Nature Communications

Journal: Nature Communications

Abbreviation

Nat Commun

Publisher

Nature

ISSN

2041-1723

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Publications 1 - 10 of 1650

Ecology of Endozoicomonadaceae in three coral genera across the Pacific Ocean
Hochart, Corentin; Paoli, Lucas Pierre Antoine; Ruscheweyh, Hans-Joachim; et al. (2023)
Nature Communications
Health and resilience of the coral holobiont depend on diverse bacterial communities often dominated by key marine symbionts of the Endozoicomonadaceae family. The factors controlling their distribution and their functional diversity remain, however, poorly known. Here, we study the ecology of Endozoicomonadaceae at an ocean basin-scale by sampling specimens from three coral genera (Pocillopora, Porites, Millepora) on 99 reefs from 32 islands across the Pacific Ocean. The analysis of 2447 metabarcoding and 270 metagenomic samples reveals that each coral genus harbored a distinct new species of Endozoicomonadaceae. These species are composed of nine lineages that have distinct biogeographic patterns. The most common one, found in Pocillopora, appears to be a globally distributed symbiont with distinct metabolic capabilities, including the synthesis of amino acids and vitamins not produced by the host. The other lineages are structured partly by the host genetic lineage in Pocillopora and mainly by the geographic location in Porites. Millepora is more rarely associated to Endozoicomonadaceae. Our results show that different coral genera exhibit distinct strategies of host-Endozoicomonadaceae associations that are defined at the bacteria lineage level.
Constructing exact representations of quantum many-body systems with deep neural networks
Carleo, Giuseppe; Nomura, Yusuke; Imada, Masatoshi (2018)
Nature Communications
Obtaining accurate properties of many-body interacting quantum matter is a long-standing challenge in theoretical physics and chemistry, rooting into the complexity of the many-body wave-function. Classical representations of many-body states constitute a key tool for both analytical and numerical approaches to interacting quantum problems. Here, we introduce a technique to construct classical representations of many-body quantum systems based on artificial neural networks. Our constructions are based on the deep Boltzmann machine architecture, in which two layers of hidden neurons mediate quantum correlations. The approach reproduces the exact imaginary-time evolution for many-body lattice Hamiltonians, is completely deterministic, and yields networks with a polynomially-scaling number of neurons. We provide examples where physical properties of spin Hamiltonians can be efficiently obtained. Also, we show how systematic improvements upon existing restricted Boltzmann machines ansatze can be obtained. Our method is an alternative to the standard path integral and opens new routes in representing quantum many-body states.
Standardization and harmonization of distributed multi-center proteotype analysis supporting precision medicine studies
Xuan, Yue; Goetze, Sandra; Wollscheid, Bernd; et al. (2020)
Nature Communications
Cancer has no borders: Generation and analysis of molecular data across multiple centers worldwide is necessary to gain statistically significant clinical insights for the benefit of patients. Here we conceived and standardized a proteotype data generation and analysis workflow enabling distributed data generation and evaluated the quantitative data generated across laboratories of the international Cancer Moonshot consortium. Using harmonized mass spectrometry (MS) instrument platforms and standardized data acquisition procedures, we demonstrate robust, sensitive, and reproducible data generation across eleven international sites on seven consecutive days in a 24/7 operation mode. The data presented from the high-resolution MS1-based quantitative data-independent acquisition (HRMS1-DIA) workflow shows that coordinated proteotype data acquisition is feasible from clinical specimens using such standardized strategies. This work paves the way for the distributed multi-omic digitization of large clinical specimen cohorts across multiple sites as a prerequisite for turning molecular precision medicine into reality.
Nanostructure-specific X-ray tomography reveals myelin levels, integrity and axon orientations in mouse and human nervous tissue
Georgiadis, Marios; Schroeter, Aileen; Gao, Zirui; et al. (2021)
Nature Communications
Myelin insulates neuronal axons and enables fast signal transmission, constituting a key component of brain development, aging and disease. Yet, myelin-specific imaging of macroscopic samples remains a challenge. Here, we exploit myelin’s nanostructural periodicity, and use small-angle X-ray scattering tensor tomography (SAXS-TT) to simultaneously quantify myelin levels, nanostructural integrity and axon orientations in nervous tissue. Proof-of-principle is demonstrated in whole mouse brain, mouse spinal cord and human white and gray matter samples. Outcomes are validated by 2D/3D histology and compared to MRI measurements sensitive to myelin and axon orientations. Specificity to nanostructure is exemplified by concomitantly imaging different myelin types with distinct periodicities. Finally, we illustrate the method’s sensitivity towards myelin-related diseases by quantifying myelin alterations in dysmyelinated mouse brain. This non-destructive, stain-free molecular imaging approach enables quantitative studies of myelination within and across samples during development, aging, disease and treatment, and is applicable to other ordered biomolecules or nanostructures.
Cryo-EM structure of the botulinum neurotoxin A/SV2B complex and its implications for translocation
Khanppnavar, Basavraj; Leka, Oneda; Pal, Sushant K.; et al. (2025)
Nature Communications
Botulinum neurotoxin A1 (BoNT/A1) belongs to the most potent toxins and is used as a major therapeutic agent. Neurotoxin conformation is crucial for its translocation to the neuronal cytosol, a key process for intoxication that is only poorly understood. To gain molecular insights into the steps preceding toxin translocation, we determine cryo-EM structures of BoNT/A1 alone and in complex with its receptor synaptic vesicle glycoprotein 2B (SV2B). In solution, BoNT/A1 adopts a unique, semi-closed conformation. The toxin changes its structure into an open state upon receptor binding with the translocation domain (HN) and the catalytic domain (LC) remote from the membrane, suggesting translocation incompatibility. Under acidic pH conditions, where translocation is initiated, receptor-bound BoNT/A1 switches back into a semi-closed conformation. This conformation brings the LC and HN close to the membrane, suggesting that a translocation-competent state of the toxin is required for successful LC transport into the neuronal cytosol.
