Growth-dependent Gene Expression Variation Influences the Strength of Codon Usage Biases.

The most highly expressed genes in microbial genomes tend to use a limited set of synonymous codons, often referred to as "preferred codons." The existence of preferred codons is commonly attributed to selection pressures on various aspects of protein translation including accuracy and/or speed. However, gene expression is condition-dependent and even within single-celled organisms transcript and protein abundances can vary depending on a variety of environmental and other factors. Here, we show that growth rate-dependent expression variation is an important constraint that significantly influences the evolution of gene sequences. Using large-scale transcriptomic and proteomic data sets in Escherichia coli and Saccharomyces cerevisiae, we confirm that codon usage biases are strongly associated with gene expression but highlight that this relationship is most pronounced when gene expression measurements are taken during rapid growth conditions. Specifically, genes whose relative expression increases during periods of rapid growth have stronger codon usage biases than comparably expressed genes whose expression decreases during rapid growth conditions. These findings highlight that gene expression measured in any particular condition tells only part of the story regarding the forces shaping the evolution of microbial gene sequences. More generally, our results imply that microbial physiology during rapid growth is critical for explaining long-term translational constraints.

Comparative genomic analysis of PIK3R1-mutated and wild-type breast cancers.

PURPOSE: The PIK3R1 gene encodes the regulatory subunit-p85a-of the PI3K signaling complex. Prior studies have found that pathogenic somatic alterations in PIK3R1 are enriched in human breast cancers but the genomic landscape of breast cancer patients harboring PIK3R1 mutations has not been extensively characterized. METHODS: We retrospectively analyzed 6,009 patient records that underwent next-generation sequencing (NGS) using the Tempus xT solid tumor assay. All patients had breast cancer with known HER2 (+/-) and hormone receptor (HR; +/-) status and were classified according to the presence of PIK3R1 mutations including short variants and copy number alterations. RESULTS: The frequency of PIK3R1 mutations varied according to subtype: 6% in triple negative (TNBC, 89/1,475), 2% in HER2-/HR+ (80/3,893) and 2.3% in HER2+ (15/641) (p < 0.001). Co-mutations in PTEN, TP53 and NF1 were significantly enriched, co-mutations in PIK3CA were significantly less prevalent, and tumor mutational burden was significantly higher in PIK3R1-mutated HER2- samples relative to PIK3R1 wild-type. At the transcriptional-level, PIK3R1 RNA expression in HER2- disease was significantly higher in PIK3R1-mutated (excluding copy number loss) samples, regardless of subtype. CONCLUSION: This is the largest investigation of the PIK3R1 mutational landscape in breast cancer patients (n = 6,009). PIK3R1 mutations were more common in triple-negative breast cancer (~ 6%) than in HER2 + or HER2-/HR + disease (approximately 2%). While alterations in the PI3K/AKT pathway are often actionable in HER2-/HR + breast cancer, our study suggests that PIK3R1 could be an important target in TNBC as well.

Mutational characterization and mapping of the 70S ribosome active site.

The synthetic capability of the Escherichia coli ribosome has attracted efforts to repurpose it for novel functions, such as the synthesis of polymers containing non-natural building blocks. However, efforts to repurpose ribosomes are limited by the lack of complete peptidyl transferase center (PTC) active site mutational analyses to inform design. To address this limitation, we leverage an in vitro ribosome synthesis platform to build and test every possible single nucleotide mutation within the PTC-ring, A-loop and P-loop, 180 total point mutations. These mutant ribosomes were characterized by assessing bulk protein synthesis kinetics, readthrough, assembly, and structure mapping. Despite the highly-conserved nature of the PTC, we found that >85% of the PTC nucleotides possess mutational flexibility. Our work represents a comprehensive single-point mutant characterization and mapping of the 70S ribosome's active site. We anticipate that it will facilitate structure-function relationships within the ribosome and make possible new synthetic biology applications.

Within-Gene Shine-Dalgarno Sequences Are Not Selected for Function.

