Early Detection of Severe Acute Respiratory Syndrome Coronavirus 2 Variants Using Traveler-based Genomic Surveillance at 4 US Airports, September 2021-January 2022.

We enrolled arriving international air travelers in a severe acute respiratory syndrome coronavirus 2 genomic surveillance program. We used molecular testing of pooled nasal swabs and sequenced positive samples for sublineage. Traveler-based surveillance provided early-warning variant detection, reporting the first US Omicron BA.2 and BA.3 in North America.

Lineage abundance estimation for SARS-CoV-2 in wastewater using transcriptome quantification techniques.

Effectively monitoring the spread of SARS-CoV-2 mutants is essential to efforts to counter the ongoing pandemic. Predicting lineage abundance from wastewater, however, is technically challenging. We show that by sequencing SARS-CoV-2 RNA in wastewater and applying algorithms initially used for transcriptome quantification, we can estimate lineage abundance in wastewater samples. We find high variability in signal among individual samples, but the overall trends match those observed from sequencing clinical samples. Thus, while clinical sequencing remains a more sensitive technique for population surveillance, wastewater sequencing can be used to monitor trends in mutant prevalence in situations where clinical sequencing is unavailable.

Variant abundance estimation for SARS-CoV-2 in wastewater using RNA-Seq quantification.

Effectively monitoring the spread of SARS-CoV-2 variants is essential to efforts to counter the ongoing pandemic. Wastewater monitoring of SARS-CoV-2 RNA has proven an effective and efficient technique to approximate COVID-19 case rates in the population. Predicting variant abundances from wastewater, however, is technically challenging. Here we show that by sequencing SARS-CoV-2 RNA in wastewater and applying computational techniques initially used for RNA-Seq quantification, we can estimate the abundance of variants in wastewater samples. We show by sequencing samples from wastewater and clinical isolates in Connecticut U.S.A. between January and April 2021 that the temporal dynamics of variant strains broadly correspond. We further show that this technique can be used with other wastewater sequencing techniques by expanding to samples taken across the United States in a similar timeframe. We find high variability in signal among individual samples, and limited ability to detect the presence of variants with clinical frequencies <10%; nevertheless, the overall trends match what we observed from sequencing clinical samples. Thus, while clinical sequencing remains a more sensitive technique for population surveillance, wastewater sequencing can be used to monitor trends in variant prevalence in situations where clinical sequencing is unavailable or impractical.

Genomic discovery of an evolutionarily programmed modality for small-molecule targeting of an intractable protein surface.

The vast majority of intracellular protein targets are refractory toward small-molecule therapeutic engagement, and additional therapeutic modalities are needed to overcome this deficiency. Here, the identification and characterization of a natural product, WDB002, reveals a therapeutic modality that dramatically expands the currently accepted limits of druggability. WDB002, in complex with the FK506-binding protein (FKBP12), potently and selectively binds the human centrosomal protein 250 (CEP250), resulting in disruption of CEP250 function in cells. The recognition mode is unprecedented in that the targeted domain of CEP250 is a coiled coil and is topologically featureless, embodying both a structural motif and surface topology previously considered on the extreme limits of "undruggability" for an intracellular target. Structural studies reveal extensive protein-WDB002 and protein-protein contacts, with the latter being distinct from those seen in FKBP12 ternary complexes formed by FK506 and rapamycin. Outward-facing structural changes in a bound small molecule can thus reprogram FKBP12 to engage diverse, otherwise "undruggable" targets. The flat-targeting modality demonstrated here has the potential to expand the druggable target range of small-molecule therapeutics. As CEP250 was recently found to be an interaction partner with the Nsp13 protein of the SARS-CoV-2 virus that causes COVID-19 disease, it is possible that WDB002 or an analog may exert useful antiviral activity through its ability to form high-affinity ternary complexes containing CEP250 and FKBP12.

Critical features for biosynthesis, stability, and functionality of a G protein-coupled receptor uncovered by all-versus-all mutations.

