Accelerating drug discovery and repurposing by combining transcriptional signature connectivity with docking.

We present an in silico approach for drug discovery, dubbed connectivity enhanced structure activity relationship (ceSAR). Building on the landmark LINCS library of transcriptional signatures of drug-like molecules and gene knockdowns, ceSAR combines cheminformatic techniques with signature concordance analysis to connect small molecules and their targets and further assess their biophysical compatibility using molecular docking. Candidate compounds are first ranked in a target structure-independent manner, using chemical similarity to LINCS analogs that exhibit transcriptomic concordance with a target gene knockdown. Top candidates are subsequently rescored using docking simulations and machine learning-based consensus of the two approaches. Using extensive benchmarking, we show that ceSAR greatly reduces false-positive rates, while cutting run times by multiple orders of magnitude and further democratizing drug discovery pipelines. We further demonstrate the utility of ceSAR by identifying and experimentally validating inhibitors of BCL2A1, an important antiapoptotic target in melanoma and preterm birth-associated inflammation.

Validation of collection and anaerobic fermentation techniques for measuring prebiotic impact on gut microbiota.

BACKGROUND: Defining the ability of prebiotic dietary carbohydrates to influence the composition and metabolism of the gut microbiota is central to defining their health impact in diverse individuals. Many clinical trials are using indirect methods. This study aimed to validate collection and fermentation methods enabling their use in the context of clinical studies. METHODS AND RESULTS: Parameters tested included stool sample acquisition, storage, and growth conditions. Stool from 3 infants and 3 adults was collected and stored under varying conditions. Samples were cultured anaerobically for two days in the presence of prebiotics, whereupon optical density and pH were measured across time. Whole genome shotgun sequencing and NMR metabolomics were performed. Neither the type of collection vial (standard vial and two different BD anaerobic collection vials) nor cryopreservation (-80 °C or 4 °C) significantly influenced either microbial composition at 16 h of anaerobic culture or the principal components of the metabolome at 8 or 16 h. Metagenomic differences were driven primarily by subject, while metabolomic differences were driven by fermentation sugar (2'-fucosyllactose or dextrose). CONCLUSIONS: These data identified a feasible and valid approach for prebiotic fermentation analysis of individual samples in large clinical studies: collection of stool microbiota using standard vials; cryopreservation prior to testing; and collecting fermentation read-out at 8 and 16 hr. Thus, fermentation analysis can be a valid technique for testing the effects of prebiotics on human fecal microbiota.

Correlates of Rotavirus Vaccine Shedding and Seroconversion in a U.S. Cohort of Healthy Infants.

BACKGROUND: Rotavirus is a leading cause of severe pediatric gastroenteritis; two highly effective vaccines are used in the US. We aimed to identify correlates of immune response to rotavirus vaccination in a US cohort. METHODS: PREVAIL is a birth cohort of 245 mother-child pairs enrolled 2017-2018 and followed for 2 years. Infant stool samples and symptom information were collected weekly. Shedding was defined as RT-PCR detection of rotavirus vaccine virus in stools collected 4-28 days after dose one. Seroconversion was defined as a threefold rise in IgA between the six-week and six-month blood draws. Correlates were analyzed using generalized estimating equations and logistic regression. RESULTS: Pre-vaccination IgG (OR=0.84, 95% CI [0.75-0.94] per 100-unit increase) was negatively associated with shedding. Shedding was also less likely among infants with a single-nucleotide polymorphism inactivating FUT2 antigen secretion ("non-secretors") with non-secretor mothers, versus all other combinations (OR 0.37 [0.16-0.83]). Of 141 infants with data, 105 (74%) seroconverted; 78 (77%) had shed vaccine virus following dose one. Pre-vaccination IgG and secretor status were significantly associated with seroconversion. Neither shedding nor seroconversion significantly differed by vaccine product. DISCUSSION: In this US cohort, pre-vaccination IgG and maternal and infant secretor status were associated with rotavirus vaccine response.

Gut Microbiome Composition and Metabolic Capacity Differ by FUT2 Secretor Status in Exclusively Breastfed Infants.

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, infant­but not maternal­secretor status is associated with infant microbial colonization and metabolic capacity.

Crowdsourcing biocuration: The Community Assessment of Community Annotation with Ontologies (CACAO).

