and therefore lysis of the cell, preventing serial analysis of cellular transcriptome. To address this area of unmet need, we have recently developed a technology allowing transcriptomic analysis over time without cellular destruction. Our method, TRACE-seq (TRanscriptomic Analysis Captured in Extracellular vesicles using sequencing), is characterized by a cell-type specific transgene expression. It provides data on the transcriptome inside extracellular vesicles that provides an accurate representation of stress-responsive cellular transcriptomic changes. Thus, the transcriptome of cells expressing TRACE can be followed over time without destroying the source cell, which is a powerful tool for many fields of fundamental and translational biology research.

BACKGROUND Atherosclerosis is an inflammatory vascular disease marked by hyperlipidemia and hematopoietic stem cell (HSC) expansion. Activin A, a member of the Activin/GDF/TGFβ/BMP family is broadly expressed and increases in human atherosclerosis, but its functional effects in vivo in this context remain unclear. METHODS We studied LDLR-/- mice on a Western diet for 12 weeks and used adeno-associated viral vectors with a liver-specific thyroxine binding globulin (TBG) promoter to express Activin A or GFP (control). Atherosclerotic lesions were analyzed by oil red staining. Blood lipid profiling was performed by HPLC (High Performance Liquid Chromatography), and immune cells were evaluated by flow cytometry. Liver RNA-sequencing was performed to explore the underlying mechanisms. RESULTS Activin A expression decreased in both livers and aortae from LDLR-/- mice fed a Western diet compared with chow. AAV-TBG-Activin A increased Activin A hepatic expression (∼10-fold at 12-weeks, p<0.0001) and circulating Activin A levels (∼2000pg/ml vs ∼50pg/ml, p<0.001, compared with controls). Hepatic Activin A expression decreased plasma total and low-density lipoprotein (LDL) cholesterol (∼60% and ∼40%, respectively), reduced inflammatory cells in aortae and proliferating hematopoietic stem cells (HSC) in bone marrow, and reduced atherosclerotic lesion area in the aortic arch by ∼60%. Activin A also attenuated liver steatosis and expression of the lipogenesis genes, Srebp1 and Srebp2. RNA sequencing revealed Activin A not only blocked expression of genes involved in hepatic de novo lipogenesis but also fatty acid uptake, and liver inflammation. In addition, Activin A expressed in the liver also reduced white fat tissue accumulation, decreased adipocyte size, and improved glucose tolerance. CONCLUSIONS Our studies reveal hepatic Activin A expression reduces inflammation, HSC expansion, liver steatosis, circulating cholesterol, and fat accumulation, which likely all contribute to the observed protection against atherosclerosis. The reduced Activin A observed in LDLR-/- mice on a Western diet appears maladaptive and deleterious for atherogenesis.

The core Alzheimer's disease (AD) cerebrospinal fluid (CSF) biomarkers; amyloid-β (Aß), total tau (t-tau), and phosphorylated tau (p-tau181), are strong indicators of the presence of AD pathology, but do not correlate well with disease progression, and can be difficult to implement in longitudinal studies where repeat biofluid sampling is required. As a result, blood-based biomarkers are increasingly being sought as alternatives. In this study, we aimed to evaluate a promising blood biomarker discovery technology, Olink Proximity Extension Assays for technical reproducibility characteristics in order to highlight the advantages and disadvantages of using this technology in biomarker discovery in AD. We evaluated the performance of five Olink Proteomic multiplex proximity extension assays (PEA) in plasma samples. Three technical control samples included on each plate allowed calculation of technical variability. Biotemporal stability was measured in three sequential annual samples from 54 individuals with and without AD. Coefficients of variation (CVs), analysis of variance (ANOVA), and variance component analyses were used to quantify technical and individual variation over time. We show that overall, Olink assays are technically robust, with the largest experimental variation stemming from biological differences between individuals for most analytes. As a powerful illustration of one of the potential pitfalls of using a multi-plexed technology for discovery, we performed power calculations using the baseline samples to demonstrate the size of study required to overcome the need for multiple test correction with this technology. We show that the power of moderate effect size proteins was strongly reduced, and as a result investigators should strongly consider pooling resources to perform larger studies using this multiplexed technique where possible.

