HLA Class II (DR, DQ, DP) Genes Were Separately Associated With the Progression From Seroconversion to Onset of Type 1 Diabetes Among Participants in Two Diabetes Prevention Trials (DPT-1 and TN07)

OBJECTIVE

To explore associations of HLA class II genes (HLAII) with the progression of islet autoimmunity from asymptomatic to symptomatic type 1 diabetes (T1D).

RESEARCH DESIGN AND METHODS

Next-generation targeted sequencing was used to genotype eight HLAII genes (DQA1, DQB1, DRB1, DRB3, DRB4, DRB5, DPA1, DPB1) in 1,216 participants from the Diabetes Prevention Trial-1 and Randomized Diabetes Prevention Trial with Oral Insulin sponsored by TrialNet. By the linkage disequilibrium, DQA1 and DQB1 are haplotyped to form DQ haplotypes; DP and DR haplotypes are similarly constructed. Together with available clinical covariables, we applied the Cox regression model to assess HLAII immunogenic associations with the disease progression.

RESULTS

First, the current investigation updated the previously reported genetic associations of DQA1*03:01-DQB1*03:02 (hazard ratio [HR] = 1.25, P = 3.50*10-3) and DQA1*03:03-DQB1*03:01 (HR = 0.56, P = 1.16*10-3), and also uncovered a risk association with DQA1*05:01-DQB1*02:01 (HR = 1.19, P = 0.041). Second, after adjusting for DQ, DPA1*02:01-DPB1*11:01 and DPA1*01:03-DPB1*03:01 were found to have opposite associations with progression (HR = 1.98 and 0.70, P = 0.021 and 6.16*10-3, respectively). Third, DRB1*03:01-DRB3*01:01 and DRB1*03:01-DRB3*02:02, sharing the DRB1*03:01, had opposite associations (HR = 0.73 and 1.44, P = 0.04 and 0.019, respectively), indicating a role of DRB3. Meanwhile, DRB1*12:01-DRB3*02:02 and DRB1*01:03 alone were found to associate with progression (HR = 2.6 and 2.32, P = 0.018 and 0.039, respectively). Fourth, through enumerating all heterodimers, it was found that both DQ and DP could exhibit associations with disease progression.

CONCLUSIONS

These results suggest that HLAII polymorphisms influence progression from islet autoimmunity to T1D among at-risk subjects with islet autoantibodies.

Distinct transcriptomic profiles in children prior to the appearance of type 1 diabetes-linked islet autoantibodies and following enterovirus infection

Although the genetic basis and pathogenesis of type 1 diabetes have been studied extensively, how host responses to environmental factors might contribute to autoantibody development remains largely unknown. Here, we use longitudinal blood transcriptome sequencing data to characterize host responses in children within 12 months prior to the appearance of type 1 diabetes-linked islet autoantibodies, as well as matched control children. We report that children who present with insulin-specific autoantibodies first have distinct transcriptional profiles from those who develop GADA autoantibodies first. In particular, gene dosage-driven expression of GSTM1 is associated with GADA autoantibody positivity. Moreover, compared with controls, we observe increased monocyte and decreased B cell proportions 9-12 months prior to autoantibody positivity, especially in children who developed antibodies against insulin first. Lastly, we show that control children present transcriptional signatures consistent with robust immune responses to enterovirus infection, whereas children who later developed islet autoimmunity do not. These findings highlight distinct immune-related transcriptomic differences between case and control children prior to case progression to islet autoimmunity and uncover deficient antiviral response in children who later develop islet autoimmunity.

Type 1 Diabetes Risk Phenotypes Using Cluster Analysis

Background

Although statistical models for predicting type 1 diabetes risk have been developed, approaches that reveal clinically meaningful clusters in the at-risk population and allow for non-linear relationships between predictors are lacking. We aimed to identify and characterize clusters of islet autoantibody-positive individuals that share similar characteristics and type 1 diabetes risk.

Methods

We tested a novel outcome-guided clustering method in initially non-diabetic autoantibody-positive relatives of individuals with type 1 diabetes, using the TrialNet Pathway to Prevention (PTP) study data (n=1127). The outcome of the analysis was time to type 1 diabetes and variables in the model included demographics, genetics, metabolic factors and islet autoantibodies. An independent dataset (Diabetes Prevention Trial of Type 1 Diabetes, DPT-1 study) (n=704) was used for validation.

