RNA-seq transcriptional profiling of peripheral blood leukocytes from cattle infected with Mycobacterium bovis

Bovine tuberculosis, caused by infection with Mycobacterium bovis, is a major endemic disease affecting cattle populations worldwide, despite the implementation of stringent surveillance and control programs in many countries. The development of high-throughput functional genomics technologies, including gene expression microarrays and RNA-sequencing (RNA-seq), has enabled detailed analysis of the host transcriptome to M. bovis infection, particularly at the macrophage and peripheral blood level. In the present study, we have analyzed the peripheral blood leukocyte (PBL) transcriptome of eight natural M. bovis-infected and eight age- and sex-matched non-infected control Holstein-Friesian animals using RNA-seq. In addition, we compared gene expression profiles generated using RNA-seq with those previously generated using the high-density Affymetrix(®) GeneChip(®) Bovine Genome Array platform from the same PBL-extracted RNA. A total of 3,250 differentially expressed (DE) annotated genes were detected in the M. bovis-infected samples relative to the controls (adjusted P-value ≤0.05), with the number of genes displaying decreased relative expression (1,671) exceeding those with increased relative expression (1,579). Ingenuity(®) Systems Pathway Analysis (IPA) of all DE genes revealed enrichment for genes with immune function. Notably, transcriptional suppression was observed among several of the top-ranking canonical pathways including Leukocyte Extravasation Signaling. Comparative platform analysis demonstrated that RNA-seq detected a larger number of annotated DE genes (3,250) relative to the microarray (1,398), of which 917 genes were common to both technologies and displayed the same direction of expression. Finally, we show that RNA-seq had an increased dynamic range compared to the microarray for estimating differential gene expression

Long-term results after liver transplantation for primary hepatic epithelioid hemangioendothelioma

Background: Hepatic epithelioid hemangioendothelioma (PHEHE) is a multifocal, low-grade malignant neoplasia characterized by its epithelial-like appearance and vascular endothelial histogenesis. The outcome of 16 patients treated with orthotopic liver transplantation (OLT) is the subject of this report. Methods: A retrospective study of 16 patients with HEHE (7 men, 9 women) with ages ranging from 24 to 58 years (mean 37 ± 10.6 years). Follow-up intervals ranged from 1 to 15 years (median of 4.5 years). Results: Actual patient survival at 1, 3, and 5 years was 100, 87.5, and 71.3%, respectively. Disease-free survival at 1, 3, and 5 years was 81.3, 68.8, and 60.2%, respectively. The 90-day operative mortality was 0. Involvement of the hilar lymph nodes or vascular invasion did not affect survival. The 5-year survival of HEHE compares favorably with that of hepatocellular carcinoma at the same stage (stage 4A): 71.3 versus 9.8% (p=0.001) Conclusions: The long-term survival obtained in this series justifies OLT for these tumors even in the presence of limited extrahepatic disease. © 1995 The Society of Surgical Oncology, Inc

A gentle introduction to the functional renormalization group: the Kondo effect in quantum dots

The functional renormalization group provides an efficient description of the interplay and competition of correlations on different energy scales in interacting Fermi systems. An exact hierarchy of flow equations yields the gradual evolution from a microscopic model Hamiltonian to the effective action as a function of a continuously decreasing energy cutoff. Practical implementations rely on suitable truncations of the hierarchy, which capture nonuniversal properties at higher energy scales in addition to the universal low-energy asymptotics. As a specific example we study transport properties through a single-level quantum dot coupled to Fermi liquid leads. In particular, we focus on the temperature T=0 gate voltage dependence of the linear conductance. A comparison with exact results shows that the functional renormalization group approach captures the broad resonance plateau as well as the emergence of the Kondo scale. It can be easily extended to more complex setups of quantum dots.Comment: contribution to Les Houches proceedings 2006, Springer styl

