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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Evolutionary couplings detect side-chain interactions

Patterns of amino acid covariation in large protein sequence alignments can inform the prediction of de novo protein structures, binding interfaces, and mutational effects. While algorithms that detect these so-called evolutionary couplings between residues have proven useful for practical applications, less is known about how and why these methods perform so well, and what insights into biological processes can be gained from their application. Evolutionary coupling algorithms are commonly benchmarked by comparison to true structural contacts derived from solved protein structures. However, the methods used to determine true structural contacts are not standardized and different definitions of structural contacts may have important consequences for interpreting the results from evolutionary coupling analyses and understanding their overall utility. Here, we show that evolutionary coupling analyses are significantly more likely to identify structural contacts between side-chain atoms than between backbone atoms. We use both simulations and empirical analyses to highlight that purely backbone-based definitions of true residue–residue contacts (i.e., based on the distance between Cα atoms) may underestimate the accuracy of evolutionary coupling algorithms by as much as 40% and that a commonly used reference point (Cβ atoms) underestimates the accuracy by 10–15%. These findings show that co-evolutionary outcomes differ according to which atoms participate in residue–residue interactions and suggest that accounting for different interaction types may lead to further improvements to contact-prediction methods.

A novel framework for evaluating the performance of codon usage bias metrics

The unequal utilization of synonymous codons affects numerous cellular processes including translation rates, protein folding and mRNA degradation. In order to understand the biological impact of variable codon usage bias (CUB) between genes and genomes, it is crucial to be able to accurately measure CUB for a given sequence. A large number of metrics have been developed for this purpose, but there is currently no way of systematically testing the accuracy of individual metrics or knowing whether metrics provide consistent results. This lack of standardization can result in false-positive and false-negative findings if underpowered or inaccurate metrics are applied as tools for discovery. Here, we show that the choice of CUB metric impacts both the significance and measured effect sizes in numerous empirical datasets, raising questions about the generality of findings in published research. To bring about standardization, we developed a novel method to create synthetic protein-coding DNA sequences according to different models of codon usage. We use these benchmark sequences to identify the most accurate and robust metrics with regard to sequence length, GC content and amino acid heterogeneity. Finally, we show how our benchmark can aid the development of new metrics by providing feedback on its performance compared to the state of the art.

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.

Assembly and functionality of the ribosome with tethered subunits

Ribo-T is an engineered ribosome whose small and large subunits are tethered together by linking 16S rRNA and 23S rRNA in a single molecule. Although Ribo-T can support cell proliferation in the absence of wild type ribosomes, Ribo-T cells grow slower than those with wild type ribosomes. Here, we show that cell growth defect is likely explained primarily by slow Ribo-T assembly rather than its imperfect functionality. Ribo-T maturation is stalled at a late assembly stage. Several post-transcriptional rRNA modifications and some ribosomal proteins are underrepresented in the accumulated assembly intermediates and rRNA ends are incompletely trimmed. Ribosome profiling of Ribo-T cells shows no defects in translation elongation but reveals somewhat higher occupancy by Ribo-T of the start codons and to a lesser extent stop codons, suggesting that subunit tethering mildly affects the initiation and termination stages of translation. Understanding limitations of Ribo-T system offers ways for its future development.

The tethered ribosome system Ribo-T supports cell proliferation though at a reduced rate. Here the authors show this is due to slower ribosome assembly instead of reduced functionality.

Predicting bacterial growth conditions from mRNA and protein abundances

Cells respond to changing nutrient availability and external stresses by altering the expression of individual genes. Condition-specific gene expression patterns may thus provide a promising and low-cost route to quantifying the presence of various small molecules, toxins, or species-interactions in natural environments. However, whether gene expression signatures alone can predict individual environmental growth conditions remains an open question. Here, we used machine learning to predict 16 closely-related growth conditions using 155 datasets of E. coli transcript and protein abundances. We show that models are able to discriminate between different environmental features with a relatively high degree of accuracy. We observed a small but significant increase in model accuracy by combining transcriptome and proteome-level data, and we show that measurements from stationary phase cells typically provide less useful information for discriminating between conditions as compared to exponentially growing populations. Nevertheless, with sufficient training data, gene expression measurements from a single species are capable of distinguishing between environmental conditions that are separated by a single environmental variable.

