A Call for Gene Expression Analysis in Whole Blood of Patients With Rheumatoid Arthritis (RA) as a Biomarker for RA-Associated Interstitial Lung Disease.

OBJECTIVE: Rheumatoid arthritis (RA)-associated interstitial lung disease (ILD) is one of the most common and prognostic organ manifestations of RA. Therefore, to allow effective treatment, it is of crucial importance to diagnose RA-ILD at the earliest possible stage. So far, the gold standard of early detection has been high-resolution computed tomography (HRCT) of the lungs. This procedure involves considerable radiation exposure for the patient and is therefore unsuitable as a routine screening measure for ethical reasons. Here, we propose the analysis of characteristic gene expression patterns as a biomarker to aid in the early detection and initiation of appropriate, possibly antifibrotic, therapy. METHODS: To investigate unique molecular patterns of RA-ILD, whole blood samples were taken from 12 female patients with RA-ILD (n = 7) or RA (n = 5). The RNA was extracted, sequenced by RNA-Seq, and analyzed for characteristic differences in the gene expression patterns between patients with RA-ILD and those with RA without ILD. RESULTS: The differential gene expression analysis revealed 9 significantly upregulated genes in RA-ILD compared to RA without ILD: arginase 1 (ARG1), thymidylate synthetase (TYMS), sortilin 1 (SORT1), marker of proliferation Ki-67 (MKI67), olfactomedin 4 (OLFM4), baculoviral inhibitor of apoptosis repeat containing 5 (BIRC5), membrane spanning 4-domains A4A (MS4A4A), C-type lectin domain family 12 member A (CLEC12A), and the long intergenic nonprotein coding RNA (LINC02967). CONCLUSION: All gene products of these genes (except for LINC02967) are known from the literature to be involved in the pathogenesis of fibrosis. Further, for some, a contribution to the development of pulmonary fibrosis has even been demonstrated in experimental studies. Therefore, the results presented here provide an encouraging perspective for using specific gene expression patterns as biomarkers for the early detection and differential diagnosis of RA-ILD as a routine screening test.

N1-methylation of adenosine (m1A) in ND5 mRNA leads to complex I dysfunction in Alzheimer's disease.

One mechanism of particular interest to regulate mRNA fate post-transcriptionally is mRNA modification. Especially the extent of m1A mRNA methylation is highly discussed due to methodological differences. However, one single m1A site in mitochondrial ND5 mRNA was unanimously reported by different groups. ND5 is a subunit of complex I of the respiratory chain. It is considered essential for the coupling of oxidation and proton transport. Here we demonstrate that this m1A site might be involved in the pathophysiology of Alzheimer's disease (AD). One of the pathological hallmarks of this neurodegenerative disease is mitochondrial dysfunction, mainly induced by Amyloid ß (Aß). Aß mainly disturbs functions of complex I and IV of the respiratory chain. However, the molecular mechanism of complex I dysfunction is still not fully understood. We found enhanced m1A methylation of ND5 mRNA in an AD cell model as well as in AD patients. Formation of this m1A methylation is catalyzed by increased TRMT10C protein levels, leading to translation repression of ND5. As a consequence, here demonstrated for the first time, TRMT10C induced m1A methylation of ND5 mRNA leads to mitochondrial dysfunction. Our findings suggest that this newly identified mechanism might be involved in Aß-induced mitochondrial dysfunction.

Low-Cost Probabilistic 3D Denoising with Applications for Ultra-Low-Radiation Computed Tomography.

We propose a pipeline for synthetic generation of personalized Computer Tomography (CT) images, with a radiation exposure evaluation and a lifetime attributable risk (LAR) assessment. We perform a patient-specific performance evaluation for a broad range of denoising algorithms (including the most popular deep learning denoising approaches, wavelets-based methods, methods based on Mumford−Shah denoising, etc.), focusing both on accessing the capability to reduce the patient-specific CT-induced LAR and on computational cost scalability. We introduce a parallel Probabilistic Mumford−Shah denoising model (PMS) and show that it markedly-outperforms the compared common denoising methods in denoising quality and cost scaling. In particular, we show that it allows an approximately 22-fold robust patient-specific LAR reduction for infants and a 10-fold LAR reduction for adults. Using a normal laptop, the proposed algorithm for PMS allows cheap and robust (with a multiscale structural similarity index >90%) denoising of very large 2D videos and 3D images (with over 107 voxels) that are subject to ultra-strong noise (Gaussian and non-Gaussian) for signal-to-noise ratios far below 1.0. The code is provided for open access.

