Symmetry, gauge freedoms, and the interpretability of sequence-function relationships.

Quantitative models of sequence-function relationships, which describe how biological sequences encode functional activities, are ubiquitous in modern biology. One important aspect of these models is that they commonly exhibit gauge freedoms, i.e., directions in parameter space that do not affect model predictions. In physics, gauge freedoms arise when physical theories are formulated in ways that respect fundamental symmetries. However, the connections that gauge freedoms in models of sequence-function relationships have to the symmetries of sequence space have yet to be systematically studied. Here we study the gauge freedoms of models that respect a specific symmetry of sequence space: the group of position-specific character permutations. We find that gauge freedoms arise when the transformations of model parameters that compensate for these symmetry transformations are described by redundant irreducible matrix representations. Based on this finding, we describe an "embedding distillation" procedure that enables analytic calculation of the dimension of the space of gauge freedoms, as well as efficient computation of a sparse basis for this space. Finally, we show that the ability to interpret model parameters as quantifying allelic effects places strong constraints on the form that models can take, and in particular show that all nontrivial equivariant models of allelic effects must exhibit gauge freedoms. Our work thus advances the understanding of the relationship between symmetries and gauge freedoms in quantitative models of sequence-function relationships.

Gauge fixing for sequence-function relationships.

Quantitative models of sequence-function relationships are ubiquitous in computational biology, e.g., for modeling the DNA binding of transcription factors or the fitness landscapes of proteins. Interpreting these models, however, is complicated by the fact that the values of model parameters can often be changed without affecting model predictions. Before the values of model parameters can be meaningfully interpreted, one must remove these degrees of freedom (called "gauge freedoms" in physics) by imposing additional constraints (a process called "fixing the gauge"). However, strategies for fixing the gauge of sequence-function relationships have received little attention. Here we derive an analytically tractable family of gauges for a large class of sequence-function relationships. These gauges are derived in the context of models with all-order interactions, but an important subset of these gauges can be applied to diverse types of models, including additive models, pairwise-interaction models, and models with higher-order interactions. Many commonly used gauges are special cases of gauges within this family. We demonstrate the utility of this family of gauges by showing how different choices of gauge can be used both to explore complex activity landscapes and to reveal simplified models that are approximately correct within localized regions of sequence space. The results provide practical gauge-fixing strategies and demonstrate the utility of gauge-fixing for model exploration and interpretation.

Specificity, synergy, and mechanisms of splice-modifying drugs.

Drugs that target pre-mRNA splicing hold great therapeutic potential, but the quantitative understanding of how these drugs work is limited. Here we introduce mechanistically interpretable quantitative models for the sequence-specific and concentration-dependent behavior of splice-modifying drugs. Using massively parallel splicing assays, RNA-seq experiments, and precision dose-response curves, we obtain quantitative models for two small-molecule drugs, risdiplam and branaplam, developed for treating spinal muscular atrophy. The results quantitatively characterize the specificities of risdiplam and branaplam for 5' splice site sequences, suggest that branaplam recognizes 5' splice sites via two distinct interaction modes, and contradict the prevailing two-site hypothesis for risdiplam activity at SMN2 exon 7. The results also show that anomalous single-drug cooperativity, as well as multi-drug synergy, are widespread among small-molecule drugs and antisense-oligonucleotide drugs that promote exon inclusion. Our quantitative models thus clarify the mechanisms of existing treatments and provide a basis for the rational development of new therapies.

Interpreting cis-regulatory mechanisms from genomic deep neural networks using surrogate models.

Deep neural networks (DNNs) have greatly advanced the ability to predict genome function from sequence. Interpreting genomic DNNs in terms of biological mechanisms, however, remains difficult. Here we introduce SQUID, a genomic DNN interpretability framework based on surrogate modeling. SQUID approximates genomic DNNs in user-specified regions of sequence space using surrogate models, i.e., simpler models that are mechanistically interpretable. Importantly, SQUID removes the confounding effects that nonlinearities and heteroscedastic noise in functional genomics data can have on model interpretation. Benchmarking analysis on multiple genomic DNNs shows that SQUID, when compared to established interpretability methods, identifies motifs that are more consistent across genomic loci and yields improved single-nucleotide variant-effect predictions. SQUID also supports surrogate models that quantify epistatic interactions within and between cis-regulatory elements. SQUID thus advances the ability to mechanistically interpret genomic DNNs.

