Evolving Strategies for Management of Obstructive Hypertrophic Cardiomyopathy.

For many years, treatment of hypertrophic cardiomyopathy (HCM) has focused on non-disease specific therapies. Cardiac myosin modulators (i.e., mavacamten and aficamten) reduce the pathologic actin-myosin interactions that are characteristic of HCM, leading to improved cardiac energetics and reduction in hypercontractility. Several recently published randomized clinical trials have demonstrated that mavacamten improves exercise capacity, left ventricular outflow tract obstruction, and symptoms in patients with obstructive HCM (oHCM), and may delay the need for septal reduction therapy. Long-term data in real world populations will be needed to fully assess the safety and efficacy of mavacamten. Importantly, HCM is a complex and heterogeneous disease and not all patients will respond to mavacamten; therefore, careful patient selection and shared decision-making will be necessary in guiding the use of mavacamten in oHCM.

Patient experiences in receiving telegenetics care for inherited cardiovascular diseases.

Telegenetics played an important role in providing genetic services to patients during the COVID-19 pandemic. In particular, at our institution, it enabled us to expand our genetic counseling and testing services to non-local family members of patients outside of our prior catchment area. However, as telegenetics continues to be utilized even as social distancing is no longer required, further information is needed regarding the impact of this modality on patient experience within cardiogenetics. This study qualitatively explored the experiences of 12 genotype positive individuals who underwent genetic counseling and testing via telegenetics during the first 22 months of the COVID-19 pandemic and compared the experiences of local vs. non-local patients. Both local and non-local participants discussed similar benefits and drawbacks to the use of technology in telegenetics and overall found the use of telegenetics and at-home genetic testing to be convenient. Both groups also noted having to make changes in their daily lives and future planning as a consequence of the positive genetic testing results. However, access to follow-up care differed between local and non-local participants, with more local participants having scheduled and attended appointments with the appropriate medical providers compared to non-local participants. Supplying non-local patients access to remote cardiogenetic testing may therefore require careful consideration in how to ensure proper follow-up care for genotype positive patients and may necessitate the involvement of national professional or patient-centered organizations to help streamline the referral process.

Myocardial Recovery With HeartMate 3 Left Ventricular Assist Device: An Attainable Goal That Needs Better Precision.

Despite advances in our understanding of myocardial recovery among left ventricular assist device (LVAD) patients, with 10-30% of patients achieving substantial myocardial improvement, the rates of LVAD support cessation remain extremely low (1-2%). These numbers are in stark contrast to clinical trial data where successful LVAD cessation is reported in up to 47% of patients. The majority of LVAD programs lack structured recovery programs and targeted protocols, likely underscoring the heterogeneity that exists among LVAD patients with myocardial recovery. This perspective summarizes the current medical and surgical challenges with respect to 1) identifying the appropriate candidates for LVAD cessation; 2) methods to wean LVAD support; 3) reviewing surgical techniques for cessation of current generation HeartMate 3 LVAD; and 4) approaching shared decision making for LVAD cessation between patients and providers given the uncertainties that remain in the field.

Signaling Pathways Associated With Prior Cardiovascular Events in Hypertrophic Cardiomyopathy.

BACKGROUND: Hypertrophic cardiomyopathy (HCM) is the most common genetic cardiomyopathy. A subset of patients experience major adverse cardiovascular events (MACEs), including arrhythmias, strokes and heart failure. However, the molecular mechanisms underlying MACEs in HCM are still not well understood. Therefore, we conducted a multicenter case-control study of patients with HCM, comparing those with and without prior histories of MACEs to identify dysregulated signaling pathways through plasma proteomics profiling. METHODS: We performed plasma proteomics profiling of 4986 proteins. We developed a proteomics-based discrimination model in patients enrolled at 1 institution (training set) and externally validated the model in patients enrolled at another institution (test set). We performed pathway analysis of proteins dysregulated in patients with prior MACEs. RESULTS: A total of 402 patients were included, with 278 in the training set and 124 in the test set. In this cohort, 257 (64%) patients had prior MACEs (172 in the training set and 85 in the test set). Using the proteomics-based model from the training set, the area under the receiver operating characteristic curve was 0.82 (95% confidence interval, 0.75-0.90) in the test set. Patients with prior MACEs demonstrated dysregulation in pathways known to be associated with MACEs (eg, TGF-ß) and novel pathways (eg, Ras-MAPK and associated pathways). CONCLUSIONS: In this multicenter study of 402 patients with HCM, we identified both known and novel pathways dysregulated in a subset of patients with more advanced disease.