The Shine-Dalgarno (SD) sequence motif facilitates translation initiation and is frequently found upstream of bacterial start codons. However, thousands of instances of this motif occur throughout the middle of protein coding genes in a typical bacterial genome. Here, we use comparative evolutionary analysis to test whether SD sequences located within genes are functionally constrained. We measure the conservation of SD sequences across Enterobacteriales, and find that they are significantly less conserved than expected. Further, the strongest SD sequences are the least conserved whereas we find evidence of conservation for the weakest possible SD sequences given amino acid constraints. Our findings indicate that most SD sequences within genes are likely to be deleterious and removed via selection. To illustrate the origin of these deleterious costs, we show that ATG start codons are significantly depleted downstream of SD sequences within genes, highlighting the constraint that these sequences impose on the surrounding nucleotides to minimize the potential for erroneous translation initiation.

Diversity of Translation Initiation Mechanisms across Bacterial Species Is Driven by Environmental Conditions and Growth Demands.

The Shine-Dalgarno (SD) sequence motif is frequently found upstream of protein coding genes and is thought to be the dominant mechanism of translation initiation used by bacteria. Experimental studies have shown that the SD sequence facilitates start codon recognition and enhances translation initiation by directly interacting with the highly conserved anti-SD sequence on the 30S ribosomal subunit. However, the proportion of SD-led genes within a genome varies across species and the factors governing this variation in translation initiation mechanisms remain largely unknown. Here, we conduct a phylogenetically informed analysis and find that species capable of rapid growth contain a higher proportion of SD-led genes throughout their genomes. We show that SD sequence utilization covaries with a suite of genomic features that are important for efficient translation initiation and elongation. In addition to these endogenous genomic factors, we further show that exogenous environmental factors may influence the evolution of translation initiation mechanisms by finding that thermophilic species contain significantly more SD-led genes than mesophiles. Our results demonstrate that variation in translation initiation mechanisms across bacterial species is predictable and is a consequence of differential life-history strategies related to maximum growth rate and environmental-specific constraints.

Phylogenetic weighting does little to improve the accuracy of evolutionary coupling analyses.

Homologous sequence alignments contain important information about the constraints that shape protein family evolution. Correlated changes between different residues, for instance, can be highly predictive of physical contacts within three-dimensional structures. Detecting such co-evolutionary signals via direct coupling analysis is particularly challenging given the shared phylogenetic history and uneven sampling of different lineages from which protein sequences are derived. Current best practices for mitigating such effects include sequence-identity-based weighting of input sequences and post-hoc re-scaling of evolutionary coupling scores. However, numerous weighting schemes have been previously developed for other applications, and it is unknown whether any of these schemes may better account for phylogenetic artifacts in evolutionary coupling analyses. Here, we show across a dataset of 150 diverse protein families that the current best practices out-perform several alternative sequence- and tree-based weighting methods. Nevertheless, we find that sequence weighting in general provides only a minor benefit relative to post-hoc transformations that re-scale the derived evolutionary couplings. While our findings do not rule out the possibility that an as-yet-untested weighting method may show improved results, the similar predictive accuracies that we observe across distinct weighting methods suggests that there may be little room for further improvement on top of existing strategies.

NullSeq: A Tool for Generating Random Coding Sequences with Desired Amino Acid and GC Contents.

The existence of over- and under-represented sequence motifs in genomes provides evidence of selective evolutionary pressures on biological mechanisms such as transcription, translation, ligand-substrate binding, and host immunity. In order to accurately identify motifs and other genome-scale patterns of interest, it is essential to be able to generate accurate null models that are appropriate for the sequences under study. While many tools have been developed to create random nucleotide sequences, protein coding sequences are subject to a unique set of constraints that complicates the process of generating appropriate null models. There are currently no tools available that allow users to create random coding sequences with specified amino acid composition and GC content for the purpose of hypothesis testing. Using the principle of maximum entropy, we developed a method that generates unbiased random sequences with pre-specified amino acid and GC content, which we have developed into a python package. Our method is the simplest way to obtain maximally unbiased random sequences that are subject to GC usage and primary amino acid sequence constraints. Furthermore, this approach can easily be expanded to create unbiased random sequences that incorporate more complicated constraints such as individual nucleotide usage or even di-nucleotide frequencies. The ability to generate correctly specified null models will allow researchers to accurately identify sequence motifs which will lead to a better understanding of biological processes as well as more effective engineering of biological systems.