Mentorship Landscape and Common Practices in an Academic Pharmacy Association.

OBJECTIVES: To explore the landscape of mentorship within professional associations in pharmacy academia, including reviewing available literature and describing currently available programs within the American Association of Colleges of Pharmacy, and recommend key considerations for the development of mentorship programs within professional associations. FINDINGS: A literature review of mentorship programs within professional associations for pharmacy academics was conducted, with a total of 5 articles identified and summarized. Additionally, a survey was conducted to determine the landscape of available mentorship programs within American Association of Colleges of Pharmacy affinity groups to capture unpublished experiences. Information regarding common characteristics and assessment methods was collected for groups that have mentorship programs, while needs and barriers were collected for those who did not. SUMMARY: Literature, while limited, supports positive perceptions of mentorship programs within professional associations. Based on the responses and working group experience, several recommendations are proposed for mentorship program development, including the need for clearly defined goals, relevant program outcomes, association support to reduce redundancies and promote participation, and, in some cases, implementation of an association-wide program to ensure access to mentorship.

Advancing Pharmacy Education by Moving From Sequenced "Integration" to True Curricular Integration.

OBJECTIVES: Traditional pharmacy education focuses on teaching content, which is affectionately known as "silos". Each topic area or discipline includes a course or an individual class session designed to impart knowledge, skills, or abilities needed for the student pharmacist to become a practice-ready, team-ready pharmacist. With expanding content and educational standards, there have been calls to simplify and streamline content. Truly "integrated" curricula (sequenced, coordinated, and collaboratively taught) where silos are removed to foster student integrative learning and build connections across disciplines (foundational, clinical, and social or administrative sciences) could be one such approach. Thus, the objectives of this integrative review are to provide recommendations for decreasing curriculum overload by moving to truly integrated curricula, explore integrated approaches, discuss challenges and barriers, and propose next steps for creating integrated curricula that decrease content overload. FINDINGS: Although there are different approaches to curricular integration, most curricular integration occurs through sequenced courses or integrated cases. In order to truly streamline content and foster connections across disciplines, integration must move beyond simply sequencing of content to content that includes all the disciplines taught seamlessly. When taught together, curricular integration offers the opportunity to cover medication classes quickly and efficiently with multiple opportunities for reinforcement. SUMMARY: There remains limited data and examples of these types of true integration approaches. Thus, it is important for the Academy to determine if the integration of content improves curricular outcomes, positively affects students' learning, and addresses curriculum overload by increasing efficiency and streamlining curricula.

Higher-order epistasis and phenotypic prediction.

Contemporary high-throughput mutagenesis experiments are providing an increasingly detailed view of the complex patterns of genetic interaction that occur between multiple mutations within a single protein or regulatory element. By simultaneously measuring the effects of thousands of combinations of mutations, these experiments have revealed that the genotype-phenotype relationship typically reflects not only genetic interactions between pairs of sites but also higher-order interactions among larger numbers of sites. However, modeling and understanding these higher-order interactions remains challenging. Here we present a method for reconstructing sequence-to-function mappings from partially observed data that can accommodate all orders of genetic interaction. The main idea is to make predictions for unobserved genotypes that match the type and extent of epistasis found in the observed data. This information on the type and extent of epistasis can be extracted by considering how phenotypic correlations change as a function of mutational distance, which is equivalent to estimating the fraction of phenotypic variance due to each order of genetic interaction (additive, pairwise, three-way, etc.). Using these estimated variance components, we then define an empirical Bayes prior that in expectation matches the observed pattern of epistasis and reconstruct the genotype-phenotype mapping by conducting Gaussian process regression under this prior. To demonstrate the power of this approach, we present an application to the antibody-binding domain GB1 and also provide a detailed exploration of a dataset consisting of high-throughput measurements for the splicing efficiency of human pre-mRNA [Formula: see text] splice sites, for which we also validate our model predictions via additional low-throughput experiments.

Structural and mechanistic basis of σ-dependent transcriptional pausing.