The structural features determining efficient biosynthesis, stability in the membrane and, after solubilization, in detergents are not well understood for integral membrane proteins such as G protein-coupled receptors (GPCRs). Starting from the rat neurotensin receptor 1, a class A GPCR, we generated a separate library comprising all 64 codons for each amino acid position. By combining a previously developed FACS-based selection system for functional expression [Sarkar C, et al. (2009) Proc Natl Acad Sci USA 105:14808-14813] with ultradeep 454 sequencing, we determined the amino acid preference in every position and identified several positions in the natural sequence that restrict functional expression. A strong accumulation of shifts, i.e., a residue preference different from wild type, is detected for helix 1, suggesting a key role in receptor biosynthesis. Furthermore, under selective pressure we observe a shift of the most conserved residues of the N-terminal helices. This unique data set allows us to compare the in vitro evolution of a GPCR to the natural evolution of the GPCR family and to observe how selective pressure shapes the sequence space covered by functional molecules. Under the applied selective pressure, several positions shift away from the wild-type sequence, and these improve the biophysical properties. We discuss possible structural reasons for conserved and shifted residues.

Hedgehog pathway inhibitor saridegib (IPI-926) increases lifespan in a mouse medulloblastoma model.

The Sonic Hedgehog (Shh) pathway drives a subset of medulloblastomas, a malignant neuroectodermal brain cancer, and other cancers. Small-molecule Shh pathway inhibitors have induced tumor regression in mice and patients with medulloblastoma; however, drug resistance rapidly emerges, in some cases via de novo mutation of the drug target. Here we assess the response and resistance mechanisms to the natural product derivative saridegib in an aggressive Shh-driven mouse medulloblastoma model. In this model, saridegib treatment induced tumor reduction and significantly prolonged survival. Furthermore, the effect of saridegib on tumor-initiating capacity was demonstrated by reduced tumor incidence, slower growth, and spontaneous tumor regression that occurred in allografts generated from previously treated autochthonous medulloblastomas compared with those from untreated donors. Saridegib, a known P-glycoprotein (Pgp) substrate, induced Pgp activity in treated tumors, which likely contributed to emergence of drug resistance. Unlike other Smoothened (Smo) inhibitors, the drug resistance was neither mutation-dependent nor Gli2 amplification-dependent, and saridegib was found to be active in cells with the D473H point mutation that rendered them resistant to another Smo inhibitor, GDC-0449. The fivefold increase in lifespan in mice treated with saridegib as a single agent compares favorably with both targeted and cytotoxic therapies. The absence of genetic mutations that confer resistance distinguishes saridegib from other Smo inhibitors.

Translating cancer 'omics' to improved outcomes.

The genomics era has yielded great advances in the understanding of cancer biology. At the same time, the immense complexity of the cancer genome has been revealed, as well as a striking heterogeneity at the whole-genome (or omics) level that exists between even histologically similar tumors. The vast accrual and public availability of multi-omics databases with associated clinical annotation including tumor histology, patient response, and outcome are a rich resource that has the potential to lead to rapid translation of high-throughput omics to improved overall survival. We focus on the unique advantages of a multidimensional approach to genomic analysis in this new high-throughput omics age and discuss the implications of the changing cancer demographic to translational omics research.

The SEQanswers wiki: a wiki database of tools for high-throughput sequencing analysis.

Recent advances in sequencing technology have created unprecedented opportunities for biological research. However, the increasing throughput of these technologies has created many challenges for data management and analysis. As the demand for sophisticated analyses increases, the development time of software and algorithms is outpacing the speed of traditional publication. As technologies continue to be developed, methods change rapidly, making publications less relevant for users. The SEQanswers wiki (SEQwiki) is a wiki database that is actively edited and updated by the members of the SEQanswers community (http://SEQanswers.com/). The wiki provides an extensive catalogue of tools, technologies and tutorials for high-throughput sequencing (HTS), including information about HTS service providers. It has been implemented in MediaWiki with the Semantic MediaWiki and Semantic Forms extensions to collect structured data, providing powerful navigation and reporting features. Within 2 years, the community has created pages for over 500 tools, with approximately 400 literature references and 600 web links. This collaborative effort has made SEQwiki the most comprehensive database of HTS tools anywhere on the web. The wiki includes task-focused mini-reviews of commonly used tools, and a growing collection of more than 100 HTS service providers. SEQwiki is available at: http://wiki.SEQanswers.com/.

