Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy.

MotivationMultiple biological clocks govern a healthy pregnancy. These biological mechanisms produce immunologic, metabolomic, proteomic, genomic and microbiomic adaptations during the course of pregnancy. Modeling the chronology of these adaptations during full-term pregnancy provides the frameworks for future studies examining deviations implicated in pregnancy-related pathologies including preterm birth and preeclampsia.ResultsWe performed a multiomics analysis of 51 samples from 17 pregnant women, delivering at term. The datasets included measurements from the immunome, transcriptome, microbiome, proteome and metabolome of samples obtained simultaneously from the same patients. Multivariate predictive modeling using the Elastic Net (EN) algorithm was used to measure the ability of each dataset to predict gestational age. Using stacked generalization, these datasets were combined into a single model. This model not only significantly increased predictive power by combining all datasets, but also revealed novel interactions between different biological modalities. Future work includes expansion of the cohort to preterm-enriched populations and in vivo analysis of immune-modulating interventions based on the mechanisms identified.Availability and implementationDatasets and scripts for reproduction of results are available through: https://nalab.stanford.edu/multiomics-pregnancy/.Supplementary informationSupplementary data are available at Bioinformatics online

Immunosuppressive effect and global dysregulation of blood transcriptome in response to psychosocial stress in vervet monkeys (Chlorocebus sabaeus).

Psychosocial stressors - life events that challenge social support and relationships - represent powerful risk factors for human disease; included amongst these events are relocation, isolation and displacement. To evaluate the impact of a controlled psychosocial stressor on physiology and underlying molecular pathways, we longitudinally studied the influence of a 28-day period of quarantine on biomarkers of immune signalling, microbial translocation, glycaemic health and blood transcriptome in the wild-born vervet monkey. This event caused a coordinated, mostly transient, reduction of circulating levels of nine immune signalling molecules. These were paralleled by a massive dysregulation of blood transcriptome, including genes implicated in chronic pathologies and immune functions. Immune and inflammatory functions were enriched among the genes downregulated in response to stress. An upregulation of genes involved in blood coagulation, platelet activation was characteristic of the rapid response to stress induction. Stress also decreased neutrophils and increased CD4 + T cell proportions in blood. This model of psychosocial stress, characterised by an immune dysregulation at the transcriptomic, molecular and cellular levels, creates opportunities to uncover the underlying mechanisms of stress-related diseases with an immune component, including cardiovascular diseases and susceptibility to infections

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Application of the Sensory Contact Model for Pharmacological Studies under Simulated Clinical Conditions

The sensory contact model allows forming different psycho-pathological states (anxious depression, catalepsy, social withdrawal, pathological aggression, cognition disturbances, anhedonia, addictive states etc.) produced by repeated agonistic interactions in male mice and investigating the therapeutic and preventive properties of any drug as well as its efficiency under simulated clinical conditions. This approach can be useful for a better understanding of the drugs’ action in different stages of disease development in individuals. It is suggested that this behavioral approach and pharmacological designs may be applied for the screening of novel psychotropic drugs. 
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Why are identity disorders interesting for philosophers?

"Identity disorders" constitute a large class of psychiatric disturbances that, due to deviant forms ofself-modeling, result in dramatic changes in the patients' phenomenal experience of their own personal identity. The phenomenal experience of selfhood and transtemporal identity can vary along an extremely large number of dimensions: There are simple losses of content (for example, complete losses of proprioception, resulting in a "bodiless" state of self-consciousness, see Cole 1995, Gallagher and Cole 1995, Sacks 1998). There are also various typologies of phenomenal disintegration as in schizophrenia, in depersonalization disorders and in Dissociative Identity Disorder (DID), sometimes accompanied by multiplications of the phenomenal self within one and the same physical system. It is important to not only analyze these state-classes in terms of functional deficits or phenomenology alone, but as self-representational content as well. For instance, in the second type of cases just mentioned, we confront major redistributions of the phenomenal property of "mineness" in representational space, of what is sometimes also called the "sense of ownership". Finally, there are at least four different delusions of misidentification (DM1; namely Capgras syndrome, Frégoli syndrome, intermetamorphosis, reverse intermetamorphosis and reduplicative paramnesia). Being a philosopher, I will discuss two particular types of identity disorder in this contribution - disorders, which are of direct philosophical relevance: A specific form of DM, and the Cotard delusion. Why should philosophers do this? And why should psychiatrists care

With mouse age comes wisdom : a review and suggestions of relevant mouse models for age-related conditions

Ageing is a complex multifactorial process that results in many changes in physiological changes processes that ultimately increase susceptibility to a wide range of diseases. As such an ageing population is resulting in a pressing need for more and improved treatments across an assortment of diseases. Such treatments can come from a better understanding of the pathogenic pathways which, in turn, can be derived from models of disease. Therefore the more closely the model resembles the disease situation the more likely relevant the data will be that is generated from them. Here we review the state of knowledge of mouse models of a range of diseases and aspects of an ageing physiology that are all germane to ageing. We also give recommendations on the most common mouse models on their relevance to the clinical situations occurring in aged patients and look forward as to how research in ageing models can be carried out. As we continue to elucidate the pathophysiology of disease, often through mouse models, we also learn what is needed to refine these models. Such factors can include better models, reflecting the ageing patient population, or a better phenotypic understanding of existing models

Computational Approach to Identifying Universal Macrophage Biomarkers.

Macrophages engulf and digest microbes, cellular debris, and various disease-associated cells throughout the body. Understanding the dynamics of macrophage gene expression is crucial for studying human diseases. As both bulk RNAseq and single cell RNAseq datasets become more numerous and complex, identifying a universal and reliable marker of macrophage cell becomes paramount. Traditional approaches have relied upon tissue specific expression patterns. To identify universal biomarkers of macrophage, we used a previously published computational approach called BECC (Boolean Equivalent Correlated Clusters) that was originally used to identify conserved cell cycle genes. We performed BECC analysis using the known macrophage marker CD14 as a seed gene. The main idea behind BECC is that it uses massive database of public gene expression dataset to establish robust co-expression patterns identified using a combination of correlation, linear regression and Boolean equivalences. Our analysis identified and validated FCER1G and TYROBP as novel universal biomarkers for macrophages in human and mouse tissues

On the Road to Accurate Biomarkers for Cardiometabolic Diseases by Integrating Precision and Gender Medicine Approaches

The need to facilitate the complex management of cardiometabolic diseases (CMD) has led to the detection of many biomarkers, however, there are no clear explanations of their role in the prevention, diagnosis or prognosis of these diseases. Molecules associated with disease pathways represent valid disease surrogates and well-fitted CMD biomarkers. To address this challenge, data from multi-omics types (genomics, epigenomics, transcriptomics, proteomics, metabolomics, microbiomics, and nutrigenomics), from human and animal models, have become available. However, individual omics types only provide data on a small part of molecules involved in the complex CMD mechanisms, whereas, here, we propose that their integration leads to multidimensional data. Such data provide a better understanding of molecules related to CMD mechanisms and, consequently, increase the possibility of identifying well-fitted biomarkers. In addition, the application of gender medicine also helps to identify accurate biomarkers according to gender, facilitating a differential CMD management. Accordingly, the impact of gender differences in CMD pathophysiology has been widely demonstrated, where gender is referred to the complex interrelation and integration of sex (as a biological and functional marker of the human body) and psychological and cultural behavior (due to ethnical, social, and religious background). In this review, all these aspects are described and discussed, as well as potential limitations and future directions in this incipient field