Experimental data about gene functions curated from the primary literature have enormous value for research scientists in understanding biology. Using the Gene Ontology (GO), manual curation by experts has provided an important resource for studying gene function, especially within model organisms. Unprecedented expansion of the scientific literature and validation of the predicted proteins have increased both data value and the challenges of keeping pace. Capturing literature-based functional annotations is limited by the ability of biocurators to handle the massive and rapidly growing scientific literature. Within the community-oriented wiki framework for GO annotation called the Gene Ontology Normal Usage Tracking System (GONUTS), we describe an approach to expand biocuration through crowdsourcing with undergraduates. This multiplies the number of high-quality annotations in international databases, enriches our coverage of the literature on normal gene function, and pushes the field in new directions. From an intercollegiate competition judged by experienced biocurators, Community Assessment of Community Annotation with Ontologies (CACAO), we have contributed nearly 5,000 literature-based annotations. Many of those annotations are to organisms not currently well-represented within GO. Over a 10-year history, our community contributors have spurred changes to the ontology not traditionally covered by professional biocurators. The CACAO principle of relying on community members to participate in and shape the future of biocuration in GO is a powerful and scalable model used to promote the scientific enterprise. It also provides undergraduate students with a unique and enriching introduction to critical reading of primary literature and acquisition of marketable skills.

Identification of candidate repurposable drugs to combat COVID-19 using a signature-based approach.

The COVID-19 pandemic caused by the novel SARS-CoV-2 is more contagious than other coronaviruses and has higher rates of mortality than influenza. Identification of effective therapeutics is a crucial tool to treat those infected with SARS-CoV-2 and limit the spread of this novel disease globally. We deployed a bioinformatics workflow to identify candidate drugs for the treatment of COVID-19. Using an "omics" repository, the Library of Integrated Network-Based Cellular Signatures (LINCS), we simultaneously probed transcriptomic signatures of putative COVID-19 drugs and publicly available SARS-CoV-2 infected cell lines to identify novel therapeutics. We identified a shortlist of 20 candidate drugs: 8 are already under trial for the treatment of COVID-19, the remaining 12 have antiviral properties and 6 have antiviral efficacy against coronaviruses specifically, in vitro. All candidate drugs are either FDA approved or are under investigation. Our candidate drug findings are discordant with (i.e., reverse) SARS-CoV-2 transcriptome signatures generated in vitro, and a subset are also identified in transcriptome signatures generated from COVID-19 patient samples, like the MEK inhibitor selumetinib. Overall, our findings provide additional support for drugs that are already being explored as therapeutic agents for the treatment of COVID-19 and identify promising novel targets that are worthy of further investigation.

Identification of new drug treatments to combat COVID19: A signature-based approach using iLINCS.

The COVID-19 pandemic caused by the novel SARS-CoV-2 is more contagious than other coronaviruses and has higher rates of mortality than influenza. As no vaccine or drugs are currently approved to specifically treat COVID-19, identification of effective therapeutics is crucial to treat the afflicted and limit disease spread. We deployed a bioinformatics workflow to identify candidate drugs for the treatment of COVID-19. Using an "omics" repository, the Library of Integrated Network-Based Cellular Signatures (LINCS), we simultaneously probed transcriptomic signatures of putative COVID-19 drugs and signatures of coronavirus-infected cell lines to identify therapeutics with concordant signatures and discordant signatures, respectively. Our findings include three FDA approved drugs that have established antiviral activity, including protein kinase inhibitors, providing a promising new category of candidates for COVID-19 interventions.

Hiding in plain sight: immune evasion by the staphylococcal protein SdrE.

The human immune system is responsible for identification and destruction of invader cells, such as the bacterial pathogen Staphylococcus aureus In response, S. aureus brings to the fight a large number of virulence factors, including several that allow it to evade the host immune response. The staphylococcal surface protein SdrE was recently reported to bind to complement Factor H, an important regulator of complement activation. Factor H attaches to the surface of host cells to inhibit complement activation and amplification, preventing the destruction of the host cell. SdrE binding to Factor H allows S. aureus to mimic a host cell and reduces bacterial killing by granulocytes. In a new study published in Biochemical Journal, Zhang et al. describe crystal structures of SdrE and its complex with the C-terminal portion of Factor H. The structure of SdrE and its interaction with the Factor H peptide closely resemble a family of surface proteins that recognize extracellular matrix components such as fibrinogen. However, unbound SdrE forms a novel 'Closed' conformation with an occluded peptide-binding groove. These structures reveal a fascinating mechanism for immune evasion and provide a potential avenue for the development of novel antimicrobial agents to target SdrE.

Biases in the experimental annotations of protein function and their effect on our understanding of protein function space.

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.