Quantifying position-dependent codon usage bias.

Although the mapping of codon to amino acid is conserved across nearly all species, the frequency at which synonymous codons are used varies both between organisms and between genes from the same organism. This variation affects diverse cellular processes including protein expression, regulation, and folding. Here, we mathematically model an additional layer of complexity and show that individual codon usage biases follow a position-dependent exponential decay model with unique parameter fits for each codon. We use this methodology to perform an in-depth analysis on codon usage bias in the model organism Escherichia coli. Our methodology shows that lowly and highly expressed genes are more similar in their codon usage patterns in the 5'-gene regions, but that these preferences diverge at distal sites resulting in greater positional dependency (pD, which we mathematically define later) for highly expressed genes. We show that position-dependent codon usage bias is partially explained by the structural requirements of mRNAs that results in increased usage of A/T rich codons shortly after the gene start. However, we also show that the pD of 4- and 6-fold degenerate codons is partially related to the gene copy number of cognate-tRNAs supporting existing hypotheses that posit benefits to a region of slow translation in the beginning of coding sequences. Lastly, we demonstrate that viewing codon usage bias through a position-dependent framework has practical utility by improving accuracy of gene expression prediction when incorporating positional dependencies into the Codon Adaptation Index model.

Concurrent Tissue and Circulating Tumor DNA Molecular Profiling to Detect Guideline-Based Targeted Mutations in a Multicancer Cohort.

Importance: Tissue-based next-generation sequencing (NGS) of solid tumors is the criterion standard for identifying somatic mutations that can be treated with National Comprehensive Cancer Network guideline-recommended targeted therapies. Sequencing of circulating tumor DNA (ctDNA) can also identify tumor-derived mutations, and there is increasing clinical evidence supporting ctDNA testing as a diagnostic tool. The clinical value of concurrent tissue and ctDNA profiling has not been formally assessed in a large, multicancer cohort from heterogeneous clinical settings. Objective: To evaluate whether patients concurrently tested with both tissue and ctDNA NGS testing have a higher rate of detection of guideline-based targeted mutations compared with tissue testing alone. Design, Setting, and Participants: This cohort study comprised 3209 patients who underwent sequencing between May 2020, and December 2022, within the deidentified, Tempus multimodal database, consisting of linked molecular and clinical data. Included patients had stage IV disease (non-small cell lung cancer, breast cancer, prostate cancer, or colorectal cancer) with sufficient tissue and blood sample quantities for analysis. Exposures: Received results from tissue and plasma ctDNA genomic profiling, with biopsies and blood draws occurring within 30 days of one another. Main Outcomes and Measures: Detection rates of guideline-based variants found uniquely by ctDNA and tissue profiling. Results: The cohort of 3209 patients (median age at diagnosis of stage IV disease, 65.3 years [2.5%-97.5% range, 43.3-83.3 years]) who underwent concurrent tissue and ctDNA testing included 1693 women (52.8%). Overall, 1448 patients (45.1%) had a guideline-based variant detected. Of these patients, 9.3% (135 of 1448) had variants uniquely detected by ctDNA profiling, and 24.2% (351 of 1448) had variants uniquely detected by solid-tissue testing. Although largely concordant with one another, differences in the identification of actionable variants by either assay varied according to cancer type, gene, variant, and ctDNA burden. Of 352 patients with breast cancer, 20.2% (71 of 352) with actionable variants had unique findings in ctDNA profiling results. Most of these unique, actionable variants (55.0% [55 of 100]) were found in ESR1, resulting in a 24.7% increase (23 of 93) in the identification of patients harboring an ESR1 mutation relative to tissue testing alone. Conclusions and Relevance: This study suggests that unique actionable biomarkers are detected by both concurrent tissue and ctDNA testing, with higher ctDNA identification among patients with breast cancer. Integration of concurrent NGS testing into the routine management of advanced solid cancers may expand the delivery of molecularly guided therapy and improve patient outcomes.

N-methyl-D-aspartate receptor mechanosensitivity is governed by C terminus of NR2B subunit.

