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The ongoing functional annotation of proteins relies upon the work of curators to capture experimental findings from scientific literature and apply them to protein sequence and structure data. However, with the increasing use of high-throughput experimental assays, a small number of experimental studies dominate the functional protein annotations collected in databases. Here, we investigate just how prevalent is the \"few articles - many proteins\" phenomenon. We examine the experimentally validated annotation of proteins provided by several groups in the GO Consortium, and show that the distribution of proteins per published study is exponential, with 0.14% of articles providing the source of annotations for 25% of the proteins in the UniProt-GOA compilation. Since each of the dominant articles describes the use of an assay that can find only one function or a small group of functions, this leads to substantial biases in what we know about the function of many proteins. Mass-spectrometry, microscopy and RNAi experiments dominate high throughput experiments. Consequently, the functional information derived from these experiments is mostly of the subcellular location of proteins, and of the participation of proteins in embryonic developmental pathways. For some organisms, the information provided by different studies overlap by a large amount. We also show that the information provided by high throughput experiments is less specific than those provided by low throughput experiments. Given the experimental techniques available, certain biases in protein function annotation due to high-throughput experiments are unavoidable. Knowing that these biases exist and understanding their characteristics and extent is important for database curators, developers of function annotation programs, and anyone who uses protein function annotation data to plan experiments.

Airborne particulate matter (PM), particularly fine (PM ) and coarse (PM ) particles, is a major environmental health concern linked to increased respiratory morbidity and mortality. During the COVID-19 pandemic, epidemiological studies suggested that PM exposure may worsen SARS-CoV-2 infection outcomes; however, cellular mechanisms underlying this association remain incompletely understood. Here, we investigated how pre-exposure to PM and PM impacts SARS-CoV-2 infection dynamics in Calu-3 human epithelial cells. Calu-3 cells were pre-exposed to PM for 72 h prior to infection with either the wild-type Wuhan strain or the more virulent Delta variant for additional 48 h. Viral infection, receptor expression, apoptosis and cytokine responses were assessed. PM , but not PM , enhanced Delta variant infection, increasing the proportion of infected cells by 13.7% and viral titers by 2.6-fold compared with controls. This enhancement was not attributable to changes in ACE2 receptor expression or viral entry efficiency but instead impaired antiviral defenses. PM10 pre-exposure suppressed apoptosis and reduced the expression of pro-inflammatory cytokines including IFN-γ, IP-10, and TNF-α during Delta infection. These findings suggest that PM compromise epithelial antiviral response by dampening apoptotic cell clearance and inflammatory responses, thereby creating a cellular condition more permissive to viral replication. Our study provides a mechanistic basis by which particulate air pollution may amplify SARS-CoV-2 pathogenicity in a variant-specific manner. These results underscore further validation in physiologically relevant systems and highlight the potential public health implications of air pollution during viral pandemics.

A major polymorphism in the fucosyltransferase2 (FUT2) gene influences risk of multiple gut diseases, but its impact on the microbiome of breastfed infants was unknown. In individuals with an active FUT2 enzyme (“secretors”), the intestinal mucosa is abundantly fucosylated, providing mutualist bacteria with a rich endogenous source of fucose. Non-secretors comprise approximately one-fifth of the population, and they lack the ability to create this enzyme. Similarly, maternal secretor status influences the abundance of a breastfeeding mother’s fucosylated milk oligosaccharides. We compared the impact of maternal secretor status, measured by FUT2 genotype, and infant secretor status, measured by FUT2 genotype and phenotype, on early infant fecal microbiome samples collected from 2-month-old exclusively breastfed infants (n = 59). Infant secretor status (19% non-secretor, 25% low-secretor, and 56% full-secretor) was more strongly associated with the infant microbiome than it was with the maternal FUT2 genotype. Alpha diversity was greater in the full-secretors than in the low- or non-secretor infants (p = 0.049). Three distinct microbial enterotypes corresponded to infant secretor phenotype (p = 0.022) and to the dominance of Bifidobacterium breve, B. longum, or neither (p < 0.001). Infant secretor status was also associated with microbial metabolic capacity, specifically, bioenergetics pathways. We concluded that in exclusively breastfed infants, infant—but not maternal—secretor status is associated with infant microbial colonization and metabolic capacity.