Advanced Heart Failure Therapies for Hypertrophic Cardiomyopathy: State-of-the-Art Review and an Updated Analysis From UNOS.

Hypertrophic cardiomyopathy (HCM) is most commonly associated with obstructive symptoms and sudden cardiac death; however, predominantly nonobstructive advanced heart failure in HCM, marked by medically refractory disease with severe functional impairment, occurs in 5% to 7% of patients with HCM. The diagnosis relies on the integration of imaging (echocardiography/cardiac magnetic resonance), hemodynamic data, and cardiopulmonary exercise testing to identify the patients who will benefit from advanced heart failure therapies. Most advanced heart failure therapies focus on systolic dysfunction and are not always applicable to this patient population. Left ventricular assist devices may be an option in a highly selected population with left ventricular dilation. Heart transplantation is often the best option for patients with advanced heart failure in HCM with excellent post-transplantation survival.

Prediction of worsening heart failure in hypertrophic cardiomyopathy using plasma proteomics.

OBJECTIVE: Heart failure (HF) is one of the most common and lifestyle-limiting complications of hypertrophic cardiomyopathy (HCM). Prediction of worsening HF using clinical measures alone remains limited. Moreover, the mechanisms by which patients with HCM develop worsening HF have not been elucidated. Therefore, the aim of this study was to develop a plasma proteomics-based model to predict worsening HF among patients with HCM and to identify signalling pathways that are differentially regulated in those who subsequently develop worsening HF. METHODS: In this multi-centre, prospective cohort study of 389 patients with HCM, plasma proteomics profiling of 4986 proteins was performed at enrolment. A proteomics-based random forest model was developed to predict worsening HF using data from one institution (training set, n=268). This model was externally validated in patients from a different institution (test set, n=121). Pathway analysis of proteins significantly dysregulated in patients who subsequently developed worsening HF compared with those who did not was executed, using a false discovery rate (FDR) threshold of <0.001. RESULTS: Using the 11-protein proteomics-based model derived from the training set, the area under the receiver-operating characteristic curve to predict worsening HF was 0.87 (95% CI: 0.76 to 0.98) in the test set. Pathway analysis revealed that the Ras-MAPK pathway (FDR<0.00001) and related pathways were dysregulated in patients who subsequently developed worsening HF. CONCLUSIONS: The present study with comprehensive plasma proteomics profiling demonstrated a high accuracy to predict worsening HF in patients with HCM and identified the Ras-MAPK and related signalling pathways as potential underlying mechanisms.

Comprehensive Transcriptomics Profiling of MicroRNA Reveals Plasma Circulating Biomarkers of Hypertrophic Cardiomyopathy and Dysregulated Signaling Pathways.