N-methyl-D-aspartate receptors (NMDARs), critical mediators of both physiologic and pathologic neurological signaling, have previously been shown to be sensitive to mechanical stretch through the loss of its native Mg(2+) block. However, the regulation of this mechanosensitivity has yet to be further explored. Furthermore, as it has become apparent that NMDAR-mediated signaling is dependent on specific NMDAR subtypes, as governed by the identity of the NR2 subunit, a crucial unanswered question is the role of subunit composition in observed NMDAR mechanosensitivity. Here, we used a recombinant system to assess the mechanosensitivity of specific subtypes and demonstrate that the mechanosensitive property is uniquely governed by the NR2B subunit. NR1/NR2B NMDARs displayed significant stretch sensitivity, whereas NR1/NR2A NMDARs did not respond to stretch. Furthermore, NR2B mechanosensitivity was regulated by PKC activity, because PKC inhibition reduced stretch responses in transfected HEK 293 cells and primary cortical neurons. Finally, using NR2B point mutations, we identified a PKC phosphorylation site, Ser-1323 on NR2B, as a unique critical regulator of stretch sensitivity. These data suggest that the selective mechanosensitivity of NR2B can significantly impact neuronal response to traumatic brain injury and illustrate that the mechanical tone of the neuron can be dynamically regulated by PKC activity.

Growth-dependent gene expression variation influences the strength of codon usage biases.

The most highly expressed genes in microbial genomes tend to use a limited set of synonymous codons, often referred to as "preferred codons." The existence of preferred codons is commonly attributed to selection pressures on various aspects of protein translation including accuracy and/or speed. However, gene expression is condition-dependent and even within single-celled organisms transcript and protein abundances can vary depending on a variety of environmental and other factors. Here, we show that growth rate-dependent expression variation is an important constraint that significantly influences the evolution of gene sequences. Using large-scale transcriptomic and proteomic data sets in Escherichia coli and Saccharomyces cerevisiae, we confirm that codon usage biases are strongly associated with gene expression but highlight that this relationship is most pronounced when gene expression measurements are taken during rapid growth conditions. Specifically, genes whose relative expression increases during periods of rapid growth have stronger codon usage biases than comparably expressed genes whose expression decreases during rapid growth conditions. These findings highlight that gene expression measured in any particular condition tells only part of the story regarding the forces shaping the evolution of microbial gene sequences. More generally, our results imply that microbial physiology during rapid growth is critical for explaining long-term translational constraints.

Validation of a Transcriptome-Based Assay for Classifying Cancers of Unknown Primary Origin.

INTRODUCTION: Cancers assume a variety of distinct histologies, and may originate from a myriad of sites including solid organs, hematopoietic cells, and connective tissue. Clinical decision-making based on consensus guidelines such as the National Comprehensive Cancer Network (NCCN) is often predicated on a specific histologic and anatomic diagnosis, supported by clinical features and pathologist interpretation of morphology and immunohistochemical (IHC) staining patterns. However, in patients with nonspecific morphologic and IHC findings-in addition to ambiguous clinical presentations such as recurrence versus new primary-a definitive diagnosis may not be possible, resulting in the patient being categorized as having a cancer of unknown primary (CUP). Therapeutic options and clinical outcomes are poor for patients with CUP, with a median survival of 8-11 months. METHODS: Here, we describe and validate the Tempus Tumor Origin (Tempus TO) assay, an RNA-sequencing-based machine learning classifier capable of discriminating between 68 clinically relevant cancer subtypes. Model accuracy was assessed using primary and/or metastatic samples with known subtype. RESULTS: We show that the Tempus TO model is 91% accurate when assessed on both a retrospectively held out cohort and a set of samples sequenced after model freeze that collectively contained 9210 total samples with known diagnoses. When evaluated on a cohort of CUPs, the model recapitulated established associations between genomic alterations and cancer subtype. DISCUSSION: Combining diagnostic prediction tests (e.g., Tempus TO) with sequencing-based variant reporting (e.g., Tempus xT) may expand therapeutic options for patients with cancers of unknown primary or uncertain histology.

