Catálogo de publicaciones - revistas

<jats:title>Abstract</jats:title><jats:p>High-throughput proteomics platforms measuring thousands of proteins in plasma combined with genomic and phenotypic information have the power to bridge the gap between the genome and diseases. Here we performed association studies of Olink Explore 3072 data generated by the UK Biobank Pharma Proteomics Project<jats:sup>1</jats:sup> on plasma samples from more than 50,000 UK Biobank participants with phenotypic and genotypic data, stratifying on British or Irish, African and South Asian ancestries. We compared the results with those of a SomaScan v4 study on plasma from 36,000 Icelandic people<jats:sup>2</jats:sup>, for 1,514 of whom Olink data were also available. We found modest correlation between the two platforms. Although <jats:italic>cis</jats:italic> protein quantitative trait loci were detected for a similar absolute number of assays on the two platforms (2,101 on Olink versus 2,120 on SomaScan), the proportion of assays with such supporting evidence for assay performance was higher on the Olink platform (72% versus 43%). A considerable number of proteins had genomic associations that differed between the platforms. We provide examples where differences between platforms may influence conclusions drawn from the integration of protein levels with the study of diseases. We demonstrate how leveraging the diverse ancestries of participants in the UK Biobank helps to detect novel associations and refine genomic location. Our results show the value of the information provided by the two most commonly used high-throughput proteomics platforms and demonstrate the differences between them that at times provides useful complementarity.</jats:p>

<jats:title>Abstract</jats:title><jats:p>Coronavirus spike proteins mediate receptor binding and membrane fusion, making them prime targets for neutralizing antibodies. In the cases of severe acute respiratory syndrome coronavirus, severe acute respiratory syndrome coronavirus 2 and Middle East respiratory syndrome coronavirus, spike proteins transition freely between open and closed conformations to balance host cell attachment and immune evasion<jats:sup>1–5</jats:sup>. Spike opening exposes domain S1<jats:sup>B</jats:sup>, allowing it to bind to proteinaceous receptors<jats:sup>6,7</jats:sup>, and is also thought to enable protein refolding during membrane fusion<jats:sup>4,5</jats:sup>. However, with a single exception, the pre-fusion spike proteins of all other coronaviruses studied so far have been observed exclusively in the closed state. This raises the possibility of regulation, with spike proteins more commonly transitioning to open states in response to specific cues, rather than spontaneously. Here, using cryogenic electron microscopy and molecular dynamics simulations, we show that the spike protein of the common cold human coronavirus HKU1 undergoes local and long-range conformational changes after binding a sialoglycan-based primary receptor to domain S1<jats:sup>A</jats:sup>. This binding triggers the transition of S1<jats:sup>B</jats:sup> domains to the open state through allosteric interdomain crosstalk. Our findings provide detailed insight into coronavirus attachment, with possibilities of dual receptor usage and priming of entry as a means of immune escape.</jats:p>

<jats:title>Abstract</jats:title><jats:p>Integrating human genomics and proteomics can help elucidate disease mechanisms, identify clinical biomarkers and discover drug targets<jats:sup>1–4</jats:sup>. Because previous proteogenomic studies have focused on common variation via genome-wide association studies, the contribution of rare variants to the plasma proteome remains largely unknown. Here we identify associations between rare protein-coding variants and 2,923 plasma protein abundances measured in 49,736 UK Biobank individuals. Our variant-level exome-wide association study identified 5,433 rare genotype–protein associations, of which 81% were undetected in a previous genome-wide association study of the same cohort<jats:sup>5</jats:sup>. We then looked at aggregate signals using gene-level collapsing analysis, which revealed 1,962 gene–protein associations. Of the 691 gene-level signals from protein-truncating variants, 99.4% were associated with decreased protein levels. <jats:italic>STAB1</jats:italic> and <jats:italic>STAB2</jats:italic>, encoding scavenger receptors involved in plasma protein clearance, emerged as pleiotropic loci, with 77 and 41 protein associations, respectively. We demonstrate the utility of our publicly accessible resource through several applications. These include detailing an allelic series in <jats:italic>NLRC4</jats:italic>, identifying potential biomarkers for a fatty liver disease-associated variant in <jats:italic>HSD17B13</jats:italic> and bolstering phenome-wide association studies by integrating protein quantitative trait loci with protein-truncating variants in collapsing analyses. Finally, we uncover distinct proteomic consequences of clonal haematopoiesis (CH), including an association between <jats:italic>TET2-</jats:italic>CH and increased FLT3 levels. Our results highlight a considerable role for rare variation in plasma protein abundance and the value of proteogenomics in therapeutic discovery.</jats:p>

<jats:title>Abstract</jats:title><jats:p>Scientists have grappled with reconciling biological evolution<jats:sup>1,2</jats:sup> with the immutable laws of the Universe defined by physics. These laws underpin life’s origin, evolution and the development of human culture and technology, yet they do not predict the emergence of these phenomena. Evolutionary theory explains why some things exist and others do not through the lens of selection. To comprehend how diverse, open-ended forms can emerge from physics without an inherent design blueprint, a new approach to understanding and quantifying selection is necessary<jats:sup>3–5</jats:sup>. We present assembly theory (AT) as a framework that does not alter the laws of physics, but redefines the concept of an ‘object’ on which these laws act. AT conceptualizes objects not as point particles, but as entities defined by their possible formation histories. This allows objects to show evidence of selection, within well-defined boundaries of individuals or selected units. We introduce a measure called assembly (<jats:italic>A</jats:italic>), capturing the degree of causation required to produce a given ensemble of objects. This approach enables us to incorporate novelty generation and selection into the physics of complex objects. It explains how these objects can be characterized through a forward dynamical process considering their assembly. By reimagining the concept of matter within assembly spaces, AT provides a powerful interface between physics and biology. It discloses a new aspect of physics emerging at the chemical scale, whereby history and causal contingency influence what exists.</jats:p>