Findings

The analysis revealed 8 clusters with varying type 1 diabetes risks, categorized into three groups. Group A had three clusters with high glucose levels and high risk. Group B included four clusters with elevated autoantibody titers. Group C had three lower-risk clusters with lower autoantibody titers and glucose levels. Within the groups, the clusters exhibit variations in characteristics such as glucose levels, C-peptide levels, age, and genetic risk. A decision rule for assigning individuals to clusters was developed. The validation dataset confirms that the clusters can identify individuals with similar characteristics.

Interpretation

Demographic, metabolic, immunological, and genetic markers can be used to identify clusters of distinctive characteristics and different risks of progression to type 1 diabetes among autoantibody-positive individuals with a family history of type 1 diabetes. The results also revealed the heterogeneity in the population and complex interactions between variables.

The Rare and Atypical Diabetes Network (RADIANT) Study: Design and Early Results

OBJECTIVE

The Rare and Atypical Diabetes Network (RADIANT) will perform a study of individuals and, if deemed informative, a study of their family members with uncharacterized forms of diabetes.

RESEARCH DESIGN AND METHODS

The protocol includes genomic (whole-genome [WGS], RNA, and mitochondrial sequencing), phenotypic (vital signs, biometric measurements, questionnaires, and photography), metabolomics, and metabolic assessments.

RESULTS

Among 122 with WGS results of 878 enrolled individuals, a likely pathogenic variant in a known diabetes monogenic gene was found in 3 (2.5%), and six new monogenic variants have been identified in the SMAD5, PTPMT1, INS, NFKB1, IGF1R, and PAX6 genes. Frequent phenotypic clusters are lean type 2 diabetes, autoantibody-negative and insulin-deficient diabetes, lipodystrophic diabetes, and new forms of possible monogenic or oligogenic diabetes.

CONCLUSIONS

The analyses will lead to improved means of atypical diabetes identification. Genetic sequencing can identify new variants, and metabolomics and transcriptomics analysis can identify novel mechanisms and biomarkers for atypical disease.

Interaction Between Dietary Iron Intake and Genetically Determined Iron Overload: Risk of Islet Autoimmunity and Progression to Type 1 Diabetes in the TEDDY Study

OBJECTIVE

To examine whether iron intake and genetically determined iron overload interact in predisposing to the development of childhood islet autoimmunity (IA) and type 1 diabetes (T1D).

RESEARCH DESIGN AND METHODS

In The Environmental Determinants of Diabetes in the Young (TEDDY) study, 7,770 genetically high-risk children were followed from birth until the development of IA and progression to T1D. Exposures included energy-adjusted iron intake in the first 3 years of life and a genetic risk score (GRS) for increased circulating iron.

RESULTS

We found a U-shaped association between iron intake and risk of GAD antibody as the first autoantibody. In children with GRS ≥2 iron risk alleles, high iron intake was associated with an increased risk of IA, with insulin as first autoantibody (adjusted hazard ratio 1.71 [95% CI 1.14; 2.58]) compared with moderate iron intake.

CONCLUSIONS

Iron intake may alter the risk of IA in children with high-risk HLA haplogenotypes.

Epigenetic Age in Peripheral Blood Among Children, Adolescent, and Adult Survivors of Childhood Cancer

Importance

Certain cancer therapies are risk factors for epigenetic age acceleration (EAA) among survivors of childhood cancer, and EAA is associated with chronic health conditions (CHCs). However, small numbers of younger survivors (aged <20 years) previously evaluated have limited the ability to calculate EAA among this age group.

Objective

To evaluate the change rate of epigenetic age (EA) and EAA in younger compared with older survivors and the possible association of EAA with early-onset obesity (aged <20 years), severity/burden of CHCs, and late mortality (>5 years from cancer diagnosis).

Design, Setting, and Participants

Study participants were from the St Jude Lifetime Cohort, initiated in 2007 with ongoing follow-up. The present study was conducted from April 17, 2022, to March 23, 2023. Survivors in this cohort of European ancestry with DNA methylation data were included. Cross-sectional annual changes in EA and EAA were compared across 5 different chronologic age groups: age 0 to 9 (children), 10 to 19 (adolescents), 20 to 34 (younger adults), 35 to 49 (middle-aged adults), and greater than or equal to 50 (older adults) years. Logistic regression evaluated the association between EAA and early-onset obesity or severity/burden of CHCs. Cox proportional hazards regression assessed the association between EAA and late mortality.

