Impact of between-tissue differences on pan-cancer predictions of drug sensitivity.

Increased availability of drug response and genomics data for many tumor cell lines has accelerated the development of pan-cancer prediction models of drug response. However, it is unclear how much between-tissue differences in drug response and molecular characteristics may contribute to pan-cancer predictions. Also unknown is whether the performance of pan-cancer models could vary by cancer type. Here, we built a series of pan-cancer models using two datasets containing 346 and 504 cell lines, each with MEK inhibitor (MEKi) response and mRNA expression, point mutation, and copy number variation data, and found that, while the tissue-level drug responses are accurately predicted (between-tissue ρ = 0.88-0.98), only 5 of 10 cancer types showed successful within-tissue prediction performance (within-tissue ρ = 0.11-0.64). Between-tissue differences make substantial contributions to the performance of pan-cancer MEKi response predictions, as exclusion of between-tissue signals leads to a decrease in Spearman's ρ from a range of 0.43-0.62 to 0.30-0.51. In practice, joint analysis of multiple cancer types usually has a larger sample size, hence greater power, than for one cancer type; and we observe that higher accuracy of pan-cancer prediction of MEKi response is almost entirely due to the sample size advantage. Success of pan-cancer prediction reveals how drug response in different cancers may invoke shared regulatory mechanisms despite tissue-specific routes of oncogenesis, yet predictions in different cancer types require flexible incorporation of between-cancer and within-cancer signals. As most datasets in genome sciences contain multiple levels of heterogeneity, careful parsing of group characteristics and within-group, individual variation is essential when making robust inference.

Robust predictions of specialized metabolism genes through machine learning.

Plant specialized metabolism (SM) enzymes produce lineage-specific metabolites with important ecological, evolutionary, and biotechnological implications. Using Arabidopsis thaliana as a model, we identified distinguishing characteristics of SM and GM (general metabolism, traditionally referred to as primary metabolism) genes through a detailed study of features including duplication pattern, sequence conservation, transcription, protein domain content, and gene network properties. Analysis of multiple sets of benchmark genes revealed that SM genes tend to be tandemly duplicated, coexpressed with their paralogs, narrowly expressed at lower levels, less conserved, and less well connected in gene networks relative to GM genes. Although the values of each of these features significantly differed between SM and GM genes, any single feature was ineffective at predicting SM from GM genes. Using machine learning methods to integrate all features, a prediction model was established with a true positive rate of 87% and a true negative rate of 71%. In addition, 86% of known SM genes not used to create the machine learning model were predicted. We also demonstrated that the model could be further improved when we distinguished between SM, GM, and junction genes responsible for reactions shared by SM and GM pathways, indicating that topological considerations may further improve the SM prediction model. Application of the prediction model led to the identification of 1,220 A. thaliana genes with previously unknown functions, each assigned a confidence measure called an SM score, providing a global estimate of SM gene content in a plant genome.

A simple and cost-effective protocol for high-yield expression of deuterated and selectively isoleucine/leucine/valine methyl protonated proteins in Escherichia coli grown in shaker flasks.

A simple and cost-effective protocol is presented for expression of perdeuterated, Ile/Leu/Val 1H/13C methyl protonated proteins from 100 ml cultures in M9 ++ /D2O medium induced at high (OD600 ~ 10) cell density in shaker flasks. This protocol, which is an extension of our previous protocols for expression of 2H/15N/13C and 1H/13C labeled proteins, yields comparable quantities of protein from 100 ml cell culture to those obtained using a conventional 1 L culture with M9/D2O medium, while using three-fold less α-ketoisovaleric (1,2,3,4-13C4; 3,4',4',4'-d4) and α-ketobutyric (13C4; 3,3-d2) acid precursors.

Symptomology following mRNA vaccination against SARS-CoV-2.