Cardiovascular complications are common in COVID-19 and strongly associated with disease severity and mortality. However, the mechanisms driving cardiac injury and failure in COVID-19 are largely unknown. We performed plasma proteomics on 80 COVID-19 patients and controls, grouped according to disease severity and cardiac involvement. Findings were validated in 305 independent COVID-19 patients and investigated in an animal model. Here we show that senescence-associated secretory proteins, markers of biological aging, strongly associate with disease severity and cardiac involvement even in age-matched cohorts. FSTL3, an indicator of Activin/TGFβ signaling, was the most significantly upregulated protein associated with the heart failure biomarker, NTproBNP (β = 0.4;padj=4.6x10−7), while ADAMTS13, a vWF-cleaving protease whose loss-of-function causes microvascular thrombosis, was the most downregulated protein associated with myocardial injury (β=−0.4;padj=8x10−7). Mendelian randomization supported a causal role for ADAMTS13 in myocardial injury. These data provide important new insights into the pathophysiology of COVID-19 cardiovascular complications with therapeutic implications.

Abstract Proteomic characterization of human brain tissue is increasingly utilized to identify potential novel biomarkers and drug targets for a variety of neurological diseases. In whole-tissue studies, results may be driven by changes in the proportion of the largest and most abundant organelles or tissue cell-type composition. Spatial proteomics approaches enhance our knowledge of disease mechanisms and changing signalling pathways at the subcellular level by taking into account the importance of cellular localization, which critically influences protein function. Density gradient-based ultracentrifugation methods allow for subcellular fractionation and have been utilized in cell lines, mouse and human brain tissue to quantify thousands of proteins in specific enriched organelles such as the pre- and post-synapse. Serial ultracentrifugation methods allow for the analysis of multiple cellular organelles from the same biological sample, and to our knowledge have not been previously applied to frozen post-mortem human brain tissue. The use of frozen human tissue for tissue fractionation faces two major challenges, the post-mortem interval, during which proteins may leach from their usual location into the cytosol, and freezing, which results in membrane breakdown. Despite these challenges, in this proof-of-concept study, we show that the majority of proteins segregate reproducibly into crude density-based centrifugation fractions, that the fractions are enriched for the appropriate organellar markers and that significant differences in protein localization can be observed between tissue from individuals with Alzheimer’s disease and control individuals.

The aim of this work was to define metabolic correlates and pathways of diabetes pathogenesis in young adults during a subclinical latent phase of diabetes development. We studied 2083 young adults of Black and White ethnicity in the prospective observational cohort Coronary Artery Risk Development in Young Adults (CARDIA) study (mean ± SD age 32.1 ± 3.6 years; 43.9% women; 42.7% Black; mean ± SD BMI 25.6 ± 4.9 kg/m2) and 1797 Framingham Heart Study (FHS) participants (mean ± SD age 54.7 ± 9.7 years; 52.1% women; mean ± SD BMI 27.4 ± 4.8 kg/m2), examining the association of comprehensive metabolite profiles with endophenotypes of diabetes susceptibility (adipose and muscle tissue phenotypes and systemic inflammation). Statistical learning techniques and Cox regression were used to identify metabolite signatures of incident diabetes over a median of nearly two decades of follow-up across both cohorts. We identified known and novel metabolites associated with endophenotypes that delineate the complex pathophysiological architecture of diabetes, spanning mechanisms of muscle insulin resistance, inflammatory lipid signalling and beta cell metabolism (e.g. bioactive lipids, amino acids and microbe- and diet-derived metabolites). Integrating endophenotypes of diabetes susceptibility with the metabolome generated two multi-parametric metabolite scores, one of which (a proinflammatory adiposity score) was associated with incident diabetes across the life course in participants from both the CARDIA study (young adults; HR in a fully adjusted model 2.10 [95% CI 1.72, 2.55], p<0.0001) and FHS (middle-aged and older adults; HR 1.33 [95% CI 1.14, 1.56], p=0.0004). A metabolite score based on the outcome of diabetes was strongly related to diabetes in CARDIA study participants (fully adjusted HR 3.41 [95% CI 2.85, 4.07], p<0.0001) but not in the older FHS population (HR 1.15 [95% CI 0.99, 1.33], p=0.07). Selected metabolic abnormalities in young adulthood identify individuals with heightened diabetes risk independent of race, sex and traditional diabetes risk factors. These signatures replicate across the life course.

To gain insights into the mechanisms driving cardiovascular complications in COVID-19, we performed a case-control plasma proteomics study in COVID-19 patients. Our results identify the senescence-associated secretory phenotype, a marker of biological aging, as the dominant process associated with disease severity and cardiac involvement. FSTL3, an indicator of senescence-promoting Activin/TGFβ signaling, and ADAMTS13, the von Willebrand Factor–cleaving protease whose loss-of-function causes microvascular thrombosis, were among the proteins most strongly associated with myocardial stress and injury. Findings were validated in a larger COVID-19 patient cohort and the hamster COVID-19 model, providing new insights into the pathophysiology of COVID-19 cardiovascular complications with therapeutic implications.