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Background
Gut microbes influence their hosts in many ways, in particular by modulating the impact of diet. These effects have been studied most extensively in humans and mice. In this work, we used whole genome metagenomics to investigate the relationship between the gut metagenomes of dogs, humans, mice, and pigs.
Results
We present a dog gut microbiome gene catalog containing 1,247,405 genes (based on 129 metagenomes and a total of 1.9 terabasepairs of sequencing data). Based on this catalog and taxonomic abundance profiling, we show that the dog microbiome is closer to the human microbiome than the microbiome of either pigs or mice. To investigate this similarity in terms of response to dietary changes, we report on a randomized intervention with two diets (high-protein/low-carbohydrate vs. lower protein/higher carbohydrate). We show that diet has a large and reproducible effect on the dog microbiome, independent of breed or sex. Moreover, the responses were in agreement with those observed in previous human studies.
Conclusions
We conclude that findings in dogs may be predictive of human microbiome results. In particular, a novel finding is that overweight or obese dogs experience larger compositional shifts than lean dogs in response to a high-protein diet.
Despite promising clinical results in osteochondral defect repair, a recently developed bi-layered collagen/collagen-magnesium-hydroxyapatite scaffold has demonstrated less optimal subchondral bone repair. This study aimed to improve the bone repair potential of this scaffold by adsorbing bone morphogenetic protein 2 (BMP-2) and/or platelet-derived growth factor-BB (PDGF-BB) onto said scaffold. The in vitro release kinetics of BMP-2/PDGF-BB demonstrated that PDGF-BB was burst released from the collagen-only layer, whereas BMP-2 was largely retained in both layers. Cell ingrowth was enhanced by BMP-2/PDFG-BB in a bovine osteochondral defect ex vivo model. In an in vivo semi-orthotopic athymic mouse model, adding BMP-2 or PDGF-BB increased tissue repair after four weeks. After eight weeks, most defects were filled with bone tissue. To further investigate the promising effect of BMP-2, a caprine bilateral stifle osteochondral defect model was used where defects were created in weight-bearing femoral condyle and non-weight-bearing trochlear groove locations. After six months, the adsorption of BMP-2 resulted in significantly less bone repair compared with scaffold-only in the femoral condyle defects and a trend to more bone repair in the trochlear groove. Overall, the adsorption of BMP-2 onto a Col/Col-Mg-HAp scaffold reduced bone formation in weight-bearing osteochondral defects, but not in non-weight-bearing osteochondral defects.
Excitons in atomically thin transition-metal dichalcogenides (TMDs) have been established as an attractive platform to explore polaritonic physics, owing to their enormous binding energies and giant oscillator strength. Basic spectral features of exciton polaritons in TMD microcavities, thus far, were conventionally explained via two-coupled-oscillator models. This ignores, however, the impact of phonons on the polariton energy structure. Here we establish and quantify the threefold coupling between excitons, cavity photons, and phonons. For this purpose, we employ energy-momentum-resolved photoluminescence and spatially resolved coherent two-dimensional spectroscopy to investigate the spectral properties of a high-quality-factor microcavity with an embedded WSe\(_2\) van-der-Waals heterostructure at room temperature. Our approach reveals a rich multi-branch structure which thus far has not been captured in previous experiments. Simulation of the data reveals hybridized exciton-photon-phonon states, providing new physical insight into the exciton polariton system based on layered TMDs.
Low-frequency oscillatory patterns of pallidal local field potentials (LFPs) have been proposed as a physiomarker for dystonia and hold the promise for personalized adaptive deep brain stimulation. Head tremor, a low-frequency involuntary rhythmic movement typical of cervical dystonia, may cause movement artifacts in LFP signals, compromising the reliability of low-frequency oscillations as biomarkers for adaptive neurostimulation. We investigated chronic pallidal LFPs with the Percept\(^{TM}\) PC (Medtronic PLC) device in eight subjects with dystonia (five with head tremors). We applied a multiple regression approach to pallidal LFPs in patients with head tremors using kinematic information measured with an inertial measurement unit (IMU) and an electromyographic signal (EMG). With IMU regression, we found tremor contamination in all subjects, whereas EMG regression identified it in only three out of five. IMU regression was also superior to EMG regression in removing tremor-related artifacts and resulted in a significant power reduction, especially in the theta-alpha band. Pallido-muscular coherence was affected by a head tremor and disappeared after IMU regression. Our results show that the Percept PC can record low-frequency oscillations but also reveal spectral contamination due to movement artifacts. IMU regression can identify such artifact contamination and be a suitable tool for its removal.
Gait disturbances are common manifestations of Parkinson’s disease (PD), with unmet therapeutic needs. Inertial measurement units (IMUs) are capable of monitoring gait, but they lack neurophysiological information that may be crucial for studying gait disturbances in these patients. Here, we present a machine learning approach to approximate IMU angular velocity profiles and subsequently gait events using electromyographic (EMG) channels during overground walking in patients with PD. We recorded six parkinsonian patients while they walked for at least three minutes. Patient-agnostic regression models were trained on temporally embedded EMG time series of different combinations of up to five leg muscles bilaterally (i.e., tibialis anterior, soleus, gastrocnemius medialis, gastrocnemius lateralis, and vastus lateralis). Gait events could be detected with high temporal precision (median displacement of <50 ms), low numbers of missed events (<2%), and next to no false-positive event detections (<0.1%). Swing and stance phases could thus be determined with high fidelity (median F1-score of ~0.9). Interestingly, the best performance was obtained using as few as two EMG probes placed on the left and right vastus lateralis. Our results demonstrate the practical utility of the proposed EMG-based system for gait event prediction, which allows the simultaneous acquisition of an electromyographic signal to be performed. This gait analysis approach has the potential to make additional measurement devices such as IMUs and force plates less essential, thereby reducing financial and preparation overheads and discomfort factors in gait studies.