Despite demonstrated efficacy of vaccines against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), the causative agent of coronavirus disease-2019 (COVID-19), widespread hesitancy to vaccination persists. Improved knowledge regarding frequency, severity, and duration of vaccine-associated symptoms may help reduce hesitancy. In this prospective observational study, we studied 1032 healthcare workers who received both doses of the Pfizer-BioNTech SARS-CoV-2 mRNA vaccine and completed post-vaccine symptom surveys both after dose 1 and after dose 2. We defined appreciable post-vaccine symptoms as those of at least moderate severity and lasting at least 2 days. We found that symptoms were more frequent following the second vaccine dose than the first (74% vs. 60%, P < 0.001), with >80% of all symptoms resolving within 2 days. The most common symptom was injection site pain, followed by fatigue and malaise. Overall, 20% of participants experienced appreciable symptoms after dose 1 and 30% after dose 2. In multivariable analyses, female sex was associated with greater odds of appreciable symptoms after both dose 1 (OR, 95% CI 1.73, 1.19-2.51) and dose 2 (1.76, 1.28-2.42). Prior COVID-19 was also associated with appreciable symptoms following dose 1, while younger age and history of hypertension were associated with appreciable symptoms after dose 2. We conclude that most post-vaccine symptoms are reportedly mild and last <2 days. Appreciable post-vaccine symptoms are associated with female sex, prior COVID-19, younger age, and hypertension. This information can aid clinicians in advising patients on the safety and expected symptomatology associated with vaccination.

Preoperative measurements for cataract surgery: a comparison of ultrasound and optical biometric devices.

PURPOSE: To evaluate differences in preoperative measurements and refractive outcomes between ultrasound and optical biometry when using the Barrett Universal II intraocular lens (IOL) power formula. METHODS: In this consecutive case series, cataract extraction and IOL implantation cases from two surgical centers in Toronto, Canada, were recruited between January 2015 and July 2017. Differences between ultrasound (applanation or immersion A-scan) and optical biometry (IOLMaster 500) were compared for axial length (AL), anterior chamber depth and refractive outcomes. The primary outcome was the percentage of cases in each cohort within ± 0.50D of refractive error. RESULTS: In total, 527 cataract cases underwent IOLMaster testing. Of these, 329 eyes (62.4%) were also measured by applanation A-scan, and the other 198 eyes (37.6%) received immersion A-scan testing. Applanation ultrasound led to 5.8%, 16.0% and 46.4% of eyes within ± 0.25D, ± 0.50D and ± 1.00D of refractive error, respectively, whereas the IOLMaster 500 led to 48.5%, 77.1% and 94.9%, respectively (n = 293, ± 0.50D: p < 0.001). Immersion ultrasound led to 31.2%, 57.6% and 91.2% of eyes within ± 0.25D, ± 0.50D and ± 1.00D of refractive error, respectively, whereas the IOLMaster 500 led to 42.4%, 72.0% and 92.0%, respectively (n = 125, ± 0.50D: p = 0.001). Applanation (n = 329, A-scan AL: 23.64 ± 1.67 mm, IOLMaster AL: 24.20 ± 1.70 mm, p < 0.001) and immersion ultrasound (n = 198, A-scan AL: 25.01 ± 2.06 mm, IOLMaster AL: 25.08 ± 2.13 mm, p = 0.002) yielded significantly lower AL values compared to optical biometry measurements. CONCLUSIONS: Optical biometry yielded a significantly larger percentage of cases within ± 0.50D of refractive error compared to ultrasound biometry when using the Barrett Universal II IOL power formula.

A comparative analysis of 12 intraocular lens power formulas.