Assessing the utility of molecular diagnostic classification for cancers of unknown primary.

BACKGROUND: Roughly 5% of metastatic cancers present with uncertain origin, for which molecular classification could influence subsequent management; however, prior studies of molecular diagnostic classifiers have reported mixed results with regard to clinical impact. In this retrospective study, we evaluated the utility of a novel molecular diagnostic classifier by assessing theoretical changes in treatment and additional testing recommendations from oncologists before and after the review of classifier predictions. METHODS: We retrospectively analyzed de-identified records from 289 patients with a consensus diagnosis of cancer of uncertain/unknown primary (CUP). Two (or three, if adjudication was required) independent oncologists separately reviewed patient clinical information to determine the course of treatment before they reviewed results from the molecular diagnostic classifier and subsequently evaluated whether the predicted diagnosis would alter their treatment plan. RESULTS: Results from the molecular diagnostic classifier changed the consensus oncologist-reported treatment recommendations for 235 out of 289 patients (81.3%). At the level of individual oncologist reviews (n = 414), 64.7% (n = 268) of treatment recommendations were based on CUP guidelines prior to review of results from the molecular diagnostic classifier. After seeing classifier results, 98.1% (n = 207) of the reviews, where treatment was specified (n = 211), were guided by the tissue of origin-specific guidelines. Overall, 89.9% of the 414 total reviews either expressed strong agreement (n = 242) or agreement (n = 130) that the molecular diagnostic classifier result increased confidence in selecting the most appropriate treatment regimen. CONCLUSIONS: A retrospective review of CUP cases demonstrates that a novel molecular diagnostic classifier could affect treatment in the majority of patients, supporting its clinical utility. Further studies are needed to prospectively evaluate whether the use of molecular diagnostic classifiers improves clinical outcomes in CUP patients.

Computational investigation of the changing patterns of subtype specific NMDA receptor activation during physiological glutamatergic neurotransmission.

NMDA receptors (NMDARs) are the major mediator of the postsynaptic response during synaptic neurotransmission. The diversity of roles for NMDARs in influencing synaptic plasticity and neuronal survival is often linked to selective activation of multiple NMDAR subtypes (NR1/NR2A-NMDARs, NR1/NR2B-NMDARs, and triheteromeric NR1/NR2A/NR2B-NMDARs). However, the lack of available pharmacological tools to block specific NMDAR populations leads to debates on the potential role for each NMDAR subtype in physiological signaling, including different models of synaptic plasticity. Here, we developed a computational model of glutamatergic signaling at a prototypical dendritic spine to examine the patterns of NMDAR subtype activation at temporal and spatial resolutions that are difficult to obtain experimentally. We demonstrate that NMDAR subtypes have different dynamic ranges of activation, with NR1/NR2A-NMDAR activation sensitive at univesicular glutamate release conditions, and NR2B containing NMDARs contributing at conditions of multivesicular release. We further show that NR1/NR2A-NMDAR signaling dominates in conditions simulating long-term depression (LTD), while the contribution of NR2B containing NMDAR significantly increases for stimulation frequencies that approximate long-term potentiation (LTP). Finally, we show that NR1/NR2A-NMDAR content significantly enhances response magnitude and fidelity at single synapses during chemical LTP and spike timed dependent plasticity induction, pointing out an important developmental switch in synaptic maturation. Together, our model suggests that NMDAR subtypes are differentially activated during different types of physiological glutamatergic signaling, enhancing the ability for individual spines to produce unique responses to these different inputs.

Generating dynamic gene expression patterns without the need for regulatory circuits.

Synthetic biology has successfully advanced our ability to design and implement complex, time-varying genetic circuits to control the expression of recombinant proteins. However, these circuits typically require the production of regulatory genes whose only purpose is to coordinate expression of other genes. When designing very small genetic constructs, such as viral genomes, we may want to avoid introducing such auxiliary gene products while nevertheless encoding complex expression dynamics. To this end, here we demonstrate that varying only the placement and strengths of promoters, terminators, and RNase cleavage sites in a computational model of a bacteriophage genome is sufficient to achieve solutions to a variety of basic gene expression patterns. We discover these genetic solutions by computationally evolving genomes to reproduce desired gene expression time-course data. Our approach shows that non-trivial patterns can be evolved, including patterns where the relative ordering of genes by abundance changes over time. We find that some patterns are easier to evolve than others, and comparable expression patterns can be achieved via different genetic architectures. Our work opens up a novel avenue to genome engineering via fine-tuning the balance of gene expression and gene degradation rates.