In σ-dependent transcriptional pausing, the transcription initiation factor σ, translocating with RNA polymerase (RNAP), makes sequence-specific protein­DNA interactions with a promoter-like sequence element in the transcribed region, inducing pausing. It has been proposed that, in σ-dependent pausing, the RNAP active center can access off-pathway "backtracked" states that are substrates for the transcript-cleavage factors of the Gre family and on-pathway "scrunched" states that mediate pause escape. Here, using site-specific protein­DNA photocrosslinking to define positions of the RNAP trailing and leading edges and of σ relative to DNA at the λPR' promoter, we show directly that σ-dependent pausing in the absence of GreB in vitro predominantly involves a state backtracked by 2­4 bp, and σ-dependent pausing in the presence of GreB in vitro and in vivo predominantly involves a state scrunched by 2­3 bp. Analogous experiments with a library of 47 (∼16,000) transcribed-region sequences show that the state scrunched by 2­3 bp­and only that state­is associated with the consensus sequence, T−3N−2Y−1G+1, (where −1 corresponds to the position of the RNA 3' end), which is identical to the consensus for pausing in initial transcription and which is related to the consensus for pausing in transcription elongation. Experiments with heteroduplex templates show that sequence information at position T−3 resides in the DNA nontemplate strand. A cryoelectron microscopy structure of a complex engaged in σ-dependent pausing reveals positions of DNA scrunching on the DNA nontemplate and template strands and suggests that position T−3 of the consensus sequence exerts its effects by facilitating scrunching.

Revisiting the Socratic method of teaching to improve third-year pharmacy students critical thinking and advanced pharmacy practice experience readiness in a critical care elective.

BACKGROUND: One of the most important abilities we look to nurture and develop in pharmacy students is critical thinking. A critical care elective course was re-designed to optimize high-pressure situations to engage and target critical thinking, clinical reasoning, and advanced pharmacy practice experience (APPE) readiness using the Socratic method of teaching. EDUCATIONAL ACTIVITY: Pharmacy students at Loma Linda University School of Pharmacy were selected and exposed to a high-pressure classroom environment with each day structured around the Socratic method of teaching. Pass/fail grades were earned daily based solely on verbal responses and discussions. The assessment tools used were: Health Sciences and Reasoning Test (HSRT) to measure critical thinking, an APPE-readiness survey of the entire third-year class for peer comparison, and a survey of the style and pedagogies used. CRITICAL ANALYSIS OF THE EDUCATIONAL ACTIVITY: The elective students showed noteworthy gains on their HSRT results in an eight-week critical care elective course. They also had significantly more positive responses on the APPE-readiness survey relative to their classmates and rated this teaching style higher than a typical course. Revisiting and applying the Socratic method of teaching in a high-pressure course is an effective way to engage pharmacy students, producing substantial improvements in critical thinking, clinical reasoning, and APPE readiness in a short time. Lastly, it offers one way to incorporate a validated tool to measure critical thinking in pharmacy students for institutional assessment and accreditation that is widely available, easy to use, and cost friendly.

MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effect.

Multiplex assays of variant effect (MAVEs) are a family of methods that includes deep mutational scanning experiments on proteins and massively parallel reporter assays on gene regulatory sequences. Despite their increasing popularity, a general strategy for inferring quantitative models of genotype-phenotype maps from MAVE data is lacking. Here we introduce MAVE-NN, a neural-network-based Python package that implements a broadly applicable information-theoretic framework for learning genotype-phenotype maps-including biophysically interpretable models-from MAVE datasets. We demonstrate MAVE-NN in multiple biological contexts, and highlight the ability of our approach to deconvolve mutational effects from otherwise confounding experimental nonlinearities and noise.

Multicenter Retrospective Review of Ketamine Use in the ICU.

