Polyphenism – A Window Into Gene-Environment Interactions and Phenotypic Plasticity

Phenotypic plasticity describes the capacity of a single genotype to exhibit a variety of phenotypes as well as the mechanisms that translate environmental variation into reproducible phenotypic modifications. Polyphenism describes the unique sub-type of phenotypic plasticity where the outputs are not continuous, but rather discrete and multi-stable, resulting in several distinct phenotypes on the same genetic background. Epigenetic regulation underpins the stable phenotypic divergences that exemplify polyphenism and their evolutionary origin. Here, we briefly summarize the apparent ubiquity and diversity of polyphenisms across the animal kingdom. We briefly review the best characterized models across taxa and highlight the consistent themes both in their epidemiology and what little we know about molecular mechanisms. Finally, we highlight work that supports the possibility that humans may have a subtle polyphenism at the level of metabolism

Bacterial Butyrate in Parkinson's Disease Is Linked to Epigenetic Changes and Depressive Symptoms

Background The gut microbiome and its metabolites can impact brain health and are altered in Parkinson's disease (PD) patients. It has been recently demonstrated that PD patients have reduced fecal levels of the potent epigenetic modulator butyrate and its bacterial producers. Objectives Here, we investigate whether the changes in the gut microbiome and associated metabolites are related to PD symptoms and epigenetic markers in leucocytes and neurons. Methods Stool, whole blood samples, and clinical data were collected from 55 PD patients and 55 controls. We performed DNA methylation analysis on whole blood samples and analyzed the results in relation to fecal short-chain fatty acid concentrations and microbiota composition. In another cohort, prefrontal cortex neurons were isolated from control and PD brains. We identified genome-wide DNA methylation by targeted bisulfite sequencing. Results We show that lower fecal butyrate and reduced counts of genera Roseburia, Romboutsia, and Prevotella are related to depressive symptoms in PD patients. Genes containing butyrate-associated methylation sites include PD risk genes and significantly overlap with sites epigenetically altered in PD blood leucocytes, predominantly neutrophils, and in brain neurons, relative to controls. Moreover, butyrate-associated methylated-DNA regions in PD overlap with those altered in gastrointestinal (GI), autoimmune, and psychiatric diseases. Conclusions Decreased levels of bacterially produced butyrate are related to epigenetic changes in leucocytes and neurons from PD patients and to the severity of their depressive symptoms. PD shares common butyrate-dependent epigenetic changes with certain GI and psychiatric disorders, which could be relevant for their epidemiological relation. (c) 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder SocietyPeer reviewe

Trim28 Haploinsufficiency Triggers Bi-stable Epigenetic Obesity.

This is the final version of the article. It first appeared from Cell Press via http://dx.doi.org/10.1016/j.cell.2015.12.025More than one-half billion people are obese, and despite progress in genetic research, much of the heritability of obesity remains enigmatic. Here, we identify a Trim28-dependent network capable of triggering obesity in a non-Mendelian, "on/off" manner. Trim28(+/D9) mutant mice exhibit a bi-modal body-weight distribution, with isogenic animals randomly emerging as either normal or obese and few intermediates. We find that the obese-"on" state is characterized by reduced expression of an imprinted gene network including Nnat, Peg3, Cdkn1c, and Plagl1 and that independent targeting of these alleles recapitulates the stochastic bi-stable disease phenotype. Adipose tissue transcriptome analyses in children indicate that humans too cluster into distinct sub-populations, stratifying according to Trim28 expression, transcriptome organization, and obesity-associated imprinted gene dysregulation. These data provide evidence of discrete polyphenism in mouse and man and thus carry important implications for complex trait genetics, evolution, and medicine.This work was supported by funding from the Max-Planck Society, ERC (ERC-StG-281641), DFG (SFB992 “MedEp”; SFB 1052 “ObesityMechanisms”), EU_FP7 (NoE ”Epigenesys”; “Beta-JUDO” n° 279153), BMBF (DEEP), MRC (Metabolic Disease Unit - APC, SOR, GSHY, MRC_MC_UU_12012/1), Wellcome Trust (SOR, 095515/Z/11/Z) and the German Research Council (DFG) for the Clinical Research Center "Obesity Mechanisms" CRC1052/1 C05 and the Federal Ministry of Education and Research, Germany, FKZ, 01EO1001 (Integrated Research and Treatment Center (IFB) Adiposity Diseases

New loci for body fat percentage reveal link between adiposity and cardiometabolic disease risk

To increase our understanding of the genetic basis of adiposity and its links to cardiometabolic disease risk, we conducted a genome-wide association meta-analysis of body fat percentage (BF%) in up to 100,716 individuals. Twelve loci reached genome-wide significance (P<5 × 10−8), of which eight were previously associated with increased overall adiposity (BMI, BF%) and four (in or near COBLL1/GRB14, IGF2BP1, PLA2G6, CRTC1) were novel associations with BF%. Seven loci showed a larger effect on BF% than on BMI, suggestive of a primary association with adiposity, while five loci showed larger effects on BMI than on BF%, suggesting association with both fat and lean mass. In particular, the loci more strongly associated with BF% showed distinct cross-phenotype association signatures with a range of cardiometabolic traits revealing new insights in the link between adiposity and disease risk

New loci for body fat percentage reveal link between adiposity and cardiometabolic disease risk