Main Outcomes and Measures

Early-onset obesity, severity/burden of CHCs (graded using the Common Terminology Criteria for Adverse Events (grade 1, mild; 2, moderate; 3, severe/disabling; 4, life-threatening) and were combined into high vs low severity/burden based on frequency and grade), and late mortality were the outcomes based on follow-up until April 2020. Expanded DNA methylation profiling increased the number of survivors younger than 20 years (n = 690). Epigenetic age was calculated primarily using the Levine clock, and EAA was derived from least squares regression of EA against chronologic age and was standardized to a z score (Levine EEA).

Results

Among 2846 participants (median age, 30.3 [IQR, 9.3-41.5] years; 53% males), the cross-sectional annual change in EA_Levine was higher in children (1.63 years) and adolescents (1.14 years), and the adjusted least-squares mean of Levine EEA was lower in children (-0.22 years) and older adults (-1.70 years). Each 1-SD increase in Levine EEA was associated with increased risk of developing early-onset obesity (odds ratio [OR], 1.46; 95% CI, 1.19-1.78), high severity/burden of CHCs (OR, 1.13; 95% CI, 1.03-1.24), and late mortality (hazard ratio, 1.75; 95% CI, 1.35-2.26).

Conclusions and Relevance

The findings of this study suggest that EAA measured in children and adolescent survivors of childhood cancer is associated with early-onset obesity, severity/burden of all CHCs, and late mortality. Evaluating EAA may help identify survivors of childhood cancer at increased risk for early-onset obesity, morbidity in general, and mortality.

Data Mining Framework for Discovering and Clustering Phenotypes of Atypical Diabetes

CONTEXT

Some individuals present with forms of diabetes that are "atypical" (AD), which do not conform to typical features of either type 1 diabetes (T1D) or type 2 diabetes (T2D). These forms of AD display a range of phenotypic characteristics that likely reflect different endotypes based on unique etiologies or pathogenic processes.

OBJECTIVE

To develop an analytical approach to identify and cluster phenotypes of AD.

METHODS

We developed Discover Atypical Diabetes (DiscoverAD), a data mining framework, to identify and cluster phenotypes of AD. DiscoverAD was trained against characteristics of manually classified patients with AD among 278 adults with diabetes within the Cameron County Hispanic Cohort (CCHC) (Study A). We then tested DiscoverAD in a separate population of 758 multiethnic children with T1D within the Texas Children's Hospital Registry for New-Onset Type 1 Diabetes (TCHRNO-1) (Study B).

RESULTS

We identified an AD frequency of 11.5% in the CCHC (Study A) and 5.3% in the pediatric TCHRNO-1 (Study B). Cluster analysis identified 4 distinct groups of AD in Study A: cluster 1, positive for the 65 kDa glutamate decarboxylase autoantibody (GAD65Ab), adult-onset, long disease duration, preserved beta-cell function, no insulin treatment; cluster 2, GAD65Ab negative, diagnosed at age ≤21 years; cluster 3, GAD65Ab negative, adult-onset, poor beta-cell function, lacking central obesity; cluster 4, diabetic ketoacidosis (DKA)-prone participants lacking a typical T1D phenotype. Applying DiscoverAD to the pediatric patients with T1D in Study B revealed 2 distinct groups of AD: cluster 1, autoantibody negative, poor beta-cell function, lower body mass index (BMI); cluster 2, autoantibody positive, higher BMI, higher incidence of DKA.

CONCLUSION

DiscoverAD can be adapted to different datasets to identify and define phenotypes of participants with AD based on available clinical variables.

High-throughput muscle fiber typing from RNA sequencing data

Background

Skeletal muscle fiber type distribution has implications for human health, muscle function, and performance. This knowledge has been gathered using labor-intensive and costly methodology that limited these studies. Here, we present a method based on muscle tissue RNA sequencing data (totRNAseq) to estimate the distribution of skeletal muscle fiber types from frozen human samples, allowing for a larger number of individuals to be tested.

Methods

By using single-nuclei RNA sequencing (snRNAseq) data as a reference, cluster expression signatures were produced by averaging gene expression of cluster gene markers and then applying these to totRNAseq data and inferring muscle fiber nuclei type via linear matrix decomposition. This estimate was then compared with fiber type distribution measured by ATPase staining or myosin heavy chain protein isoform distribution of 62 muscle samples in two independent cohorts (n = 39 and 22).

Results

The correlation between the sequencing-based method and the other two were r_ATPas = 0.44 [0.13–0.67], [95% CI], and r_myosin = 0.83 [0.61–0.93], with p = 5.70 × 10^–3 and 2.00 × 10^–6, respectively. The deconvolution inference of fiber type composition was accurate even for very low totRNAseq sequencing depths, i.e., down to an average of ~ 10,000 paired-end reads.