Neisseria gonorrhoeae Exposed to Sublethal Levels of Hydrogen Peroxide Mounts a Complex Transcriptional Response

Neisseria gonorrhoeae mounts a substantial transcriptional program in response to hydrogen peroxide (HP), a prominent reactive oxygen species (ROS) encountered during infection. We tested which strain FA1090 genes show differential transcript abundance in response to sublethal amounts of HP to differentiate HP-responsive signaling from widespread cellular death and dysregulation. RNA sequencing (RNA-Seq) revealed that 150 genes were significantly upregulated and 143 genes downregulated following HP exposure. We annotated HP-responsive operons and all transcriptional start sites (TSSs) and identified which TSSs responded to HP treatment. We compared the HP responses and other previously reported genes and found only partial overlapping of other regulatory networks, indicating that the response to HP involves multiple biological functions. Using a representative subset of responsive genes, we validated the RNA-Seq results and found that the HP transcriptome was similar to that of sublethal organic peroxide. None of the genes in the representative subset, however, responded to sublethal levels of HOCl or O2 -. These results support the idea that N. gonorrhoeae may use variations in HP levels as a signal for different stages of infection. IMPORTANCE The strict human pathogen Neisseria gonorrhoeae is the only causative agent of the sexually transmitted disease gonorrhea. This bacterium encounters hydrogen peroxide produced from host cells during infection, but the organism survives in the presence of this antimicrobial agent. This work shows that the bacterium responds to hydrogen peroxide by regulating the expression of many genes involved in multiple processes.

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.

Leveraging genome-wide datasets to quantify the functional role of the anti-Shine-Dalgarno sequence in regulating translation efficiency

Studies dating back to the 1970s established that sequence complementarity between the anti-Shine–Dalgarno (aSD) sequence on prokaryotic ribosomes and the 5′ untranslated region of mRNAs helps to facilitate translation initiation. The optimal location of aSD sequence binding relative to the start codon, the full extents of the aSD sequence and the functional form of the relationship between aSD sequence complementarity and translation efficiency have not been fully resolved. Here, we investigate these relationships by leveraging the sequence diversity of endogenous genes and recently available genome-wide estimates of translation efficiency. We show that—after accounting for predicted mRNA structure—aSD sequence complementarity increases the translation of endogenous mRNAs by roughly 50%. Further, we observe that this relationship is nonlinear, with translation efficiency maximized for mRNAs with intermediate levels of aSD sequence complementarity. The mechanistic insights that we observe are highly robust: we find nearly identical results in multiple datasets spanning three distantly related bacteria. Further, we verify our main conclusions by re-analysing a controlled experimental dataset.

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.

The generation of random sequences is instrumental to the accurate identification of non-random motifs within genomes, yet there are currently no tools available that allow users to simultaneously specify amino acid and GC composition to create random coding sequences. Here, we develop an algorithm based on maximum entropy that consistently generates fully random nucleotide sequences with the desired amino acid composition and GC content.

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.

Synthetic in vitro circuits

Inspired by advances in the ability to construct programmable circuits in living organisms, in vitro circuits are emerging as a viable platform for designing, understanding, and exploiting dynamic biochemical circuitry. In vitro systems allow researchers to directly access and manipulate biomolecular parts without the unwieldy complexity and intertwined dependencies that often exist in vivo. Experimental and computational foundations in DNA, DNA/RNA, and DNA/RNA/protein based circuitry have given rise to systems with more than 100 programmed molecular constituents. Functionally, they have diverse capabilities including: complex mathematical calculations, associative memory tasks, and sensing of small molecules. Progress in this field is showing that cell-free synthetic biology is a versatile testing ground for understanding native biological circuits and engineering novel functionality.

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.

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.

Release of glutamate from one neuron onto glutamate receptors on adjacent neurons serves as the primary basis for neuronal communication. Further, different types of glutamate signals produce unique responses within the neuronal network, providing the ability for glutamate receptors to discriminate between alternative types of signaling. The NMDA receptor (NMDAR) is a glutamate receptor that mediates a variety of physiological functions, including the molecular basis for learning and memory. These receptors exist as a variety of subtypes, and this molecular heterogeneity is used to explain the diversity in signaling initiated by NMDARs. However, the lack of reliable experimental tools to control the activation of each subtype has led to debate over the subtype specific roles of the NMDAR. We have developed a stochastic model of glutamate receptor activation at a single synapse and find that NMDAR subtypes detect different types of glutamate signals. Moreover, the presence of multiple populations of NMDAR subtypes on a given neuron allows for differential patterns of NMDAR activation in response to varied glutamate inputs. This model demonstrates how NMDAR subtypes enable effective and reliable communication within neuronal networks and can be used as a tool to examine specific roles of NMDAR subtypes in neuronal function.