Magnetic Catalysis and Quantum Hall Ferromagnetism in Weakly Coupled Graphene

We study the realization in a model of graphene of the phenomenon whereby the tendency of gauge-field mediated interactions to break chiral symmetry spontaneously is greatly enhanced in an external magnetic field. We prove that, in the weak coupling limit, and where the electron-electron interaction satisfies certain mild conditions, the ground state of charge neutral graphene in an external magnetic field is a quantum Hall ferromagnet which spontaneously breaks the emergent U(4) symmetry to U(2)XU(2). We argue that, due to a residual CP symmetry, the quantum Hall ferromagnet order parameter is given exactly by the leading order in perturbation theory. On the other hand, the chiral condensate which is the order parameter for chiral symmetry breaking generically obtains contributions at all orders. We compute the leading correction to the chiral condensate. We argue that the ensuing fermion spectrum resembles that of massive fermions with a vanishing U(4)-valued chemical potential. We discuss the realization of parity and charge conjugation symmetries and argue that, in the context of our model, the charge neutral quantum Hall state in graphene is a bulk insulator, with vanishing longitudinal conductivity due to a charge gap and Hall conductivity vanishing due to a residual discrete particle-hole symmetry.Comment: 35 page

The skeletal phenotype of chondroadherin deficient mice

Chondroadherin, a leucine rich repeat extracellular matrix protein with functions in cell to matrix interactions, binds cells via their a2b1 integrin as well as via cell surface proteoglycans, providing for different sets of signals to the cell. Additionally, the protein acts as an anchor to the matrix by binding tightly to collagens type I and II as well as type VI. We generated mice with inactivated chondroadherin gene to provide integrated studies of the role of the protein. The null mice presented distinct phenotypes with affected cartilage as well as bone. At 3–6 weeks of age the epiphyseal growth plate was widened most pronounced in the proliferative zone. The proteome of the femoral head articular cartilage at 4 months of age showed some distinct differences, with increased deposition of cartilage intermediate layer protein 1 and fibronectin in the chondroadherin deficient mice, more pronounced in the female. Other proteins show decreased levels in the deficient mice, particularly pronounced for matrilin-1, thrombospondin-1 and notably the members of the a1-antitrypsin family of proteinase inhibitors as well as for a member of the bone morphogenetic protein growth factor family. Thus, cartilage homeostasis is distinctly altered. The bone phenotype was expressed in several ways. The number of bone sialoprotein mRNA expressing cells in the proximal tibial metaphysic was decreased and the osteoid surface was increased possibly indicating a change in mineral metabolism. Micro-CT revealed lower cortical thickness and increased structure model index, i.e. the amount of plates and rods composing the bone trabeculas. The structural changes were paralleled by loss of function, where the null mice showed lower femoral neck failure load and tibial strength during mechanical testing at 4 months of age. The skeletal phenotype points at a role for chondroadherin in both bone and cartilage homeostasis, however, without leading to altered longitudinal growth

Locomotor changes in length and EMG activity of feline medial gastrocnemius muscle following paralysis of two synergists

The mechanism of the compensatory increase in electromyographic activity (EMG) of a cat ankle extensor during walking shortly after paralysis of its synergists is not fully understood. It is possible that due to greater ankle flexion in stance in this situation, muscle spindles are stretched to a greater extent and, thus, contribute to the EMG enhancement. However, also changes in force feedback and central drive may play a role. The aim of the present study was to investigate the short-term (1- to 2-week post-op) effects of lateral gastrocnemius (LG) and soleus (SO) denervation on muscle fascicle and muscle–tendon unit (MTU) length changes, as well as EMG activity of the intact medial gastrocnemius (MG) muscle in stance during overground walking on level (0%), downslope (−50%, presumably enhancing stretch of ankle extensors in stance) and upslope (+50%, enhancing load on ankle extensors) surfaces. Fascicle length was measured directly using sonomicrometry, and MTU length was calculated from joint kinematics. For each slope condition, LG-SO denervation resulted in an increase in MTU stretch and peak stretch velocity of the intact MG in early stance. MG muscle fascicle stretch and peak stretch velocity were also higher than before denervation in downslope walking. Denervation significantly decreased the magnitude of MG fascicle shortening and peak shortening velocity during early stance in level and upslope walking. MG EMG magnitude in the swing and stance phases was substantially greater after denervation, with a relatively greater increase during stance of level and upslope walking. These results suggest that the fascicle length patterns of MG muscle are significantly altered when two of its synergists are in a state of paralysis. Further, the compensatory increase in MG EMG is likely mediated by enhanced MG length feedback during downslope walking, enhanced feedback from load-sensitive receptors during upslope walking and enhanced central drive in all walking conditions

Design of a Microsphere-Based High-Throughput Gene Expression Assay to Determine Estrogenic Potential