The ongoing functional annotation of proteins relies upon the work of curators to capture experimental findings from scientific literature and apply them to protein sequence and structure data. However, with the increasing use of high-throughput experimental assays, a small number of experimental studies dominate the functional protein annotations collected in databases. Here, we investigate just how prevalent is the \"few articles - many proteins\" phenomenon. We examine the experimentally validated annotation of proteins provided by several groups in the GO Consortium, and show that the distribution of proteins per published study is exponential, with 0.14% of articles providing the source of annotations for 25% of the proteins in the UniProt-GOA compilation. Since each of the dominant articles describes the use of an assay that can find only one function or a small group of functions, this leads to substantial biases in what we know about the function of many proteins. Mass-spectrometry, microscopy and RNAi experiments dominate high throughput experiments. Consequently, the functional information derived from these experiments is mostly of the subcellular location of proteins, and of the participation of proteins in embryonic developmental pathways. For some organisms, the information provided by different studies overlap by a large amount. We also show that the information provided by high throughput experiments is less specific than those provided by low throughput experiments. Given the experimental techniques available, certain biases in protein function annotation due to high-throughput experiments are unavoidable. Knowing that these biases exist and understanding their characteristics and extent is important for database curators, developers of function annotation programs, and anyone who uses protein function annotation data to plan experiments.

Abstract The development of targeted treatment options for precision medicine is hampered by a slow and costly process of drug screening. While small molecule docking simulations are often applied in conjunction with cheminformatic methods to reduce the number of candidate molecules to be tested experimentally, the current approaches suffer from high false positive rates and are computationally expensive. Here, we present a novel in silico approach for drug discovery and repurposing, dubbed connectivity enhanced Structure Activity Relationship (ceSAR) that improves on current methods by combining docking and virtual screening approaches with pharmacogenomics and transcriptional signature connectivity analysis. ceSAR builds on the landmark LINCS library of transcriptional signatures of over 20,000 drug-like molecules and ~5,000 gene knock-downs (KDs) to connect small molecules and their potential targets. For a set of candidate molecules and specific target gene, candidate molecules are first ranked by chemical similarity to their ‘concordant’ LINCS analogs that share signature similarity with a knock-down of the target gene. An efficient method for chemical similarity search, optimized for sparse binary fingerprints of chemical moieties, is used to enable fast searches for large libraries of small molecules. A small subset of candidate compounds identified in the first step is then re-scored by combining signature connectivity with docking simulations. On a set of 20 DUD-E benchmark targets with LINCS KDs, the consensus approach reduces significantly false positive rates, improving the median precision 3-fold over docking methods at the extreme library reduction. We conclude that signature connectivity and docking provide complementary signals, offering an avenue to improve the accuracy of virtual screening while reducing run times by multiple orders of magnitude. Competing Interest Statement The authors have declared no competing interest. Footnotes * https://github.com/sig2lead

The ongoing functional annotation of proteins relies upon the work of curators to capture experimental findings from scientific literature and apply them to protein sequence and structure data. However, with the increasing use of high-throughput experimental assays, a small number of experimental studies dominate the functional protein annotations collected in databases. Here we investigate just how prevalent is the \"few articles -- many proteins\" phenomenon. We examine the experimentally validated annotation of proteins provided by several groups in the GO Consortium, and show that the distribution of proteins per published study is exponential, with 0.14% of articles providing the source of annotations for 25% of the proteins in the UniProt-GOA compilation. Since each of the dominant articles describes the use of an assay that can find only one function or a small group of functions, this leads to substantial biases in what we know about the function of many proteins. Mass-spectrometry, microscopy and RNAi experiments dominate high throughput experiments. Consequently, the functional information derived from these experiments is mostly of the subcellular location of proteins, and of the participation of proteins in embryonic developmental pathways. For some organisms, the information provided by different studies overlap by a large amount. We also show that the information provided by high throughput experiments is less specific than those provided by low throughput experiments. Given the experimental techniques available, certain biases in protein function annotation due to high-throughput experiments are unavoidable. Knowing that these biases exist and understanding their characteristics and extent is important for database curators, developers of function annotation programs, and anyone who uses protein function annotation data to plan experiments.