PURPOSE: To evaluate the accuracy of 12 intraocular lens (IOL) power calculations: Barrett Universal II, EVO, Haigis, Hill-RBF version 2.0, Hoffer Q, Holladay 1, Holladay 2, Kane, Olsen, SRK/T, Super Formula and T2. METHODS: In this retrospective consecutive case series, cataract extraction and IOL implantation cases in Toronto, Canada, were recruited between 2017 and 2019. Refractive predictions were compared to the observed 1-month postoperative spherical equivalent to determine the refractive error for each formula cohort. Subgroup analysis stratified eyes into short (≤ 22.5 mm)-, intermediate (22.5 mm-25.5 mm)- and long (≥ 25.5 mm)-axial length (AL) cohorts. The primary outcome was the percentage of cases within ± 0.50D of refractive error. RESULTS: Overall, 764 cataract cases were analyzed. Formulas with the highest percentage of eyes within ± 0.50D of refractive error, in decreasing order, were: Kane (77.7%), Barrett Universal II (77.4%), EVO (76.6%), T2 (76.4%), Super (75.9%), Holladay 1 (75.4%), Hill-RBF 2.0 (74.7%), SRK/T (72.6%), Hoffer Q (72.5%), Haigis (71.7%), Olsen (67.4%) and Holladay 2 (67.3%). For short-AL eyes, the Holladay 1 formula was most accurate (n = 69, 78.3% within ± 0.50D), and for long-AL eyes, the Barrett Universal II formula was most accurate (n = 116, 76.7% within ± 0.50D). Kane, Barrett, EVO, T2 and Super formulas led to a significantly lower mean absolute error compared to the open-source calculations with optimized lens constants (p-value: < 0.001-0.042). CONCLUSIONS: The Kane formula was the most accurate formula for the overall analysis. The Holladay 1 calculation was most accurate for short-AL cases, whereas the Barrett Universal II was superior for long-AL eyes.

Improved recovery of cell-cycle gene expression in Saccharomyces cerevisiae from regulatory interactions in multiple omics data.

BACKGROUND: Gene expression is regulated by DNA-binding transcription factors (TFs). Together with their target genes, these factors and their interactions collectively form a gene regulatory network (GRN), which is responsible for producing patterns of transcription, including cyclical processes such as genome replication and cell division. However, identifying how this network regulates the timing of these patterns, including important interactions and regulatory motifs, remains a challenging task. RESULTS: We employed four in vivo and in vitro regulatory data sets to investigate the regulatory basis of expression timing and phase-specific patterns cell-cycle expression in Saccharomyces cerevisiae. Specifically, we considered interactions based on direct binding between TF and target gene, indirect effects of TF deletion on gene expression, and computational inference. We found that the source of regulatory information significantly impacts the accuracy and completeness of recovering known cell-cycle expressed genes. The best approach involved combining TF-target and TF-TF interactions features from multiple datasets in a single model. In addition, TFs important to multiple phases of cell-cycle expression also have the greatest impact on individual phases. Important TFs regulating a cell-cycle phase also tend to form modules in the GRN, including two sub-modules composed entirely of unannotated cell-cycle regulators (STE12-TEC1 and RAP1-HAP1-MSN4). CONCLUSION: Our findings illustrate the importance of integrating both multiple omics data and regulatory motifs in order to understand the significance regulatory interactions involved in timing gene expression. This integrated approached allowed us to recover both known cell-cycles interactions and the overall pattern of phase-specific expression across the cell-cycle better than any single data set. Likewise, by looking at regulatory motifs in the form of TF-TF interactions, we identified sets of TFs whose co-regulation of target genes was important for cell-cycle expression, even when regulation by individual TFs was not. Overall, this demonstrates the power of integrating multiple data sets and models of interaction in order to understand the regulatory basis of established biological processes and their associated gene regulatory networks.

Molecular determinants of drug response in TNBC cell lines.