The response of ICU patients to continuously infused ketamine when it is used for analgesia and/or sedation remains poorly established. OBJECTIVES: To describe continuous infusion (CI) ketamine use in critically ill patients, including indications, dose and duration, adverse effects, patient outcomes, time in goal pain/sedation score range, exposure to analgesics/sedatives, and delirium. DESIGN SETTING AND PARTICIPANTS: Multicenter, retrospective, observational study from twenty-five diverse institutions in the United States. Patients receiving CI ketamine between January 2014 and December 2017. MAIN OUTCOMES AND MEASURES: Chart review evaluating institutional and patient demographics, ketamine indication, dose, administration, and adverse effects. Pain/sedation scores, cumulative doses of sedatives and analgesics, and delirium screenings in the 24 hours prior to ketamine were compared with those at 0-24 hours and 25-48 hours after. RESULTS: A total of 390 patients were included (median age, 54.5 yr; interquartile range, 39-65 yr; 61% males). Primary ICU types were medical (35.3%), surgical (23.3%), and trauma (17.7%). Most common indications were analgesia/sedation (n = 357, 91.5%). Starting doses were 0.2 mg/kg/hr (0.1-0.5 mg/kg/hr) and continued for 1.6 days (0.6-2.9 d). Hemodynamics in the first 4 hours after ketamine were variable (hypertension 24.0%, hypotension 23.5%, tachycardia 19.5%, bradycardia 2.3%); other adverse effects were minimal. Compared with 24 hours prior, there was a significant increase in proportion of time spent within goal pain score after ketamine initiation (24 hr prior: 68.9% [66.7-72.6%], 0-24 hr: 78.6% [74.3-82.5%], 25-48 hr: 80.3% [74.6-84.3%]; p < 0.001) and time spent within goal sedation score (24 hr prior: 57.1% [52.5-60.0%], 0-24 hr: 64.1% [60.7-67.2%], 25-48 hr: 68.9% [65.5-79.5%]; p < 0.001). There was also a significant reduction in IV morphine (mg) equivalents (24 hr prior: 120 [25-400], 0-24 hr: 118 [10-363], 25-48 hr: 80 [5-328]; p < 0.005), midazolam (mg) equivalents (24 hr prior: 11 [4-67], 0-24 hr: 6 [0-68], 25-48 hr: 3 [0-57]; p < 0.001), propofol (mg) (24 hr prior: 942 [223-4,018], 0-24 hr: 160 [0-2,776], 25-48 hr: 0 [0-1,859]; p < 0.001), and dexmedetomidine (µg) (24 hr prior: 1,025 [276-1,925], 0-24 hr: 285 [0-1,283], 25-48 hr: 0 [0-826]; p < 0.001). There was no difference in proportion of time spent positive for delirium (24 hr prior: 43.0% [17.0-47.0%], 0-24 hr: 39.5% [27.0-43.8%], 25-48 hr: 0% [0-43.7%]; p = 0.233). Limitations to these data include lack of a comparator group, potential for confounders and selection bias, and varying pain and sedation practices that may have changed since completion of the study. CONCLUSIONS AND RELEVANCE: There is variability in the use of CI ketamine. Hemodynamic instability was the most common adverse effect. In the 48 hours after ketamine initiation compared with the 24 hours prior, proportion of time spent in goal pain/sedation score range increased and exposure to other analgesics/sedatives decreased.

Diabetic Ketoacidosis Updates: Titratable Insulin Infusions and Long-Acting Insulin Early.

BACKGROUND: To compare a titratable insulin infusion order set (vs. nontitratable) and early administration of long-acting insulin in adult patients with diabetic ketoacidosis (DKA). METHODS: Single health system, retrospective study of adult patients admitted to the intensive care unit (ICU) for DKA. The primary outcomes were insulin infusion duration and ICU/hospital length of stays (LoS). Secondary outcomes included ICU/hospital survival, hypoglycemia, and hypokalemia. RESULTS: 151 patients were included in the titratable versus nontitratable insulin infusion comparison. Patients treated with the titratable insulin had shorter hospitalization (6.4 vs. 10.4 days, p=0.03) and reduced the number hypoglycemic events by over half (20.6% vs. 46.0%, p < 0.01). 110 patients were identified to compare overlapping a long-acting insulin for more than 4 h with the insulin infusion versus the standard 1-2 h overlap. Patients who received the insulin early spent over 18 h longer on the infusion (p < 0.01). CONCLUSIONS: A titratable insulin infusion added to the institutional DKA order set was associated with fewer days in the hospital and a significant reduction in hypoglycemic events. Furthermore, overlapping the long-acting insulin earlier with the insulin infusion early showed no benefit and could potentially be worse than the standard overlap.