Early divergence of translation initiation and elongation factors.

Protein translation is a foundational attribute of all living cells. The translation function carried out by the ribosome critically depends on an assortment of protein interaction partners, collectively referred to as the translation machinery. Various studies suggest that the diversification of the translation machinery occurred prior to the last universal common ancestor, yet it is unclear whether the predecessors of the extant translation machinery factors were functionally distinct from their modern counterparts. Here we reconstructed the shared ancestral trajectory and subsequent evolution of essential translation factor GTPases, elongation factor EF-Tu (aEF-1A/eEF-1A), and initiation factor IF2 (aIF5B/eIF5B). Based upon their similar functions and structural homologies, it has been proposed that EF-Tu and IF2 emerged from an ancient common ancestor. We generated the phylogenetic tree of IF2 and EF-Tu proteins and reconstructed ancestral sequences corresponding to the deepest nodes in their shared evolutionary history, including the last common IF2 and EF-Tu ancestor. By identifying the residue and domain substitutions, as well as structural changes along the phylogenetic history, we developed an evolutionary scenario for the origins, divergence and functional refinement of EF-Tu and IF2 proteins. Our analyses suggest that the common ancestor of IF2 and EF-Tu was an IF2-like GTPase protein. Given the central importance of the translation machinery to all cellular life, its earliest evolutionary constraints and trajectories are key to characterizing the universal constraints and capabilities of cellular evolution.

Discovery of Unannotated Small Open Reading Frames in Streptococcus pneumoniae D39 Involved in Quorum Sensing and Virulence Using Ribosome Profiling.

Streptococcus pneumoniae, an opportunistic human pathogen, is the leading cause of community-acquired pneumonia and an agent of otitis media, septicemia, and meningitis. Although genomic and transcriptomic studies of S. pneumoniae have provided detailed perspectives on gene content and expression programs, they have lacked information pertaining to the translational landscape, particularly at a resolution that identifies commonly overlooked small open reading frames (sORFs), whose importance is increasingly realized in metabolism, regulation, and virulence. To identify protein-coding sORFs in S. pneumoniae, antibiotic-enhanced ribosome profiling was conducted. Using translation inhibitors, 114 novel sORFs were detected, and the expression of a subset of them was experimentally validated. Two loci associated with virulence and quorum sensing were examined in deeper detail. One such sORF, rio3, overlaps with the noncoding RNA srf-02 that was previously implicated in pathogenesis. Targeted mutagenesis parsing rio3 from srf-02 revealed that rio3 is responsible for the fitness defect seen in a murine nasopharyngeal colonization model. Additionally, two novel sORFs located adjacent to the quorum sensing receptor rgg1518 were found to impact regulatory activity. Our findings emphasize the importance of sORFs present in the genomes of pathogenic bacteria and underscore the utility of ribosome profiling for identifying the bacterial translatome. IMPORTANCE This work employed pleuromutilin-assisted ribosome profiling using retapamulin (Ribo-RET) to identify genome-wide translation start sites in the human pathogen Streptococcus pneumoniae. We identified 114 unannotated intergenic small open reading frames (sORFs). The described procedures and data sets provide a model for microbiologists seeking to explore the translational landscape of bacteria. The biological roles of four sORF examples are characterized: two control the regulation of a cell-cell communication (quorum sensing) system, one contributes to the ability of S. pneumoniae to colonize the upper respiratory tract of mice, and a fourth governs the translation of PrfB, a protein enabling ribosome release at stop codons. We propose that Ribo-RET is a valuable approach to identifying unstudied microproteins and difficult-to-find pheromone genes used by Gram-positive organisms, whose genomes are replete with pheromone receptors.

Clinical, Genomic, and Transcriptomic Data Profiling of Biliary Tract Cancer Reveals Subtype-Specific Immune Signatures.