BACKGROUND: Hypertrophic cardiomyopathy (HCM) is caused by mutations in genes coding for proteins essential for myocardial contraction. However, it remains unclear through which signaling pathways these gene mutations mediate HCM pathogenesis. Growing evidence indicates that microRNAs (miRNAs) play an important role in the regulation of gene expression. We hypothesized that transcriptomics profiling of plasma miRNAs would reveal circulating biomarkers and dysregulated signaling pathways in HCM. METHODS: We conducted a multicenter case-control study of cases with HCM and controls with hypertensive left ventricular hypertrophy. We performed plasma transcriptomics profiling of miRNAs using RNA sequencing. We developed a transcriptomics-based discrimination model using samples retrieved during the first two-thirds of the study period at one institution (training set). We prospectively tested its discriminative ability in samples collected thereafter from the same institution (prospective test set). We also externally validated the model by applying it to samples collected from the other institutions (external test set). We executed pathway analysis of dysregulated miRNAs with univariable P<0.05. RESULTS: This study included 555 patients (392 cases and 163 controls). One thousand one hundred forty-one miRNAs passed our quality control filters. The area under the receiver operating characteristic curve of the transcriptomics-based model derived from the training set was 0.86 (95% CI, 0.79-0.93) in the prospective test set and 0.94 (95% CI, 0.90-0.97) in the external test set. Pathway analysis revealed dysregulation of the Ras-MAPK (mitogen-activated protein kinase) pathway and pathways related to inflammation in HCM. CONCLUSIONS: This study utilized comprehensive transcriptomics profiling with RNA sequencing in HCM, revealing circulating miRNA biomarkers and dysregulated pathways.

Prediction of Major Adverse Cardiovascular Events in Patients With Hypertrophic Cardiomyopathy Using Proteomics Profiling.

BACKGROUND: Hypertrophic cardiomyopathy often causes major adverse cardiovascular events (MACE), for example, arrhythmias, stroke, heart failure, and sudden cardiac death. Currently, there are no models available to predict MACE. Furthermore, it remains unclear which signaling pathways mediate MACE. Therefore, we aimed to prospectively determine protein biomarkers that predict MACE in hypertrophic cardiomyopathy and to identify signaling pathways differentially regulated in patients who subsequently develop MACE. METHODS: In this multi-centre prospective cohort study of patients with hypertrophic cardiomyopathy, we conducted plasma proteomics profiling of 4979 proteins upon enrollment. We developed a proteomics-based model to predict MACE using data from one institution (training set). We tested the predictive ability in independent samples from the other institution (test set) and performed time-to-event analysis. Additionally, we executed pathway analysis of predictive proteins using a false discovery rate threshold of <0.001. RESULTS: The study included 245 patients (n=174 in the training set and n=71 in the test set). Using the proteomics-based model to predict MACE derived from the training set, the area under the receiver-operating-characteristic curve was 0.81 (95% CI, 0.68-0.93) in the test set. In the test set, the high-risk group determined by the proteomics-based predictive model had a significantly higher rate of developing MACE (hazard ratio, 13.6 [95% CI, 1.7-107]; P=0.01). The Ras-MAPK (mitogen-activated protein kinase) pathway was upregulated in patients who subsequently developed MACE (false discovery rate<1.0×10-7). Pathways involved in inflammation and fibrosis-for example, the TGF (transforming growth factor)-ß pathway-were also upregulated. CONCLUSIONS: This study serves as the first to demonstrate the ability of proteomics profiling to predict MACE in hypertrophic cardiomyopathy, exhibiting both novel (eg, Ras-MAPK) and known (eg, TGF-ß) pathways differentially regulated in patients who subsequently experience MACE.

The use of telemedicine in cardiogenetics clinical practice during the COVID-19 pandemic.

BACKGROUND: The COVID-19 pandemic has necessitated the rapid and widespread adoption of novel mechanisms of service delivery, including the use of telemedicine. The aim of this study was to examine the impact of COVID-19 on cardiogenetics practices. METHODS: We retrospectively analyzed the clinical characteristics of patients who were seen for cardiogenetics visits pre-pandemic (1 April-23 December 2019) and during the pandemic (1 April-23 December 2020) at Columbia University Irving Medical Center. RESULTS: Six percent (n = 6) of visits in 2019 were remote telemedicine encounters, whereas 80% (n = 106) of visits in 2020 were telemedicine encounters. In 2019, only 18% (n = 19) of the patients seen for genetic counseling were family members of probands; this percentage increased to 34% in 2020 (n = 45; p = .01). In 2020, the geographic reach of genetic counseling also extended far beyond New York State, reaching a total of 11 states as well as one patient in Puerto Rico. Genetic testing results were similar in 2019 and 2020. CONCLUSION: Despite the health-care delivery barriers created by the COVID-19 pandemic, the use of telemedicine allowed us to expand the reach of cardiovascular genetic counseling and testing.