Field-theoretic density estimation for biological sequence space with applications to 5' splice site diversity and aneuploidy in cancer.

Density estimation in sequence space is a fundamental problem in machine learning that is also of great importance in computational biology. Due to the discrete nature and large dimensionality of sequence space, how best to estimate such probability distributions from a sample of observed sequences remains unclear. One common strategy for addressing this problem is to estimate the probability distribution using maximum entropy (i.e., calculating point estimates for some set of correlations based on the observed sequences and predicting the probability distribution that is as uniform as possible while still matching these point estimates). Building on recent advances in Bayesian field-theoretic density estimation, we present a generalization of this maximum entropy approach that provides greater expressivity in regions of sequence space where data are plentiful while still maintaining a conservative maximum entropy character in regions of sequence space where data are sparse or absent. In particular, we define a family of priors for probability distributions over sequence space with a single hyperparameter that controls the expected magnitude of higher-order correlations. This family of priors then results in a corresponding one-dimensional family of maximum a posteriori estimates that interpolate smoothly between the maximum entropy estimate and the observed sample frequencies. To demonstrate the power of this method, we use it to explore the high-dimensional geometry of the distribution of 5' splice sites found in the human genome and to understand patterns of chromosomal abnormalities across human cancers.

Promoter-sequence determinants and structural basis of primer-dependent transcription initiation in Escherichia coli.

Chemical modifications of RNA 5'-ends enable "epitranscriptomic" regulation, influencing multiple aspects of RNA fate. In transcription initiation, a large inventory of substrates compete with nucleoside triphosphates for use as initiating entities, providing an ab initio mechanism for altering the RNA 5'-end. In Escherichia coli cells, RNAs with a 5'-end hydroxyl are generated by use of dinucleotide RNAs as primers for transcription initiation, "primer-dependent initiation." Here, we use massively systematic transcript end readout (MASTER) to detect and quantify RNA 5'-ends generated by primer-dependent initiation for ∼410 (∼1,000,000) promoter sequences in E. coli The results show primer-dependent initiation in E. coli involves any of the 16 possible dinucleotide primers and depends on promoter sequences in, upstream, and downstream of the primer binding site. The results yield a consensus sequence for primer-dependent initiation, YTSS-2NTSS-1NTSSWTSS+1, where TSS is the transcription start site, NTSS-1NTSS is the primer binding site, Y is pyrimidine, and W is A or T. Biochemical and structure-determination studies show that the base pair (nontemplate-strand base:template-strand base) immediately upstream of the primer binding site (Y:RTSS-2, where R is purine) exerts its effect through the base on the DNA template strand (RTSS-2) through interchain base stacking with the RNA primer. Results from analysis of a large set of natural, chromosomally encoded Ecoli promoters support the conclusions from MASTER. Our findings provide a mechanistic and structural description of how TSS-region sequence hard-codes not only the TSS position but also the potential for epitranscriptomic regulation through primer-dependent transcription initiation.

Deciphering the regulatory genome of Escherichia coli, one hundred promoters at a time.

Advances in DNA sequencing have revolutionized our ability to read genomes. However, even in the most well-studied of organisms, the bacterium Escherichia coli, for ≈65% of promoters we remain ignorant of their regulation. Until we crack this regulatory Rosetta Stone, efforts to read and write genomes will remain haphazard. We introduce a new method, Reg-Seq, that links massively parallel reporter assays with mass spectrometry to produce a base pair resolution dissection of more than a E. coli promoters in 12 growth conditions. We demonstrate that the method recapitulates known regulatory information. Then, we examine regulatory architectures for more than 80 promoters which previously had no known regulatory information. In many cases, we also identify which transcription factors mediate their regulation. This method clears a path for highly multiplexed investigations of the regulatory genome of model organisms, with the potential of moving to an array of microbes of ecological and medical relevance.

Logomaker: beautiful sequence logos in Python.