PURPOSE: There is a need for biomarkers of drug efficacy for targeted therapies in triple-negative breast cancer (TNBC). As a step toward this, we identify multi-omic molecular determinants of anti-TNBC efficacy in cell lines for a panel of oncology drugs. METHODS: Using 23 TNBC cell lines, drug sensitivity scores (DSS3) were determined using a panel of investigational drugs and drugs approved for other indications. Molecular readouts were generated for each cell line using RNA sequencing, RNA targeted panels, DNA sequencing, and functional proteomics. DSS3 values were correlated with molecular readouts using a FDR-corrected significance cutoff of p* < 0.05 and yielded molecular determinant panels that predict anti-TNBC efficacy. RESULTS: Six molecular determinant panels were obtained from 12 drugs we prioritized based on their efficacy. Determinant panels were largely devoid of DNA mutations of the targeted pathway. Molecular determinants were obtained by correlating DSS3 with molecular readouts. We found that co-inhibiting molecular correlate pathways leads to robust synergy across many cell lines. CONCLUSIONS: These findings demonstrate an integrated method to identify biomarkers of drug efficacy in TNBC where DNA predictions correlate poorly with drug response. Our work outlines a framework for the identification of novel molecular determinants and optimal companion drugs for combination therapy based on these correlates.

Patterns of blood mercury variation in two long-distance migratory thrushes on Mount Mansfield, Vermont.

We investigated mercury (Hg) blood concentrations in Bicknell's thrush (Catharus bicknelli) and Swainson's thrush (C. ustulatus), congeneric long-distance migratory songbirds, from 2000-2017 at a montane forest site in north-central Vermont. We analyzed variation in blood Hg of both species using mixed-effects models, incorporating atmospheric wet Hg deposition data from a nearby sampling location. Although Hg deposition varied among years and seasonally, we detected no temporal trend in either atmospheric deposition or blood Hg, nor evidence of a relationship between the two. Sampling date had the strongest effect on blood Hg concentration, which declined seasonally, followed by age and sex of the individual. The data did not support an effect of species. We believe that the absence of a clear relationship between local atmospheric deposition and thrush blood Hg concentrations suggests that Hg cycling dynamics, mechanisms of transfer, and timing of uptake by montane forest biota are complex and poorly understood. The blood Hg concentrations of ~0.07-0.1 µg/g we documented in Bicknell's and Swainson's thrush are below those found to negatively impact physiological or reproductive endpoints in other invertivorous terrestrial passerines. To better evaluate the validity of Bicknell's thrush as a bioindicator of MeHg availability in montane forest ecosystems, we recommend (1) effects-based investigations, (2) a more robust understanding of Hg and MeHg cycling, (3) more clear geospatial and temporal links between Hg deposition and biotic uptake, and (4) more thorough documentation of Hg burdens across the species' annual cycle.

A Model-Based Approach for Identifying Functional Intergenic Transcribed Regions and Noncoding RNAs.

With advances in transcript profiling, the presence of transcriptional activities in intergenic regions has been well established. However, whether intergenic expression reflects transcriptional noise or activity of novel genes remains unclear. We identified intergenic transcribed regions (ITRs) in 15 diverse flowering plant species and found that the amount of intergenic expression correlates with genome size, a pattern that could be expected if intergenic expression is largely nonfunctional. To further assess the functionality of ITRs, we first built machine learning models using Arabidopsis thaliana as a model that accurately distinguish functional sequences (benchmark protein-coding and RNA genes) and likely nonfunctional ones (pseudogenes and unexpressed intergenic regions) by integrating 93 biochemical, evolutionary, and sequence-structure features. Next, by applying the models genome-wide, we found that 4,427 ITRs (38%) and 796 annotated ncRNAs (44%) had features significantly similar to benchmark protein-coding or RNA genes and thus were likely parts of functional genes. Approximately 60% of ITRs and ncRNAs were more similar to nonfunctional sequences and were likely transcriptional noise. The predictive framework established here provides not only a comprehensive look at how functional, genic sequences are distinct from likely nonfunctional ones, but also a new way to differentiate novel genes from genomic regions with noisy transcriptional activities.

Bioaccumulation of methylmercury in wood frogs and spotted salamanders in Vermont vernal pools.