Proteomics profiling reveals a distinct high-risk molecular subtype of hypertrophic cardiomyopathy.

OBJECTIVE: Hypertrophic cardiomyopathy (HCM) is a heterogeneous disease, likely encompassing several subtypes of disease with distinct biological mechanisms (ie, molecular subtypes). Current models based solely on clinical data have yielded limited accuracy in predicting the risk of major adverse cardiovascular events (MACE). Our aim in this study was to derive molecular subtypes in our multicentre prospective cohort of patients with HCM using proteomics profiling and to examine their longitudinal associations with MACE. METHODS: We applied unsupervised machine learning methods to plasma proteomics profiling data of 1681 proteins from 258 patients with HCM who were prospectively followed for a median of 2.8 years. The primary outcome was MACE, defined as a composite of arrhythmia, heart failure, stroke and sudden cardiac death. RESULTS: We identified four molecular subtypes of HCM. Time-to-event analysis revealed significant differences in MACE-free survival among the four molecular subtypes (plogrank=0.007). Compared with the reference group with the lowest risk of MACE (molecular subtype A), patients in molecular subtype D had a higher risk of subsequently developing MACE, with an HR of 3.41 (95% CI 1.54 to 7.55, p=0.003). Pathway analysis of proteins differentially regulated in molecular subtype D demonstrated an upregulation of the Ras/mitogen-activated protein kinase and associated pathways, as well as pathways related to inflammation and fibrosis (eg, transforming growth factor-ß pathway). CONCLUSIONS: Our prospective plasma proteomics study not only exhibited the presence of HCM molecular subtypes but also identified pathobiological mechanisms associated with a distinct high-risk subtype of HCM.

Right Ventricular Global Longitudinal Strain as a Predictor of Acute and Early Right Heart Failure Post Left Ventricular Assist Device Implantation.

Early right heart failure (RHF) occurs in up to 40% of patients following left ventricular assist device (LVAD) implantation and is associated with increased morbidity and mortality. The most recent report from the Mechanical Circulatory Support-Academic Research Consortium (MCS-ARC) working group subdivides early RHF into early acute RHF and early postimplant RHF. We sought to determine the effectiveness of right ventricular (RV) longitudinal strain (LS) in predicting RHF according to the new MCS-ARC definition. We retrospectively analyzed clinical and echocardiographic data of patients who underwent LVAD implantation between 2015 and 2018. RVLS in the 4-chamber (4ch), RV outflow tract, and subcostal views were measured on pre-LVAD echocardiograms. Fifty-five patients were included in this study. Six patients (11%) suffered early acute RHF, requiring concomitant RVAD implantation intraoperatively. Twenty-two patients (40%) had postimplant RHF. RVLS was significantly reduced in patients who developed early acute and postimplant RHF. At a cutoff of -9.7%, 4ch RVLS had a sensitivity of 88.9% and a specificity of 77.8% for predicting RHF and area under the receiver operating characteristic curve of 0.86 (95% confidence interval 0.76-0.97). Echocardiographic RV strain outperformed more invasive hemodynamic measures and clinical parameters in predicting RHF.

Comprehensive Proteomics Profiling Reveals Circulating Biomarkers of Hypertrophic Cardiomyopathy.