PURPOSE: Biliary tract cancers (BTCs) are aggressive cancers that carry a poor prognosis. An enhanced understanding of the immune landscape of anatomically and molecularly defined subsets of BTC may improve patient selection for immunotherapy and inform immune-based combination treatment strategies. METHODS: We analyzed deidentified clinical, genomic, and transcriptomic data from the Tempus database to determine the mutational frequency and mutational clustering across the three major BTC subtypes (intrahepatic cholangiocarcinoma [IHC], extrahepatic cholangiocarcinoma, and gallbladder cancer). We subsequently determined the relationship between specific molecular alterations and anatomical subsets and features of the BTC immune microenvironment. RESULTS: We analyzed 454 samples of BTC, of which the most commonly detected alterations were TP53 (42.5%), CDKN2A (23.4%), ARID1A (19.6%), BAP1 (15.5%), KRAS (15%), CDKN2B (14.2%), PBRM1 (11.7%), IDH1 (11.7%), TERT (8.4%), KMT2C (10.4%) and LRP1B (8.4%), and FGFR2 fusions (8.7%). Potentially actionable molecular alterations were identified in 30.5% of BTCs including 39.1% of IHC. Integrative cluster analysis revealed four distinct molecular clusters, with cluster 4 predominately associated with FGFR2 rearrangements and BAP1 mutations in IHC. Immune-related biomarkers indicative of an inflamed tumor-immune microenvironment were elevated in gallbladder cancers and in cluster 1, which was enriched for TP53, KRAS, and ATM mutations. Multiple common driver genes, including TP53, FGFR2, IDH1, TERT, BRAF, and BAP1, were individually associated with unique BTC immune microenvironments. CONCLUSION: BTC subtypes exhibit diverse DNA alterations, RNA inflammatory signatures, and immune biomarkers. The association between specific BTC anatomical subsets, molecular alterations, and immunophenotypes highlights new opportunities for therapeutic development.

BACPHLIP: predicting bacteriophage lifestyle from conserved protein domains.

Bacteriophages are broadly classified into two distinct lifestyles: temperate and virulent. Temperate phages are capable of a latent phase of infection within a host cell (lysogenic cycle), whereas virulent phages directly replicate and lyse host cells upon infection (lytic cycle). Accurate lifestyle identification is critical for determining the role of individual phage species within ecosystems and their effect on host evolution. Here, we present BACPHLIP, a BACterioPHage LIfestyle Predictor. BACPHLIP detects the presence of a set of conserved protein domains within an input genome and uses this data to predict lifestyle via a Random Forest classifier that was trained on a dataset of 634 phage genomes. On an independent test set of 423 phages, BACPHLIP has an accuracy of 98% greatly exceeding that of the previously existing tools (79%). BACPHLIP is freely available on GitHub (https://github.com/adamhockenberry/bacphlip) and the code used to build and test the classifier is provided in a separate repository (https://github.com/adamhockenberry/bacphlip-model-dev) for users wishing to interrogate and re-train the underlying classification model.

Sequential Glycosylation of Proteins with Substrate-Specific N-Glycosyltransferases.

Protein glycosylation is a common post-translational modification that influences the functions and properties of proteins. Despite advances in methods to produce defined glycoproteins by chemoenzymatic elaboration of monosaccharides, the understanding and engineering of glycoproteins remain challenging, in part, due to the difficulty of site-specifically controlling glycosylation at each of several positions within a protein. Here, we address this limitation by discovering and exploiting the unique, conditionally orthogonal peptide acceptor specificities of N-glycosyltransferases (NGTs). We used cell-free protein synthesis and mass spectrometry of self-assembled monolayers to rapidly screen 41 putative NGTs and rigorously characterize the unique acceptor sequence preferences of four NGT variants using 1254 acceptor peptides and 8306 reaction conditions. We then used the optimized NGT-acceptor sequence pairs to sequentially install monosaccharides at four sites within one target protein. This strategy to site-specifically control the installation of N-linked monosaccharides for elaboration to a variety of functional N-glycans overcomes a major limitation in synthesizing defined glycoproteins for research and therapeutic applications.