Engineering enzyme specificity using computational design of a defined-sequence library.

Engineered biosynthetic pathways have the potential to produce high-value molecules from inexpensive feedstocks, but a key limitation is engineering enzymes with high activity and specificity for new reactions. Here, we developed a method for combining structure-based computational protein design with library-based enzyme screening, in which inter-residue correlations favored by the design are encoded into a defined-sequence library. We validated this approach by engineering a glucose 6-oxidase enzyme for use in a proposed pathway to convert D-glucose into D-glucaric acid. The most active variant, identified after only one round of diversification and screening of only 10,000 wells, is approximately 400-fold more active on glucose than is the wild-type enzyme. We anticipate that this strategy will be broadly applicable to the discovery of new enzymes for engineered biological pathways.

Application of second-generation sequencing to cancer genomics.

New generations of DNA sequencing technologies are enabling the systematic study of genetic derangement in cancers. Sequencing of cancer exomes or transcriptomes or even entire cancer genomes are now possible, though technical and economic challenges remain. Cancer samples are inherently heterogeneous and are often contaminated with normal DNA, placing additional demands on informatics tools for detecting genetic variation. However, even low coverage sequencing data can provide valuable information on genetic rearrangements, amplifications and losses in tumor genomes. Novel recurrent oncogenic mutations and fusion transcripts have been discovered with these technologies. In some sequenced cancer genomes, tens of thousands of genetic alterations have been discovered. While this enables the detailed dissection of mutation classes, it also presents a formidable informatics problem of sorting active 'driver' mutations from inactive 'passenger' mutations in order to prioritize these for further experimental characterization.

Re-examination of chimp protein kinases suggests "novel architectures" are gene prediction artifacts.

BACKGROUND: Anamika et al recently published in this journal a sequence alignment analysis of protein kinases encoded by the chimpanzee genome in comparison to those in the human genome. From this analysis they concluded that several chimpanzee kinases have unusual domain arrangements. RESULTS: Re-examination of these kinases reveals claimed novel arrangements cannot withstand scrutiny; each is either not novel or represents over-analysis of weakly confident computer generated gene models. Additional sequence evidence available at the time of the paper's submission either directly contradict the gene models or suggest alternate gene models. These alternate models would minimize or eliminate the observed differences between human and chimp kinases. CONCLUSION: None of the proposed novel chimpanzee kinase architectures are supported by experiment evidence. Guidelines to prevent such erroneous conclusions in similar papers are proposed.

Correlation of serpin-protease expression by comparative analysis of real-time PCR profiling data.

Imbalanced protease activity has long been recognized in the progression of disease states such as cancer and inflammation. Serpins, the largest family of endogenous protease inhibitors, target a wide variety of serine and cysteine proteases and play a role in a number of physiological and pathological states. The expression profiles of 20 serpins and 105 serine and cysteine proteases were determined across a panel of normal and diseased human tissues. In general, expression of serpins was highly restricted in both normal and diseased tissues, suggesting defined physiological roles for these protease inhibitors. A high correlation in expression for a particular serpin-protease pair in healthy tissues was often predictive of a biological interaction. The most striking finding was the dramatic change observed in the regulation of expression between proteases and their cognate inhibitors in diseased tissues. The loss of regulated serpin-protease matched expression may underlie the imbalanced protease activity observed in pathological states.

Edge-count probabilities for the identification of local protein communities and their organization.

We present a computational approach based on a local search strategy that discovers sets of proteins that preferentially interact with each other. Such sets are referred to as protein communities and are likely to represent functional modules. Preferential interaction between module members is quantified via an analytical framework based on a network null model known as the random graph with given expected degrees. Based on this framework, the concept of local protein community is generalized to that of community of communities. Protein communities and higher-level structures are extracted from two yeast protein interaction data sets and a network of published interactions between human proteins. The high level structures obtained with the human network correspond to broad biological concepts such as signal transduction, regulation of gene expression, and intercellular communication. Many of the obtained human communities are enriched, in a statistically significant way, for proteins having no clear orthologs in lower organisms. This indicates that the extracted modules are quite coherent in terms of function.