BACKGROUND: Hypertrophic cardiomyopathy (HCM) is caused by mutations in the genes coding for proteins essential in normal myocardial contraction. However, it remains unclear through which molecular pathways gene mutations mediate the development of HCM. The objectives were to determine plasma protein biomarkers of HCM and to reveal molecular pathways differentially regulated in HCM. METHODS: We conducted a multicenter case-control study of cases with HCM and controls with hypertensive left ventricular hypertrophy. We performed plasma proteomics profiling of 1681 proteins. We performed a sparse partial least squares discriminant analysis to develop a proteomics-based discrimination model with data from 1 institution (ie, the training set). We tested the discriminative ability in independent samples from the other institution (ie, the test set). As an exploratory analysis, we executed pathway analysis of significantly dysregulated proteins. Pathways with false discovery rate <0.05 were declared positive. RESULTS: The study included 266 cases and 167 controls (n=308 in the training set; n=125 in the test set). Using the proteomics-based model derived from the training set, the area under the receiver operating characteristic curve was 0.89 (95% CI, 0.83-0.94) in the test set. Pathway analysis revealed that the Ras-MAPK (mitogen-activated protein kinase) pathway, along with its upstream and downstream pathways, was upregulated in HCM. Pathways involved in inflammation and fibrosis-for example, the TGF (transforming growth factor)-ß pathway-were also upregulated. CONCLUSIONS: This study serves as the largest-scale investigation with the most comprehensive proteomics profiling in HCM, revealing circulating biomarkers and exhibiting both novel (eg, Ras-MAPK) and known (eg, TGF-ß) pathways differentially regulated in HCM.

Endotyping in Heart Failure　- Identifying Mechanistically Meaningful Subtypes of Disease.

Endotyping is an emerging concept in which diseases are classified into distinct subtypes based on underlying molecular mechanisms. Heart failure (HF) is a complex clinical syndrome that encompasses multiple endotypes with differential risks of adverse events, and varying responses to treatment. Identifying these distinct endotypes requires molecular-level investigation involving multi-"omics" approaches, including genomics, transcriptomics, proteomics, and metabolomics. The derivation of these HF endotypes has important implications in promoting individualized treatment and facilitating more targeted selection of patients for clinical trials, as well as in potentially revealing new pathways of disease that may serve as therapeutic targets. One challenge in the integrated analysis of high-throughput omics and detailed clinical data is that it requires the ability to handle "big data", a task for which machine learning is well suited. In particular, unsupervised machine learning has the ability to uncover novel endotypes of disease in an unbiased approach. In this review, we will discuss recent efforts to identify HF endotypes and cover approaches involving proteomics, transcriptomics, and genomics, with a focus on machine-learning methods.

Prediction of Genotype Positivity in Patients With Hypertrophic Cardiomyopathy Using Machine Learning.

BACKGROUND: Genetic testing can determine family screening strategies and has prognostic and diagnostic value in hypertrophic cardiomyopathy (HCM). However, it can also pose a significant psychosocial burden. Conventional scoring systems offer modest ability to predict genotype positivity. The aim of our study was to develop a novel prediction model for genotype positivity in patients with HCM by applying machine learning (ML) algorithms. METHODS: We constructed 3 ML models using readily available clinical and cardiac imaging data of 102 patients from Columbia University with HCM who had undergone genetic testing (the training set). We validated model performance on 76 patients with HCM from Massachusetts General Hospital (the test set). Within the test set, we compared the area under the receiver operating characteristic curves (AUROCs) for the ML models against the AUROCs generated by the Toronto HCM Genotype Score (the Toronto score) and Mayo HCM Genotype Predictor (the Mayo score) using the Delong test and net reclassification improvement. RESULTS: Overall, 63 of the 178 patients (35%) were genotype positive. The random forest ML model developed in the training set demonstrated an AUROC of 0.92 (95% CI, 0.85-0.99) in predicting genotype positivity in the test set, significantly outperforming the Toronto score (AUROC, 0.77 [95% CI, 0.65-0.90], P=0.004, net reclassification improvement: P<0.001) and the Mayo score (AUROC, 0.79 [95% CI, 0.67-0.92], P=0.01, net reclassification improvement: P=0.001). The gradient boosted decision tree ML model also achieved significant net reclassification improvement over the Toronto score (P<0.001) and the Mayo score (P=0.03), with an AUROC of 0.87 (95% CI, 0.75-0.99). Compared with the Toronto and Mayo scores, all 3 ML models had higher sensitivity, positive predictive value, and negative predictive value. CONCLUSIONS: Our ML models demonstrated a superior ability to predict genotype positivity in patients with HCM compared with conventional scoring systems in an external validation test set.