Mercury (Hg) has accumulated in forested landscapes in the Northeastern U.S., and hotspots with enhanced deposition have been identified throughout the region. Due to a variety of favorable landscape characteristics, including relatively high dissolved organic carbon (DOC), fluctuating water levels, and low pH and dissolved oxygen, vernal pools provide ideal conditions for the conversion of Hg to its more toxic and bioavailable form, methylmercury (MeHg). Yet little is known about the concentrations, speciation, and bioavailability of Hg in vernal pools, or its bioaccumulation in vernal pool fauna and potential export into terrestrial systems. We investigated the role of forest cover type on the bioaccumulation of MeHg in wood frog (Lithobates sylvatica) and spotted salamander (Ambystoma maculatum) eggs, larvae, and adults, and investigated relationships among MeHg and water chemistry (pH, DOC). Water samples from pools located in coniferous stands had greater concentrations of THg and MeHg compared to deciduous pool water, and showed significant positive correlation to DOC (r = 0.683, P < 0.001) and correlated negatively with pH (r = -0.613, P < 0.001). Methylmercury levels in amphibian embryos were similar between the two species (L. sylvatica mean = 5.4 ng/g dw; A. maculatum mean = 3.5 ng/g dw). Concentrations of MeHg increased substantially in larvae, and were significantly greater in A. maculatum (mean = 237.6 ng/g ± 18.5 SE) than L. sylvatica larvae (62.5 ng/g ± 5.7 SE). Forest cover type did not explain variation in MeHg concentration among amphibian embryos or larvae. Methylmercury levels in adult tissue samples were significantly greater in A. maculatum (mean = 79.9 ng/g ± 8.9 SE) compared to L. sylvatica (mean = 47.7 ng/g ± 9.7 SE). This research demonstrates that vernal pools are important hotspots where amphibians bioaccumulate MeHg, which may then be transferred to terrestrial ecosystems. The abundance of amphibian larvae suggests they could be important bioindicators for monitoring MeHg loading and bioavailability.

Defining Functional Genic Regions in the Human Genome through Integration of Biochemical, Evolutionary, and Genetic Evidence.

The human genome is dominated by large tracts of DNA with extensive biochemical activity but no known function. In particular, it is well established that transcriptional activities are not restricted to known genes. However, whether this intergenic transcription represents activity with functional significance or noise is under debate, highlighting the need for an effective method of defining functional genomic regions. Moreover, these discoveries raise the question whether genomic regions can be defined as functional based solely on the presence of biochemical activities, without considering evolutionary (conservation) and genetic (effects of mutations) evidence. Here, computational models integrating genetic, evolutionary, and biochemical evidence are established that provide reliable predictions of human protein-coding and RNA genes. Importantly, in addition to sequence conservation, biochemical features allow accurate predictions of genic sequences with phenotypic evidence under strong purifying selection, suggesting that they can be used as an alternative measure of selection. Moreover, 18.5% of annotated noncoding RNAs exhibit higher degrees of similarity to phenotype genes and, thus, are likely functional. However, 64.5% of noncoding RNAs appear to belong to a sequence class of their own, and the remaining 17% are more similar to pseudogenes and random intergenic sequences that may represent noisy transcription.

Performance of a prototype stationary catadioptric concentrating photovoltaic module.

A stationary catadioptric concentrating photovoltaic module with aperture area over 100 cm2, geometric concentration of 180×, and collection within 60° of polar incidence was designed, prototyped, and characterized. The module performance followed modeling closely with a peak power conversion efficiency of 26% for direct irradiance. Tracking of the sun is accomplished via translational micro-tracking completely internal to the module, avoiding the cost and complexity of mechanical two-axis trackers that point towards the sun. This demonstrates the potential for concentrating photovoltaic modules with significantly higher efficiency than industry standard silicon photovoltaic modules that could be installed in stationary configurations on rooftops.

Characteristics of Plant Essential Genes Allow for within- and between-Species Prediction of Lethal Mutant Phenotypes.