Conclusions

This new method (https://github.com/OlaHanssonLab/PredictFiberType) consequently allows for measurement of fiber type distribution of a larger number of samples using totRNAseq in a cost and labor-efficient way. It is now feasible to study the association between fiber type distribution and e.g. health outcomes in large well-powered studies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13395-022-00299-4.

Telomere length is not a main factor for the development of islet autoimmunity and type 1 diabetes in the TEDDY study

The Environmental Determinants of Diabetes in the Young (TEDDY) study enrolled 8676 children, 3-4 months of age, born with HLA-susceptibility genotypes for islet autoimmunity (IA) and type 1 diabetes (T1D). Whole-genome sequencing (WGS) was performed in 1119 children in a nested case-control study design. Telomere length was estimated from WGS data using five tools: Computel, Telseq, Telomerecat, qMotif and Motif_counter. The estimated median telomere length was 5.10 kb (IQR 4.52-5.68 kb) using Computel. The age when the blood sample was drawn had a significant negative correlation with telomere length (P = 0.003). European children, particularly those from Finland (P = 0.041) and from Sweden (P = 0.001), had shorter telomeres than children from the U.S.A. Paternal age (P = 0.019) was positively associated with telomere length. First-degree relative status, presence of gestational diabetes in the mother, and maternal age did not have a significant impact on estimated telomere length. HLA-DR4/4 or HLA-DR4/X children had significantly longer telomeres compared to children with HLA-DR3/3 or HLA-DR3/9 haplogenotypes (P = 0.008). Estimated telomere length was not significantly different with respect to any IA (P = 0.377), IAA-first (P = 0.248), GADA-first (P = 0.248) or T1D (P = 0.861). These results suggest that telomere length has no major impact on the risk for IA, the first step to develop T1D. Nevertheless, telomere length was shorter in the T1D high prevalence populations, Finland and Sweden.

First-appearing islet autoantibodies for type 1 diabetes in young children: maternal life events during pregnancy and the child's genetic risk

AIMS/HYPOTHESIS

Psychological stress has long been considered a possible trigger of type 1 diabetes, although prospective studies examining the link between psychological stress or life events during pregnancy and the child's type 1 diabetes risk are rare. The objective of this study was to examine the association between life events during pregnancy and first-appearing islet autoantibodies (IA) in young children, conditioned by the child's type 1 diabetes-related genetic risk.

METHODS

The IA status of 7317 genetically at-risk The Environmental Determinants of Diabetes in the Young (TEDDY) participants was assessed every 3 months from 3 months to 4 years, and bi-annually thereafter. Reports of major life events during pregnancy were collected at study inception when the child was 3 months of age and placed into one of six categories. Life events during pregnancy were examined for association with first-appearing insulin (IAA) (N = 222) or GAD (GADA) (N = 209) autoantibodies in the child until 6 years of age using proportional hazard models. Relative excess risk due to interaction (RERI) by the child's HLA-DR and SNP profile was estimated.

RESULTS

Overall, 65% of mothers reported a life event during pregnancy; disease/injury (25%), serious interpersonal (28%) and job-related (25%) life events were most common. The association of life events during pregnancy differed between IAA and GADA as the first-appearing autoantibody. Serious interpersonal life events correlated with increased risk of GADA-first only in HLA-DR3 children with the BACH2-T allele (HR 2.28, p < 0.0001), an additive interaction (RERI 1.87, p = 0.0004). Job-related life events were also associated with increased risk of GADA-first among HLA-DR3/4 children (HR 1.53, p = 0.04) independent of serious interpersonal life events (HR 1.90, p = 0.002), an additive interaction (RERI 1.19, p = 0.004). Job-related life events correlated with reduced risk of IAA-first (HR 0.55, p = 0.004), particularly in children with the BTNL2-GG allele (HR 0.48; 95% CI 0.31, 0.76).

CONCLUSIONS/INTERPRETATION

Specific life events during pregnancy are differentially related to IAA vs GADA as first-appearing IA and interact with different HLA and non-HLA genetic factors, supporting the concept of different endotypes underlying type 1 diabetes. However, the mechanisms underlying these associations remain to be discovered. Life events may be markers for other yet-to-be-identified factors important to the development of first-appearing IA.