Association of the V122I Hereditary Transthyretin Amyloidosis Genetic Variant With Heart Failure Among Individuals of African or Hispanic/Latino Ancestry.

Importance: Hereditary transthyretin (TTR) amyloid cardiomyopathy (hATTR-CM) due to the TTR V122I variant is an autosomal-dominant disorder that causes heart failure in elderly individuals of African ancestry. The clinical associations of carrying the variant, its effect in other African ancestry populations including Hispanic/Latino individuals, and the rates of achieving a clinical diagnosis in carriers are unknown. Objective: To assess the association between the TTR V122I variant and heart failure and identify rates of hATTR-CM diagnosis among carriers with heart failure. Design, Setting, and Participants: Cross-sectional analysis of carriers and noncarriers of TTR V122I of African ancestry aged 50 years or older enrolled in the Penn Medicine Biobank between 2008 and 2017 using electronic health record data from 1996 to 2017. Case-control study in participants of African and Hispanic/Latino ancestry with and without heart failure in the Mount Sinai BioMe Biobank enrolled between 2007 and 2015 using electronic health record data from 2007 to 2018. Exposures: TTR V122I carrier status. Main Outcomes and Measures: The primary outcome was prevalent heart failure. The rate of diagnosis with hATTR-CM among TTR V122I carriers with heart failure was measured. Results: The cross-sectional cohort included 3724 individuals of African ancestry with a median age of 64 years (interquartile range, 57-71); 1755 (47%) were male, 2896 (78%) had a diagnosis of hypertension, and 753 (20%) had a history of myocardial infarction or coronary revascularization. There were 116 TTR V122I carriers (3.1%); 1121 participants (30%) had heart failure. The case-control study consisted of 2307 individuals of African ancestry and 3663 Hispanic/Latino individuals; the median age was 73 years (interquartile range, 68-80), 2271 (38%) were male, 4709 (79%) had a diagnosis of hypertension, and 1008 (17%) had a history of myocardial infarction or coronary revascularization. There were 1376 cases of heart failure. TTR V122I was associated with higher rates of heart failure (cross-sectional cohort: n = 51/116 TTR V122I carriers [44%], n = 1070/3608 noncarriers [30%], adjusted odds ratio, 1.7 [95% CI, 1.2-2.4], P = .006; case-control study: n = 36/1376 heart failure cases [2.6%], n = 82/4594 controls [1.8%], adjusted odds ratio, 1.8 [95% CI, 1.2-2.7], P = .008). Ten of 92 TTR V122I carriers with heart failure (11%) were diagnosed as having hATTR-CM; the median time from onset of symptoms to clinical diagnosis was 3 years. Conclusions and Relevance: Among individuals of African or Hispanic/Latino ancestry enrolled in 2 academic medical center-based biobanks, the TTR V122I genetic variant was significantly associated with heart failure.

Nonbacterial thrombotic endocarditis with embolic cerebral vascular accidents in a patient with advanced, recurrent clear cell carcinoma of the ovary: A case report.

•Nonbacterial thrombotic endocarditis can occur in ovarian clear cell carcinoma.•We report on NBTE-associated embolic cerebrovascular infarcts in advanced OCCC.•Further NBTE-associated embolic events can be prevented with anticoagulant therapy.

An Assessment of Prognostic Factors, Adjuvant Treatment, and Outcomes of Stage IA Polyp-Limited Versus Endometrium-Limited Type II Endometrial Carcinoma.

