'Tipping the Balance': Karl Friedrich Meyer, Latent Infections, and the Birth of Modern Ideas of Disease Ecology

The Swiss-born medical researcher Karl Friedrich Meyer (1884–1974) is best known as a ‘microbe hunter’ who pioneered investigations into diseases at the intersection of animal and human health in California in the 1920s and 1930s. In particular, historians have singled out Meyer’s 1931 Ludwig Hektoen Lecture in which he described the animal kingdom as a ‘reservoir of disease’ as a forerunner of ‘one medicine’ approaches to emerging zoonoses. In so doing, however, historians risk overlooking Meyer’s other intellectual contributions. Developed in a series of papers from the mid-1930s onwards, these were ordered around the concept of latent infections and sought to link microbial behavior to broader bio-ecological, environmental, and social factors that impact hostpathogen interactions. In this respect Meyer—like the comparative pathologist Theobald Smith and the immunologist Frank Macfarlane Burnet—can be seen as a pioneer of modern ideas of disease ecology. However, while Burnet’s and Smith’s contributions to this scientific field have been widely acknowledged, Meyer’s have been largely ignored. Drawing on Meyer’s published writings and private correspondence, this paper aims to correct that lacuna while contributing to a reorientation of the historiography of bacteriological epidemiology. In particular I trace Meyer’s intellectual exchanges with Smith, Burnet and the animal ecologist Charles Elton, over brucellosis, psittacosis and plague—exchanges that not only showed how environmental and ecological conditions could ‘tip the balance’ in favor of parasites but which transformed Meyer thinking about resistance to infection and disease

Cardiac magnetic resonance imaging in stable ischaemic heart disease

Cardiac magnetic resonance imaging (CMR) is a new robust versatile non-invasive imaging technique that can detect global and regional myocardial dysfunction, presence of myocardial ischaemia and myocardial scar tissue in one imaging session without radiation, with superb spatial and temporal resolution, inherited three-dimensional data collection and with relatively safe contrast material. The reproducibility of CMR is high which makes it possible to use this technique for serial assessment to evaluate the effect of revascularisation therapy in patients with ischaemic heart disease

Assessment of B Cell Repertoire in Humans

The B cell receptor (BCR) repertoire is highly diverse. Repertoire diversity is achieved centrally by somatic recombination of immunoglobulin (Ig) genes and peripherally by somatic hypermutation and Ig heavy chain class-switching. Throughout these processes, there is selection for functional gene rearrangements, selection against gene combinations resulting in self-reactive BCRs, and selection for BCRs with high affinity for exogenous antigens after challenge. Hence, investigation of BCR repertoires from different groups of B cells can provide information on stages of B cell development and shed light on the etiology of B cell pathologies. In most instances, the third complementarity determining region of the Ig heavy chain (CDR-H3) contributes the majority of amino acids to the antibody/antigen binding interface. Although CDR-H3 spectratype analysis provides information on the overall diversity of BCR repertoires, this fairly simple technique analyzes the relative quantities of CDR-H3 regions of each size, within a range of approximately 10–80 bp, without sequence detail and thus is limited in scope. High-throughput sequencing (HTS) techniques on the Roche 454 GS FLX Titanium system, however, can generate a wide coverage of Ig sequences to provide more qualitative data such as V, D, and J usage as well as detailed CDR3 sequence information. Here we present protocols in detail for CDR-H3 spectratype analysis and HTS of human BCR repertoires

Early Alterations in Hippocampal Circuitry and Theta Rhythm Generation in a Mouse Model of Prenatal Infection: Implications for Schizophrenia

Post-mortem studies suggest that GABAergic neurotransmission is impaired in schizophrenia. However, it remains unclear if these changes occur early during development and how they impact overall network activity. To investigate this, we used a mouse model of prenatal infection with the viral mimic, polyriboinosinic–polyribocytidilic acid (poly I∶C), a model based on epidemiological evidence that an immune challenge during pregnancy increases the prevalence of schizophrenia in the offspring. We found that prenatal infection reduced the density of parvalbumin- but not somatostatin-positive interneurons in the CA1 area of the hippocampus and strongly reduced the strength of inhibition early during postnatal development. Furthermore, using an intact hippocampal preparation in vitro, we found reduced theta oscillation generated in the CA1 area. Taken together, these results suggest that redistribution in excitatory and inhibitory transmission locally in the CA1 is associated with a significant alteration in network function. Furthermore, given the role of theta rhythm in memory, our results demonstrate how a risk factor for schizophrenia can affect network function early in development that could contribute to cognitive deficits observed later in the disease