Relationship between insulin sensitivity and gene expression in human skeletal muscle

BACKGROUND

Insulin resistance (IR) in skeletal muscle is a key feature of the pre-diabetic state, hypertension, dyslipidemia, cardiovascular diseases and also predicts type 2 diabetes. However, the underlying molecular mechanisms are still poorly understood.

METHODS

To explore these mechanisms, we related global skeletal muscle gene expression profiling of 38 non-diabetic men to a surrogate measure of insulin sensitivity, i.e. homeostatic model assessment of insulin resistance (HOMA-IR).

RESULTS

We identified 70 genes positively and 110 genes inversely correlated with insulin sensitivity in human skeletal muscle, identifying autophagy-related genes as positively correlated with insulin sensitivity. Replication in an independent study of 9 non-diabetic men resulted in 10 overlapping genes that strongly correlated with insulin sensitivity, including SIRT2, involved in lipid metabolism, and FBXW5 that regulates mammalian target-of-rapamycin (mTOR) and autophagy. The expressions of SIRT2 and FBXW5 were also positively correlated with the expression of key genes promoting the phenotype of an insulin sensitive myocyte e.g. PPARGC1A.

CONCLUSIONS

The muscle expression of 180 genes were correlated with insulin sensitivity. These data suggest that activation of genes involved in lipid metabolism, e.g. SIRT2, and genes regulating autophagy and mTOR signaling, e.g. FBXW5, are associated with increased insulin sensitivity in human skeletal muscle, reflecting a highly flexible nutrient sensing.

Abstract 1077: Use of the SVClassify algorithm to classify pediatric solid tumor translocation variant calls as likely true or false positives

With the advent of next-generation sequencing technologies, a variety of structural variant (SV) calling algorithms have been developed. However, these algorithms are generally not sensitive and specific, in part because they are biased toward identification of specific types or lengths of SVs. Thus, concordance among algorithms is often very low. At the National Institute of Standards and Technology (NIST), we have previously rigorously characterized sequencing biases across multiple sequencing platforms for the candidate NIST reference material, RM 8398 (the genome of individual NA12878). This yielded a high-confidence set of SNP and small indel (&lt;40 bp) variant calls. To extend our methods to SVs, we have developed SVClassify, which classifies SVs as likely true or false positive variants by combining evidence from one or more sequencing datasets. For NA12878, we were able to separate a set of validated deletions from random genomic regions with false positive and false negative rates less than 5%. We also found that the set of validated deletions clustered into different categories, including heterozygous Alu deletions, homozygous Alu deletions, and other heterozygous deletions. Until now, SVClassify has only been used for classification of large deletions on the non-cancerous genome, NA12878. In order to assess the sensitivity and specificity of SVClassify for correctly classifying translocations, we are using the RSVSim R package to simulate different numbers of translocations within repeat regions of the human genome. Furthermore, we will investigate the performance of SVClassify on cancer genomes, particularly pediatric solid tumors, which exhibit extensively rearranged genomes compared to their normal counterparts. To do so, pediatric tumor and normal datasets from multiple sequencing technologies will be integrated. To represent a spectrum of translocations, we will assess variant calls from cancer genomes with varying degrees of rearrangements: neuroblastoma, Ewing sarcoma, and osteosarcoma. Finally, we will use SVclassify to classify candidate SV calls made using the Complete Genomics pipeline, as well as those made on Illumina datasets using the recently developed SMUFIN algorithm.

Citation Format: Jo Lynne Harenza, Hemang M. Parikh, Jun S. Wei, Xinyu Wen, Sivasish Sindiri, Rajesh Patidar, Marc Salit, Paul S. Meltzer, Javed Khan, Justin Zook. Use of the SVClassify algorithm to classify pediatric solid tumor translocation variant calls as likely true or false positives. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1077. doi:10.1158/1538-7445.AM2015-1077

Abstract 2778: Fine mapping the KLK3 locus on chromosome 19q13.33 associated with prostate cancer susceptibility and PSA levels