Recently gene expression studies have been multiplied at an accelerated rate by the use of high-density microarrays. By assaying thousands of transcripts at a time, microarrays have led to the discovery of dozens of genes involved in particular biochemical processes, for example, the response of a tissue/organ to a given chemical with therapeutic or toxic properties. The next step in these studies is to focus on the response of a subset of relevant genes to verify or refine potential therapeutic or toxic properties. We have developed a sensitive, high-throughput gene expression assay for this purpose. In this assay, based on the Luminex xMAP system, carefully selected oligonucleotides were covalently linked to fluorescently coded microspheres that are hybridized to biotinylated cRNA followed by amplification of the signal, which results in a rapid, sensitive, multiplexed assay platform. Using this system, we have developed an RNA expression profiling assay specific for 17 estrogen-responsive transcripts and three controls. This assay can evaluate up to 100 distinct analytes simultaneously in a single sample, in a 96-well plate format. This system has improved sensitivity versus existing microsphere-based assays and has sensitivity and precision comparable with or better than microarray technology. We have achieved detection levels down to 1 amol, detecting rare messages in complex cRNA samples, using as little as 2.5 μg starting cRNA. This assay offers increased throughput with decreased costs compared with existing microarray technologies, with the trade-off being in the total number of transcripts that can be analyzed

Detecting undiagnosed atrial fibrillation in UK primary care: Validation of a machine learning prediction algorithm in a retrospective cohort study

Aims To evaluate the ability of a machine learning algorithm to identify patients at high risk of atrial fibrillation in primary care. Methods A retrospective cohort study was undertaken using the DISCOVER registry to validate an algorithm developed using a Clinical Practice Research Datalink (CPRD) dataset. The validation dataset included primary care patients in London, England aged ≥30 years from 1 January 2006 to 31 December 2013, without a diagnosis of atrial fibrillation in the prior 5 years. Algorithm performance metrics were sensitivity, specificity, positive predictive value, negative predictive value (NPV) and number needed to screen (NNS). Subgroup analysis of patients aged ≥65 years was also performed. Results Of 2,542,732 patients in DISCOVER, the algorithm identified 604,135 patients suitable for risk assessment. Of these, 3.0% (17,880 patients) had a diagnosis of atrial fibrillation recorded before study end. The area under the curve of the receiver operating characteristic was 0.87, compared with 0.83 in algorithm development. The NNS was nine patients, matching the CPRD cohort. In patients aged ≥30 years, the algorithm correctly identified 99.1% of patients who did not have atrial fibrillation (NPV) and 75.0% of true atrial fibrillation cases (sensitivity). Among patients aged ≥65 years (n = 117,965), the NPV was 96.7% with 91.8% sensitivity. Conclusions This atrial fibrillation risk prediction algorithm, based on machine learning methods, identified patients at highest risk of atrial fibrillation. It performed comparably in a large, real-world population-based cohort and the developmental registry cohort. If implemented in primary care, the algorithm could be an effective tool for narrowing the population who would benefit from atrial fibrillation screening in the United Kingdom

Identification of undiagnosed atrial fibrillation patients using a machine learning risk prediction algorithm and diagnostic testing (PULsE-AI): Study protocol for a randomised controlled trial.

Atrial fibrillation (AF) is associated with an increased risk of stroke, enhanced stroke severity, and other comorbidities. However, AF is often asymptomatic, and frequently remains undiagnosed until complications occur. Current screening approaches for AF lack either cost-effectiveness or diagnostic sensitivity; thus, there is interest in tools that could be used for population screening. An AF risk prediction algorithm, developed using machine learning from a UK dataset of 2,994,837 patients, was found to be more effective than existing models at identifying patients at risk of AF. Therefore, the aim of the trial is to assess the effectiveness of this risk prediction algorithm combined with diagnostic testing for the identification of AF in a real-world primary care setting. Eligible participants (aged ≥30 years and without an existing AF diagnosis) registered at participating UK general practices will be randomised into intervention and control arms. Intervention arm participants identified at highest risk of developing AF (algorithm risk score ≥ 7.4%) will be invited for a 12‑lead electrocardiogram (ECG) followed by two-weeks of home-based ECG monitoring with a KardiaMobile device. Control arm participants will be used for comparison and will be managed routinely. The primary outcome is the number of AF diagnoses in the intervention arm compared with the control arm during the research window. If the trial is successful, there is potential for the risk prediction algorithm to be implemented throughout primary care for narrowing the population considered at highest risk for AF who could benefit from more intensive screening for AF. Trial Registration: NCT04045639