N6 -Methyladenosine Modification in Chronic Stress Response Due to Social Hierarchy Positioning of Mice.

Appropriately responding to stressful events is essential for maintaining health and well-being of any organism. Concerning social stress, the response is not always as straightforward as reacting to physical stressors, e.g., extreme heat, and thus has to be balanced subtly. Particularly, regulatory mechanisms contributing to gaining resilience in the face of mild social stress are not fully deciphered yet. We employed an intrinsic social hierarchy stress paradigm in mice of both sexes to identify critical factors for potential coping strategies. While global transcriptomic changes could not be observed in male mice, several genes previously reported to be involved in synaptic plasticity, learning, and anxiety-like behavior were differentially regulated in female mice. Moreover, changes in N6-methyladenosine (m6A)-modification of mRNA occurred associated with corticosterone level in both sexes with, e.g., increased global amount in submissive female mice. In accordance with this, METTL14 and WTAP, subunits of the methyltransferase complex, showed elevated levels in submissive female mice. N6-adenosyl-methylation is the most prominent type of mRNA methylation and plays a crucial role in processes such as metabolism, but also response to physical stress. Our findings underpin its essential role by also providing a link to social stress evoked by hierarchy building within same-sex groups. As recently, search for small molecule modifiers for the respective class of RNA modifying enzymes has started, this might even lead to new therapeutic approaches against stress disorders.

Voluntary Wheel Running Did Not Alter Gene Expression in 5xfad Mice, but in Wild-Type Animals Exclusively after One-Day of Physical Activity.

Physical activity is considered a promising preventive intervention to reduce the risk of developing Alzheimer's disease (AD). However, the positive effect of therapeutic administration of physical activity has not been proven conclusively yet, likely due to confounding factors such as varying activity regimens and life or disease stages. To examine the impact of different routines of physical activity in the early disease stages, we subjected young 5xFAD and wild-type mice to 1-day (acute) and 30-day (chronic) voluntary wheel running and compared them with age-matched sedentary controls. We observed a significant increase in brain lactate levels in acutely trained 5xFAD mice relative to all other experimental groups. Subsequent brain RNA-seq analysis did not reveal major differences in transcriptomic regulation between training durations in 5xFAD mice. In contrast, acute training yielded substantial gene expression changes in wild-type animals relative to their chronically trained and sedentary counterparts. The comparison of 5xFAD and wild-type mice showed the highest transcriptional differences in the chronic and sedentary groups, whereas acute training was associated with much fewer differentially expressed genes. In conclusion, our results suggest that different training durations did not affect the global transcriptome of 3-month-old 5xFAD mice, whereas acute running seemed to induce a similar transcriptional stress state in wild-type animals as already known for 5xFAD mice.

Transcriptional signatures in prefrontal cortex confer vulnerability versus resilience to food and cocaine addiction-like behavior.

Addiction is a chronic relapsing brain disease characterized by compulsive reward-seeking despite harmful consequences. The mechanisms underlying addiction are orchestrated by transcriptional reprogramming in the reward system of vulnerable subjects. This study aims at revealing gene expression alterations across different types of addiction. We analyzed publicly available transcriptome datasets of the prefrontal cortex (PFC) from a palatable food and a cocaine addiction study. We found 56 common genes upregulated in the PFC of addicted mice in these two studies, whereas most of the differentially expressed genes were exclusively linked to either palatable food or cocaine addiction. Gene ontology analysis of shared genes revealed that these genes contribute to learning and memory, dopaminergic synaptic transmission, and histone phosphorylation. Network analysis of shared genes revealed a protein-protein interaction node among the G protein-coupled receptors (Drd2, Drd1, Adora2a, Gpr6, Gpr88) and downstream targets of the cAMP signaling pathway (Ppp1rb1, Rgs9, Pde10a) as a core network in addiction. Upon extending the analysis to a cell-type specific level, some of these common molecular players were selectively expressed in excitatory neurons, oligodendrocytes, and endothelial cells. Overall, computational analysis of publicly available whole transcriptome datasets provides new insights into the molecular basis of addiction-like behaviors in PFC.