To increase our understanding of the genetic basis of adiposity and its links to cardiometabolic disease risk, we conducted a genome-wide association meta-analysis of body fat percentage (BF%) in up to 100,716 individuals. Twelve loci reached genome-wide significance (P <5 x 10(-8)), of which eight were previously associated with increased overall adiposity (BMI, BF%) and four (in or near COBLL1/GRB14, IGF2BP1, PLA2G6, CRTC1) were novel associations with BF%. Seven loci showed a larger effect on BF% than on BMI, suggestive of a primary association with adiposity, while five loci showed larger effects on BMI than on BF%, suggesting association with both fat and lean mass. In particular, the loci more strongly associated with BF% showed distinct cross-phenotype association signatures with a range of cardiometabolic traits revealing new insights in the link between adiposity and disease risk.Peer reviewe

Regulation of glucose homeostasis by Dipeptidyl Peptidase IV : studies on counter-regulation and long-term inhibition as diabetes therapy

The ubiquitous serine protease dipeptidyl peptidase IV (DP IV) plays a number of physiological roles including hormone inactivation and immune costimulation. Recent attention has been brought to the molecule because of its ability to cleave and inactivate the potent insulin secretagogues (incretins) glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide-1 (GLP-1). Use of specific DP IV-inhibitors has been shown to protect the active form of these hormones in the circulation thus enhancing their effects at target tissues. In the following study we show first, that the counterregulatory hormone glucagon serves as a physiologically relevant substrate for DP IV. Also, using a model of obesity-related diabetes (fa/fa Zucker rat) and a combination of techniques (oral glucose tolerance testing, pancreas perfusion, immunohistochemistry, and euglycemic-hyperinsulinemic clamp), we show that long-term DP IV-inhibitor (P32/98) therapy ameliorates the type-2 diabetic syndrome through enhancement of both insulin sensitivity and P-cell function. Further, we reveal the applicability of long-term DP IV-inhibitor treatment towards type-1 diabetes. In a model of insulin insufficiency and post-traumatic islet plasticity (streptozotocin-induced diabetic rat), marked improvements in disease severity are shown, associated with enhancement of β-cell survival and islet neogenesis. An in vitro analysis of the anti-apoptotic potential of GIP and GLP-1 implicate the two hormones mechanistically in the in vivo findings. Also, in a model of the autoimmune progression of type-1 diabetes (the BioBreeding rat) we show both a delay and partial prevention of the disease as well as improved glucose homeostasis prior to disease onset, supporting our hypothesis of combined immunosuppression and incretin enhancement and establishing for the first time the potential for DP IV-inhibition in the treatment of type-1 diabetes. In addition to a review of the literature surrounding DP IV and glucose regulation, the findings of the present study are discussed in the context of glucose counterregulation, diabetes and autoimmunity. In summary, the work presented here identifies a novel regulatory mechanism for the effects of the counterregulatory hormone glucagon, demonstrates the benefits of long-term incretin enhancement as a therapy for type-2 diabetes, and establishes DP IV-inhibition as a unique therapeutic strategy in the treatment of type-1 diabetes.Medicine, Faculty ofCellular and Physiological Sciences, Department ofGraduat

Image_1_A novel automated morphological analysis of Iba1+ microglia using a deep learning assisted model.TIF

There is growing evidence for the key role of microglial functional state in brain pathophysiology. Consequently, there is a need for efficient automated methods to measure the morphological changes distinctive of microglia functional states in research settings. Currently, many commonly used automated methods can be subject to sample representation bias, time consuming imaging, specific hardware requirements and difficulty in maintaining an accurate comparison across research environments. To overcome these issues, we use commercially available deep learning tools Aiforia® Cloud (Aifoira Inc., Cambridge, MA, United States) to quantify microglial morphology and cell counts from histopathological slides of Iba1 stained tissue sections. We provide evidence for the effective application of this method across a range of independently collected datasets in mouse models of viral infection and Parkinson’s disease. Additionally, we provide a comprehensive workflow with training details and annotation strategies by feature layer that can be used as a guide to generate new models. In addition, all models described in this work are available within the Aiforia® platform for study-specific adaptation and validation.</p

Image_2_A novel automated morphological analysis of Iba1+ microglia using a deep learning assisted model.JPEG

There is growing evidence for the key role of microglial functional state in brain pathophysiology. Consequently, there is a need for efficient automated methods to measure the morphological changes distinctive of microglia functional states in research settings. Currently, many commonly used automated methods can be subject to sample representation bias, time consuming imaging, specific hardware requirements and difficulty in maintaining an accurate comparison across research environments. To overcome these issues, we use commercially available deep learning tools Aiforia® Cloud (Aifoira Inc., Cambridge, MA, United States) to quantify microglial morphology and cell counts from histopathological slides of Iba1 stained tissue sections. We provide evidence for the effective application of this method across a range of independently collected datasets in mouse models of viral infection and Parkinson’s disease. Additionally, we provide a comprehensive workflow with training details and annotation strategies by feature layer that can be used as a guide to generate new models. In addition, all models described in this work are available within the Aiforia® platform for study-specific adaptation and validation.</p

Data_Sheet_1_A novel automated morphological analysis of Iba1+ microglia using a deep learning assisted model.ZIP

There is growing evidence for the key role of microglial functional state in brain pathophysiology. Consequently, there is a need for efficient automated methods to measure the morphological changes distinctive of microglia functional states in research settings. Currently, many commonly used automated methods can be subject to sample representation bias, time consuming imaging, specific hardware requirements and difficulty in maintaining an accurate comparison across research environments. To overcome these issues, we use commercially available deep learning tools Aiforia® Cloud (Aifoira Inc., Cambridge, MA, United States) to quantify microglial morphology and cell counts from histopathological slides of Iba1 stained tissue sections. We provide evidence for the effective application of this method across a range of independently collected datasets in mouse models of viral infection and Parkinson’s disease. Additionally, we provide a comprehensive workflow with training details and annotation strategies by feature layer that can be used as a guide to generate new models. In addition, all models described in this work are available within the Aiforia® platform for study-specific adaptation and validation.</p