Inference of gene regulatory networks from time series by Tsallis entropy

Background: The inference of gene regulatory networks (GRNs) from large-scale expression profiles is one of the most challenging problems of Systems Biology nowadays. Many techniques and models have been proposed for this task. However, it is not generally possible to recover the original topology with great accuracy, mainly due to the short time series data in face of the high complexity of the networks and the intrinsic noise of the expression measurements. In order to improve the accuracy of GRNs inference methods based on entropy (mutual information), a new criterion function is here proposed. Results: In this paper we introduce the use of generalized entropy proposed by Tsallis, for the inference of GRNs from time series expression profiles. The inference process is based on a feature selection approach and the conditional entropy is applied as criterion function. In order to assess the proposed methodology, the algorithm is applied to recover the network topology from temporal expressions generated by an artificial gene network (AGN) model as well as from the DREAM challenge. The adopted AGN is based on theoretical models of complex networks and its gene transference function is obtained from random drawing on the set of possible Boolean functions, thus creating its dynamics. On the other hand, DREAM time series data presents variation of network size and its topologies are based on real networks. The dynamics are generated by continuous differential equations with noise and perturbation. By adopting both data sources, it is possible to estimate the average quality of the inference with respect to different network topologies, transfer functions and network sizes. Conclusions: A remarkable improvement of accuracy was observed in the experimental results by reducing the number of false connections in the inferred topology by the non-Shannon entropy. The obtained best free parameter of the Tsallis entropy was on average in the range 2.5 <= q <= 3.5 (hence, subextensive entropy), which opens new perspectives for GRNs inference methods based on information theory and for investigation of the nonextensivity of such networks. The inference algorithm and criterion function proposed here were implemented and included in the DimReduction software, which is freely available at http://sourceforge.net/projects/dimreduction and http://code.google.com/p/dimreduction/.Fundacao de Amparo e Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)Coordenacao de Aperfeicofamento de Pessoal de Nivel Superior (CAPES)Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq

Functional Connectivity Analyses in Imaging Genetics: Considerations on Methods and Data Interpretation

Functional magnetic resonance imaging (fMRI) can be combined with genotype assessment to identify brain systems that mediate genetic vulnerability to mental disorders (“imaging genetics”). A data analysis approach that is widely applied is “functional connectivity”. In this approach, the temporal correlation between the fMRI signal from a pre-defined brain region (the so-called “seed point”) and other brain voxels is determined. In this technical note, we show how the choice of freely selectable data analysis parameters strongly influences the assessment of the genetic modulation of connectivity features. In our data analysis we exemplarily focus on three methodological parameters: (i) seed voxel selection, (ii) noise reduction algorithms, and (iii) use of additional second level covariates. Our results show that even small variations in the implementation of a functional connectivity analysis can have an impact on the connectivity pattern that is as strong as the potential modulation by genetic allele variants. Some effects of genetic variation can only be found for one specific implementation of the connectivity analysis. A reoccurring difficulty in the field of psychiatric genetics is the non-replication of initially promising findings, partly caused by the small effects of single genes. The replication of imaging genetic results is therefore crucial for the long-term assessment of genetic effects on neural connectivity parameters. For a meaningful comparison of imaging genetics studies however, it is therefore necessary to provide more details on specific methodological parameters (e.g., seed voxel distribution) and to give information how robust effects are across the choice of methodological parameters

Influence of Ecto-Nucleoside Triphosphate Diphosphohydrolase Activity on Trypanosoma cruzi Infectivity and Virulence

The protozoan Trypanosoma cruzi is the causative agent of Chagas disease, an endemic zoonosis present in some countries of South and Central Americas. The World Health Organization estimates that 100 million people are at risk of acquiring this disease. The infection affects mainly muscle tissues in the heart and digestive tract. There are no vaccines or effective treatment, especially in the chronic phase when most patients are diagnosed, which makes a strong case for the development of new drugs to treat the disease. In this work we evaluate a family of proteins called Ecto-Nucleoside-Triphosphate-Diphosphohydrolase (Ecto-NTPDase) as new chemotherapy target to block T. cruzi infection in mammalian cells and in mice. We have used inhibitors and antibodies against this protein and demonstrated that T. cruzi Ecto-NTPDases act as facilitators of infection in mammalian cells and virulence factors in mice model. Two of the drugs used in this study (Suramin and Gadolinium) are currently used for other diseases in humans, supporting the possibility of their use in the treatment of Chagas disease