SUMMARY: Sequence logos are visually compelling ways of illustrating the biological properties of DNA, RNA and protein sequences, yet it is currently difficult to generate and customize such logos within the Python programming environment. Here we introduce Logomaker, a Python API for creating publication-quality sequence logos. Logomaker can produce both standard and highly customized logos from either a matrix-like array of numbers or a multiple-sequence alignment. Logos are rendered as native matplotlib objects that are easy to stylize and incorporate into multi-panel figures. AVAILABILITY AND IMPLEMENTATION: Logomaker can be installed using the pip package manager and is compatible with both Python 2.7 and Python 3.6. Documentation is provided at http://logomaker.readthedocs.io; source code is available at http://github.com/jbkinney/logomaker.

Massively Parallel Assays and Quantitative Sequence-Function Relationships.

Over the last decade, a rich variety of massively parallel assays have revolutionized our understanding of how biological sequences encode quantitative molecular phenotypes. These assays include deep mutational scanning, high-throughput SELEX, and massively parallel reporter assays. Here, we review these experimental methods and how the data they produce can be used to quantitatively model sequence-function relationships. In doing so, we touch on a diverse range of topics, including the identification of clinically relevant genomic variants, the modeling of transcription factor binding to DNA, the functional and evolutionary landscapes of proteins, and cis-regulatory mechanisms in both transcription and mRNA splicing. We further describe a unified conceptual framework and a core set of mathematical modeling strategies that studies in these diverse areas can make use of. Finally, we highlight key aspects of experimental design and mathematical modeling that are important for the results of such studies to be interpretable and reproducible.

Mapping DNA sequence to transcription factor binding energy in vivo.

Despite the central importance of transcriptional regulation in biology, it has proven difficult to determine the regulatory mechanisms of individual genes, let alone entire gene networks. It is particularly difficult to decipher the biophysical mechanisms of transcriptional regulation in living cells and determine the energetic properties of binding sites for transcription factors and RNA polymerase. In this work, we present a strategy for dissecting transcriptional regulatory sequences using in vivo methods (massively parallel reporter assays) to formulate quantitative models that map a transcription factor binding site's DNA sequence to transcription factor-DNA binding energy. We use these models to predict the binding energies of transcription factor binding sites to within 1 kBT of their measured values. We further explore how such a sequence-energy mapping relates to the mechanisms of trancriptional regulation in various promoter contexts. Specifically, we show that our models can be used to design specific induction responses, analyze the effects of amino acid mutations on DNA sequence preference, and determine how regulatory context affects a transcription factor's sequence specificity.

Epistasis in a Fitness Landscape Defined by Antibody-Antigen Binding Free Energy.

Epistasis is the phenomenon by which the effect of a mutation depends on its genetic background. While it is usually defined in terms of organismal fitness, for single proteins, it must reflect physical interactions among residues. Here, we systematically extract the specific contribution pairwise epistasis makes to the physical affinity of antibody-antigen binding relevant to affinity maturation, a process of accelerated Darwinian evolution. We find that, among competing definitions of affinity, the binding free energy is the most appropriate to describe epistasis. We show that epistasis is pervasive, accounting for 25%-35% of variability, of which a large fraction is beneficial. This work suggests that epistasis both constrains, through negative epistasis, and enlarges, through positive epistasis, the set of possible evolutionary paths that can produce high-affinity sequences during repeated rounds of mutation and selection.

Measuring cis-regulatory energetics in living cells using allelic manifolds.

Gene expression in all organisms is controlled by cooperative interactions between DNA-bound transcription factors (TFs), but quantitatively measuring TF-DNA and TF-TF interactions remains difficult. Here we introduce a strategy for precisely measuring the Gibbs free energy of such interactions in living cells. This strategy centers on the measurement and modeling of 'allelic manifolds', a multidimensional generalization of the classical genetics concept of allelic series. Allelic manifolds are measured using reporter assays performed on strategically designed cis-regulatory sequences. Quantitative biophysical models are then fit to the resulting data. We used this strategy to study regulation by two Escherichia coli TFs, CRP and [Formula: see text] RNA polymerase. Doing so, we consistently obtained energetic measurements precise to [Formula: see text] kcal/mol. We also obtained multiple results that deviate from the prior literature. Our strategy is compatible with massively parallel reporter assays in both prokaryotes and eukaryotes, and should therefore be highly scalable and broadly applicable. Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that minor issues remain unresolved (see decision letter).