Prostate cancer is the most commonly diagnosed non-cutaneous cancer in men in the United States with over 200,000 new cases and 30,000 deaths estimated in 2010. Measurements of serum prostate specific antigen (PSA) protein levels form the basis for a widely used test to screen men for prostate cancer. Germline variants in the gene that encodes the PSA protein (KLK3) have been shown to be associated with serum PSA levels. In genome-wide association studies (GWAS) of prostate cancer, KLK3 variants have been suggested to be prostate cancer risk factors. Based on a resequence analysis of a 56 kb region on chromosome 19q13.33, centered on the KLK3 gene, that characterized common germline variations in the region, we fine mapped this locus by genotyping tag SNPs in 3,522 prostate cancer cases and 3,338 controls from five cohort and case-control studies. The cohort studies are: the Prostate, Lung, Colon and Ovarian (PLCO) Cancer Screening Trial, the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study (ATBC), the American Cancer Society Prevention Study II Nutrition Cohort (CPS-II) and the Cohort of Norway (CONOR). The case-control study is the French Prostate Case-Control Study (CeRePP) which is a hospital based case-control study. This study has &gt; 80% power to detect an association with an odds ratio of 1.25 (assuming a MAF of 0.05, a prevalence of prostate cancer = 1.5067% and alpha = 0.05). We did not observe a strong association for KLK3 variants, previously reported to confer risk for prostate cancer (rs2735839; P = 0.20). However, three highly correlated SNPs (rs17632542, rs62113212 and rs62113214) demonstrated an association with prostate cancer (P = 3.41×10−4, per-allele odds ratio (OR) = 0.77, 95% CI = 0.67-0.89). In particular, the signal is apparent in nonaggressive prostate cancer cases with Gleason score &lt; 7 and disease stage &lt; III (P = 2.62×10−5, per-allele trend OR = 0.67, 95% CI = 0.56-0.81) but not in advanced cases with Gleason score &gt; 8 or stage ≥ III (P = 0.31, per-allele OR = 1.12, 95% CI = 0.90-1.40). One of the three highly correlated SNPs, rs17632542, introduces a non-synonymous amino acid change in the KLK3 protein with a predicted benign or neutral functional impact. Baseline PSA levels were lower in control subjects with one or more minor alleles at any one of the three SNPs (1.12 ng/ml, 95% CI = 0.96-1.28) as compared to those with no minor alleles (1.61 ng/ml, 95% CI = 1.49-1.72) (P = 9.70×10−5). Together our results suggest that germline KLK3 variants could influence the diagnosis of nonaggressive prostate cancer by influencing the likelihood of biopsy. It is possible that the KLK3 locus also contributes to prostate cancer risk, but additional studies will be needed to dissect the contribution of KLK3 to PSA and prostate risk separately.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 2778. doi:10.1158/1538-7445.AM2011-2778

An investigation of alpha-methyl amino-acids and their derivatives on isolated tissue preparations

1. The ability of alpha-methyl amino-acids and their corresponding amines to restore the sympathomimetic actions of tyramine, and the uptake of the amino-acids and the amines, were studied in isolated tissue preparations obtained from reserpine pretreated animals.2. Tyramine relaxed isolated rat ileum preparations from non-reserpinized rats but not from reserpine treated animals. alpha-Methyldopa, alpha-methylnoradrenaline and lower concentrations of metaraminol restored the responses of reserpinized preparations. alpha-Methyldopamine, alpha-methyl-m-tyrosine, alpha-methyl-p-tyrosine and higher concentrations of metaraminol did not do so. The restoring effect of alpha-methyldopa was blocked by disulphiram. alpha-Methyl-p-tyrosine or alpha-methyl-m-tyrosine blocked the restoring action of alpha-methyldopa but not of alpha-methylnoradrenaline. Cocaine blocked the restoration of responses to tyramine by alpha-methylnoradrenaline but not by alpha-methyldopa. alpha-Methyldopa and alpha-methylnoradrenaline failed to restore responses to tyramine in the presence of sodium-free Tyrode solution.3. Tyramine increased the perfusion pressure in isolated rabbit ear preparations obtained from non-reserpinized animals but was very much less active in preparations obtained from reserpine treated animals. alpha-Methyldopa, alpha-methyl-m-tyrosine, alpha-methylnoradrenaline, alpha-methyl-p-tyrosine and metaraminol restored the effects of tyramine. alpha-Methyldopamine did not do so. The restoring effect of alpha-methyldopa and alpha-methyl-m-tyrosine was blocked by disulfiram. alpha-Methyl-p-tyrosine blocked the restoring effect of alpha-methyl-m-tyrosine.4. Tyramine produced positive inotropic effects in isolated rabbit heart preparations. This was either reduced or absent in preparations obtained from reserpine pretreated animals. alpha-Methyldopa, alpha-methylnoradrenaline, alpha-methyl-m-tyrosine and metaraminol restored the responses to tyramine. alpha-Methyldopamine and alpha-methyl-p-tyrosine did not do so.