Formation of the beta-barrel assembly machinery complex in lipid bilayers as seen by solid-state NMR
Pinto, Cecilia; Mance, Deni; Sinnige, Tessa; et al. (2018)
Nature Communications
The β-barrel assembly machinery (BAM) is a pentameric complex (BamA–E), which catalyzes the essential process of β-barrel protein insertion into the outer membrane of E. coli. Thus far, a detailed understanding of the insertion mechanism has been elusive but recent results suggest that local protein motion, in addition to the surrounding membrane environment, may be of critical relevance. We have devised a high-sensitivity solid-state NMR approach to directly probe protein motion and the structural changes associated with BAM complex assembly in lipid bilayers. Our results reveal how essential BamA domains, such as the interface formed by the polypeptide transport associated domains P4 and P5 become stabilized after complex formation and suggest that BamA β-barrel opening and P5 reorientation is directly related to complex formation in membranes. Both the lateral gate, as well as P5, exhibit local dynamics, a property that could play an integral role in substrate recognition and insertion.
The backpropagation algorithm implemented on spiking neuromorphic hardware
Renner, Alpha; Sheldon, Forrest; Zlotnik, Anatoly; et al. (2024)
Nature Communications
The capabilities of natural neural systems have inspired both new generations of machine learning algorithms as well as neuromorphic, very large-scale integrated circuits capable of fast, low-power information processing. However, it has been argued that most modern machine learning algorithms are not neurophysiologically plausible. In particular, the workhorse of modern deep learning, the backpropagation algorithm, has proven difficult to translate to neuromorphic hardware. This study presents a neuromorphic, spiking backpropagation algorithm based on synfire-gated dynamical information coordination and processing implemented on Intel's Loihi neuromorphic research processor. We demonstrate a proof-of-principle three-layer circuit that learns to classify digits and clothing items from the MNIST and Fashion MNIST datasets. To our knowledge, this is the first work to show a Spiking Neural Network implementation of the exact backpropagation algorithm that is fully on-chip without a computer in the loop. It is competitive in accuracy with off-chip trained SNNs and achieves an energy-delay product suitable for edge computing. This implementation shows a path for using in-memory, massively parallel neuromorphic processors for low-power, low-latency implementation of modern deep learning applications.
Programmable high-dimensional Hamiltonian in a photonic waveguide array
Yang, Yang; Chapman, Robert J.; Haylock, Ben; et al. (2024)
Nature Communications
Waveguide lattices offer a compact and stable platform for a range of applications, including quantum walks, condensed matter system simulation, and classical and quantum information processing. However, to date, waveguide lattice devices have been static and designed for specific applications. We present a programmable waveguide array in which the Hamiltonian terms can be individually electro-optically tuned to implement various Hamiltonian continuous-time evolutions on a single device. We used a single array with 11 waveguides in lithium niobate, controlled via 22 electrodes, to perform a range of experiments that realized the Su-Schriffer-Heeger model, the Aubrey-Andre model, and Anderson localization, which is equivalent to over 2500 static devices. Our architecture’s micron-scale local electric fields overcome the cross-talk limitations of thermo-optic phase shifters in other platforms such as silicon, silicon-nitride, and silica. Electro-optic control allows for ultra-fast and more precise reconfigurability with lower power consumption, and with quantum input states, our platform can enable the study of multiple condensed matter quantum dynamics with a single device.
Absolute dating of the European Neolithic using the 5259 BC rapid ¹⁴C excursion
Maczkowski, Andrej; Pearson, Charlotte; Francuz, John; et al. (2024)
Nature Communications
Abrupt radiocarbon (¹⁴C) excursions, or Miyake events, in sequences of radiocarbon measurements from calendar-dated tree-rings provide opportunities to assign absolute calendar dates to undated wood samples from contexts across history and prehistory. Here, we report a tree-ring and ¹⁴C-dating study of the Neolithic site of Dispilio, Northern Greece, a waterlogged archaeological site on Lake Kastoria. Findings secure an absolute, calendar-dated time using the 5259 BC Miyake event, with the final ring of the 303-year-long juniper tree-ring chronology dating to 5140 BC. While other sites have been absolutely dated to a calendar year through ¹⁴C-signature Miyake events, Dispilio is the first European Neolithic site of these and it provides a fixed, calendar-year anchor point for regional chronologies of the Neolithic.
Emergence of active nematics in chaining bacterial biofilms
Yaman, Yusuf I.; Demir, Esin; Vetter, Roman; et al. (2019)
Nature Communications
Growing tissue and bacterial colonies are active matter systems where cell divisions and cellular motion generate active stress. Although they operate in the non-equilibrium regime, these biological systems can form large-scale ordered structures. How mechanical instabilities drive the dynamics of active matter systems and form ordered structures are not well understood. Here, we use chaining Bacillus subtilis, also known as a biofilm, to study the relation between mechanical instabilities and nematic ordering. We find that bacterial biofilms have intrinsic length scales above which a series of mechanical instabilities occur. Localized stress and friction drive buckling and edge instabilities which further create nematically aligned structures and topological defects. We also observe that topological defects control stress distribution and initiate the formation of sporulation sites by creating three-dimensional structures. In this study we propose an alternative active matter platform to study the essential roles of mechanics in growing biological tissue.

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