OBJECTIVE: To determine clinical outcomes in patients with stage IA polyp-limited versus endometrium-limited high-grade (type II) endometrial carcinoma (EC). METHODS: We identified all cases of stage IA polyp-limited or endometrium-limited high-grade EC (FIGO grade 3 endometrioid, serous, clear cell, or mixed) who underwent simple hysterectomy, bilateral salpingo-oophorectomy, peritoneal washings, omental biopsy, and pelvic and para-aortic lymph node dissection and received adjuvant treatment at our institution from October 1995 to November 2012. Progression-free survival (PFS) and overall survival (OS) by histology, adjuvant therapy, and polyp-limited versus endometrium-limited disease status were determined using log-rank test. We analyzed 3 treatment groups: patients who received chemotherapy with or without radiation therapy (RT) (intravaginal or pelvic); patients who received RT (intravaginal RT or pelvic RT) alone; and patients who received no adjuvant treatment. RESULTS: In all, 85 women underwent hysterectomy/salpingo-oophorectomy; all were surgically staged with lymph node assessment and had stage IA EC with no lymphovascular or myometrial invasion. Median follow-up for survivors was 46.5 months (range, 1.98-188.8 months). Forty-nine patients (57.6%) had polyp-limited disease, and 36 (42.4%) had endometrium-limited disease. There were no significant differences in clinicopathologic characteristics between patients within the 3 treatment groups with regard to age at diagnosis, mean body mass index, ECOG (Eastern Cooperative Oncology Group) performance status, polyp-limited or endometrium-limited disease, diabetes, or race. The 3-year PFS rate was 94.9% and the 3-year OS rate was 98.8%. Univariate PFS and OS analysis revealed that age was a relevant prognostic factor (PFS hazard ratio [95% confidence interval], 1.13 [1.02-1.25]; P = 0.022; OS hazard ratio [95% confidence interval], 1.19 [1.02-1.38]; P = 0.03). Adjuvant treatment did not impact outcomes. CONCLUSIONS: Clinical outcomes of surgical stage IA type II polyp- or endometrium-limited high-grade epithelial EC are equally favorable regardless of histologic subtype or adjuvant therapy received. The benefit of adjuvant therapy in this select group remains to be determined.

Minimal effect of gene clustering on expression in Escherichia coli.

Genes that interact or function together are often clustered in bacterial genomes, and it has been proposed that this clustering may affect gene expression. In this study, we directly compared gene expression in nonclustered arrangements and in three common clustered arrangements (codirectional, divergent, and operon) using synthetic circuits in Escherichia coli. We found that gene clustering had minimal effects on gene expression. Specifically, gene clustering did not alter constitutive expression levels or stochastic fluctuations in expression ("expression noise"). Remarkably, the expression of two genes that share the same chromosome position with the same promoter (operon) or with separate promoters (codirectional and divergent arrangements) was not significantly more correlated than genes at different chromosome positions (nonclustered arrangements). The only observed effect of clustering was increased transcription factor binding in codirectional and divergent gene arrangements due to DNA looping, but this is not a specific feature of clustering. In summary, we demonstrate that gene clustering is not a general modulator of gene expression, and therefore any effects of clustering are likely to occur only with specific genes or under certain conditions.

Regulatory consequences of gene translocation in bacteria.

Gene translocations play an important role in the plasticity and evolution of bacterial genomes. In this study, we investigated the impact on gene regulation of three genome organizational features that can be altered by translocations: (i) chromosome position; (ii) gene orientation; and (iii) the distance between a target gene and its transcription factor gene ('target-TF distance'). Specifically, we quantified the effect of these features on constitutive expression, transcription factor binding and/or gene expression noise using a synthetic network in Escherichia coli composed of a transcription factor (LacI repressor) and its target gene (yfp). Here we show that gene regulation is generally robust to changes in chromosome position, gene orientation and target-TF distance. The only demonstrable effect was that chromosome position alters constitutive expression, due to changes in gene copy number and local sequence effects, and that this determines maximum and minimum expression levels. The results were incorporated into a mathematical model which was used to quantitatively predict the responses of a simple gene network to gene translocations; the predictions were confirmed experimentally. In summary, gene translocation can modulate constitutive gene expression levels due to changes in chromosome position but it has minimal impact on other facets of gene regulation.