For the treatment of Multiple Myeloma, proteasome inhibitors are highly efficient and widely used, but resistance is a major obstacle to successful therapy. Several underlying mechanisms have been proposed but were only reported for a minority of resistant patients. The proteasome is a large and complex machinery. Here, we focus on the AAA ATPases of the 19S proteasome regulator (PSMC1-6) and their implication in PI resistance. As an example of cancer evolution and the acquisition of resistance, we conducted an in-depth analysis of an index patient by applying FISH, WES, and immunoglobulin-rearrangement sequencing in serial samples, starting from MGUS to newly diagnosed Multiple Myeloma to a PI-resistant relapse. The WES analysis uncovered an acquired PSMC2 Y429S mutation at the relapse after intensive bortezomib-containing therapy, which was functionally confirmed to mediate PI resistance. A meta-analysis comprising 1499 newly diagnosed and 447 progressed patients revealed a total of 36 SNVs over all six PSMC genes that were structurally accumulated in regulatory sites for activity such as the ADP/ATP binding pocket. Other alterations impact the interaction between different PSMC subunits or the intrinsic conformation of an individual subunit, consequently affecting the folding and function of the complex. Interestingly, several mutations were clustered in the central channel of the ATPase ring, where the unfolded substrates enter the 20S core. Our results indicate that PSMC SNVs play a role in PI resistance in MM.
The Event Horizon Telescope (EHT) has led to the first images of a supermassive black hole, revealing the central compact objects in the elliptical galaxy M87 and the Milky Way. Proposed upgrades to this array through the next-generation EHT (ngEHT) program would sharply improve the angular resolution, dynamic range, and temporal coverage of the existing EHT observations. These improvements will uniquely enable a wealth of transformative new discoveries related to black hole science, extending from event-horizon-scale studies of strong gravity to studies of explosive transients to the cosmological growth and influence of supermassive black holes. Here, we present the key science goals for the ngEHT and their associated instrument requirements, both of which have been formulated through a multi-year international effort involving hundreds of scientists worldwide.
Climate models are the tool of choice for scientists researching climate change. Like all models they suffer from errors, particularly systematic and location-specific representation errors. One way to reduce these errors is model output statistics (MOS) where the model output is fitted to observational data with machine learning. In this work, we assess the use of convolutional Deep Learning climate MOS approaches and present the ConvMOS architecture which is specifically designed based on the observation that there are systematic and location-specific errors in the precipitation estimates of climate models. We apply ConvMOS models to the simulated precipitation of the regional climate model REMO, showing that a combination of per-location model parameters for reducing location-specific errors and global model parameters for reducing systematic errors is indeed beneficial for MOS performance. We find that ConvMOS models can reduce errors considerably and perform significantly better than three commonly used MOS approaches and plain ResNet and U-Net models in most cases. Our results show that non-linear MOS models underestimate the number of extreme precipitation events, which we alleviate by training models specialized towards extreme precipitation events with the imbalanced regression method DenseLoss. While we consider climate MOS, we argue that aspects of ConvMOS may also be beneficial in other domains with geospatial data, such as air pollution modeling or weather forecasts.
Polyneuropathy (PNP) is a term to describe diseases of the peripheral nervous system, 50% of which present with neuropathic pain. In some types of PNP, pain is restricted to the skin distally in the leg, suggesting a local regulatory process leading to pain. In this study, we proposed a pro-inflammatory pathway mediated by NF-κB that might be involved in the development of pain in patients with painful PNP. To test this hypothesis, we have collected nerve and skin samples from patients with different etiologies and levels of pain. We performed RT-qPCR to analyze the gene expression of the proposed inflammatory pathway components in sural nerve and in distal and proximal skin samples. In sural nerve, we showed a correlation of TLR4 and TNFα to neuropathic pain, and an upregulation of TNFα in patients with severe pain. Patients with an inflammatory PNP also presented a lower expression of TRPV1 and SIRT1. In distal skin, we found a reduced expression of TLR4 and miR-146-5p, in comparison to proximal skin. Our findings thus support our hypothesis of local inflammatory processes involved in pain in PNP, and further show disturbed anti-inflammatory pathways involving TRPV1 and SIRT1 in inflammatory PNP.
Aggression and deficient cognitive control problems are widespread in psychiatric disorders, including major depressive disorder (MDD). These abnormalities are known to contribute significantly to the accompanying functional impairment and the global burden of disease. Progress in the development of targeted treatments of excessive aggression and accompanying symptoms has been limited, and there exists a major unmet need to develop more efficacious treatments for depressed patients. Due to the complex nature and the clinical heterogeneity of MDD and the lack of precise knowledge regarding its pathophysiology, effective management is challenging. Nonetheless, the aetiology and pathophysiology of MDD has been the subject of extensive research and there is a vast body of the latest literature that points to new mechanisms for this disorder. Here, we overview the key mechanisms, which include neuroinflammation, oxidative stress, insulin receptor signalling and abnormal myelination. We discuss the hypotheses that have been proposed to unify these processes, as many of these pathways are integrated for the neurobiology of MDD. We also describe the current translational approaches in modelling depression, including the recent advances in stress models of MDD, and emerging novel therapies, including novel approaches to management of excessive aggression, such as anti-diabetic drugs, antioxidant treatment and herbal compositions.