Co-Inference of Data Mislabelings Reveals Improved Models in Genomics and Breast Cancer Diagnostics.

Mislabeling of cases as well as controls in case-control studies is a frequent source of strong bias in prognostic and diagnostic tests and algorithms. Common data processing methods available to the researchers in the biomedical community do not allow for consistent and robust treatment of labeled data in the situations where both, the case and the control groups, contain a non-negligible proportion of mislabeled data instances. This is an especially prominent issue in studies regarding late-onset conditions, where individuals who may convert to cases may populate the control group, and for screening studies that often have high false-positive/-negative rates. To address this problem, we propose a method for a simultaneous robust inference of Lasso reduced discriminative models and of latent group-specific mislabeling risks, not requiring any exactly labeled data. We apply it to a standard breast cancer imaging dataset and infer the mislabeling probabilities (being rates of false-negative and false-positive core-needle biopsies) together with a small set of simple diagnostic rules, outperforming the state-of-the-art BI-RADS diagnostics on these data. The inferred mislabeling rates for breast cancer biopsies agree with the published purely empirical studies. Applying the method to human genomic data from a healthy-ageing cohort reveals a previously unreported compact combination of single-nucleotide polymorphisms that are strongly associated with a healthy-ageing phenotype for Caucasians. It determines that 7.5% of Caucasians in the 1000 Genomes dataset (selected as a control group) carry a pattern characteristic of healthy ageing.

The Microbiota Promotes Arterial Thrombosis in Low-Density Lipoprotein Receptor-Deficient Mice.

Atherosclerotic plaque development depends on chronic inflammation of the arterial wall. A dysbiotic gut microbiota can cause low-grade inflammation, and microbiota composition was linked to cardiovascular disease risk. However, the role of this environmental factor in atherothrombosis remains undefined. To analyze the impact of gut microbiota on atherothrombosis, we rederived low-density lipoprotein receptor-deficient (Ldlr-/- ) mice as germfree (GF) and kept these mice for 16 weeks on an atherogenic high-fat Western diet (HFD) under GF isolator conditions and under conventionally raised specific-pathogen-free conditions (CONV-R). In spite of reduced diversity of the cecal gut microbiome, caused by atherogenic HFD, GF Ldlr-/- mice and CONV-R Ldlr-/- mice exhibited atherosclerotic lesions of comparable sizes in the common carotid artery. In contrast to HFD-fed mice, showing no difference in total cholesterol levels, CONV-R Ldlr-/- mice fed control diet (CD) had significantly reduced total plasma cholesterol, very-low-density lipoprotein (VLDL), and LDL levels compared with GF Ldlr-/- mice. Myeloid cell counts in blood as well as leukocyte adhesion to the vessel wall at the common carotid artery of GF Ldlr-/- mice on HFD were diminished compared to CONV-R Ldlr-/- controls. Plasma cytokine profiling revealed reduced levels of the proinflammatory chemokines CCL7 and CXCL1 in GF Ldlr-/- mice, whereas the T-cell-related interleukin 9 (IL-9) and IL-27 were elevated. In the atherothrombosis model of ultrasound-induced rupture of the common carotid artery plaque, thrombus area was significantly reduced in GF Ldlr-/- mice relative to CONV-R Ldlr-/- mice. Ex vivo, this atherothrombotic phenotype was explained by decreased adhesion-dependent platelet activation and thrombus growth of HFD-fed GF Ldlr-/- mice on type III collagen.IMPORTANCE Our results demonstrate a functional role for the commensal microbiota in atherothrombosis. In a ferric chloride injury model of the carotid artery, GF C57BL/6J mice had increased occlusion times compared to colonized controls. Interestingly, in late atherosclerosis, HFD-fed GF Ldlr-/- mice had reduced plaque rupture-induced thrombus growth in the carotid artery and diminished ex vivo thrombus formation under arterial flow conditions.

Common miRNA Patterns of Alzheimer's Disease and Parkinson's Disease and Their Putative Impact on Commensal Gut Microbiota.

With the rise of Next-Generation-Sequencing (NGS) methods, Micro-RNAs (miRNAs) have achieved an important position in the research landscape and have been found to present valuable diagnostic tools in various diseases such as multiple sclerosis or lung cancer. There is also emerging evidence that miRNAs play an important role in the pathogenesis of neurodegenerative diseases such as Alzheimer's disease (AD) or Parkinson's disease (PD). Apparently, these diseases come along with changes in miRNA expression patterns which led to attempts from researchers to use these small RNA species from several body fluids for a better diagnosis and in order to observe disease progression. Additionally, it became evident that microbial commensals might play an important role for pathology development and were shown to have a significantly different composition in patients suffering from neurodegeneration compared with healthy controls. As it could recently be shown that secreted miRNAs are able to enter microbial organisms, it is conceivable that the host's miRNA might affect the gut microbial ecosystem. As such, miRNAs may inherit a central role in shaping the "diseased microbiome" and thereby mutually act on the characteristics of these neurodegenerative diseases. We have therefore (1) compiled a list of miRNAs known to be associated with AD and/or PD, (2) performed an in silico target screen for binding sites of these miRNA on human gut metagenome sequences and (3) evaluated the hit list for interesting matches potentially relevant to the etiology of AD and or PD. The examination of protein identifiers connected to bacterial secretion system, lipopolysaccharide biosynthesis and biofilm formation revealed an overlap of 37 bacterial proteins that were targeted by human miRNAs. The identified links of miRNAs to the biological processes of bacteria connected to AD and PD have yet to be validated via in vivo experiments. However, our results show a promising new approach for understanding aspects of these neurodegenerative diseases in light of the regulation of the microbiome.

Toward a direct and scalable identification of reduced models for categorical processes.

The applicability of many computational approaches is dwelling on the identification of reduced models defined on a small set of collective variables (colvars). A methodology for scalable probability-preserving identification of reduced models and colvars directly from the data is derived-not relying on the availability of the full relation matrices at any stage of the resulting algorithm, allowing for a robust quantification of reduced model uncertainty and allowing us to impose a priori available physical information. We show two applications of the methodology: (i) to obtain a reduced dynamical model for a polypeptide dynamics in water and (ii) to identify diagnostic rules from a standard breast cancer dataset. For the first example, we show that the obtained reduced dynamical model can reproduce the full statistics of spatial molecular configurations-opening possibilities for a robust dimension and model reduction in molecular dynamics. For the breast cancer data, this methodology identifies a very simple diagnostics rule-free of any tuning parameters and exhibiting the same performance quality as the state of the art machine-learning applications with multiple tuning parameters reported for this problem.

On inference of causality for discrete state models in a multiscale context.

Discrete state models are a common tool of modeling in many areas. E.g., Markov state models as a particular representative of this model family became one of the major instruments for analysis and understanding of processes in molecular dynamics (MD). Here we extend the scope of discrete state models to the case of systematically missing scales, resulting in a nonstationary and nonhomogeneous formulation of the inference problem. We demonstrate how the recently developed tools of nonstationary data analysis and information theory can be used to identify the simultaneously most optimal (in terms of describing the given data) and most simple (in terms of complexity and causality) discrete state models. We apply the resulting formalism to a problem from molecular dynamics and show how the results can be used to understand the spatial and temporal causality information beyond the usual assumptions. We demonstrate that the most optimal explanation for the appropriately discretized/coarse-grained MD torsion angles data in a polypeptide is given by the causality that is localized both in time and in space, opening new possibilities for deploying percolation theory and stochastic subgridscale modeling approaches in the area of MD.