Essential genes represent critical cellular components whose disruption results in lethality. Characteristics shared among essential genes have been uncovered in fungal and metazoan model systems. However, features associated with plant essential genes are largely unknown and the full set of essential genes remains to be discovered in any plant species. Here, we show that essential genes in Arabidopsis thaliana have distinct features useful for constructing within- and cross-species prediction models. Essential genes in A. thaliana are often single copy or derived from older duplications, highly and broadly expressed, slow evolving, and highly connected within molecular networks compared with genes with nonlethal mutant phenotypes. These gene features allowed the application of machine learning methods that predicted known lethal genes as well as an additional 1970 likely essential genes without documented phenotypes. Prediction models from A. thaliana could also be applied to predict Oryza sativa and Saccharomyces cerevisiae essential genes. Importantly, successful predictions drew upon many features, while any single feature was not sufficient. Our findings show that essential genes can be distinguished from genes with nonlethal phenotypes using features that are similar across kingdoms and indicate the possibility for translational application of our approach to species without extensive functional genomic and phenomic resources.

Evaluation of fluorophore-tethered platinum complexes to monitor the fate of cisplatin analogs.

The platinum drugs cisplatin, carboplatin, and oxaliplatin are highly utilized in the clinic and as a consequence have been extensively studied in the laboratory setting, sometimes by generating fluorophore-tagged analogs. Here, we synthesized two Pt(II) complexes containing ethane-1,2-diamine ligands linked to a BODIPY fluorophore, and compared their biological activity with previously reported Pt(II) complexes conjugated to carboxyfluorescein and carboxyfluorescein diacetate. The cytotoxicity and DNA damage capacity of Pt-fluorophore complexes was compared to cisplatin, and the Pt-BODIPY complexes were found to be more cytotoxic with reduced cytotoxicity in cisplatin-resistant cells. Microscopy revealed a predominately cytosolic localization, with nuclear distribution at higher concentrations. Spheroids grown from parent and resistant cells revealed penetration of Pt-BODIPY into spheroids, and retention of the cisplatin-resistant spheroid phenotype. While most activity profiles were retained for the Pt-BODIPY complexes, accumulation in resistant cells was only slightly affected, suggesting that some aspects of Pt-fluorophore cellular pharmacology deviate from cisplatin.

Pseudosaccharin amines as potent and selective KV1.5 blockers.

Phenethyl aminoheterocycles like compound 1 were known to be potent I(Kur) blockers although they lacked potency in vivo. Modification of the heterocycle led to the design and synthesis of pseudosaccharin amines. Compounds such as 14, 17d and 21c were found to be potent K(V)1.5 blockers and selective over other cardiac ion channels. These compounds had potent pharmacodynamic activity, however, they also showed off-target activities such as hemodynamic effects.

Treatment integrity in school-wide programs: a review of the literature (1993-2012).

We assessed the reporting of treatment integrity in school-wide prevention programs in K-12 schools. This review was designed to determine (a) the extent to which treatment integrity was reported in school-wide prevention and intervention programs and how the reporting varied by research design, year, and journal; and (b) the procedures (e.g., method, frequency, informant) used to collect treatment integrity data. Results indicated that fewer than half of the studies in the review (n = 36, 45.6 %) measured and reported treatment integrity. Those studies reporting treatment integrity often used multiple methods and informants. Reporting treatment integrity in this body of literature has increased steadily over time.

Beneficial metabolic effects of a probiotic via butyrate-induced GLP-1 hormone secretion.

Obesity and diabetes are associated with excess caloric intake and reduced energy expenditure resulting in a negative energy balance. The incidence of diabetes has reached epidemic proportions, and childhood diabetes and obesity are increasing alarmingly. Therefore, it is important to develop safe, easily deliverable, and economically viable treatment alternatives for these diseases. Here, we provide data supporting the candidacy of probiotics as such a therapeutic modality against obesity and diabetes. Probiotics are live bacteria that colonize the gastrointestinal tract and impart beneficial effects for health. However, their widespread prescription as medical therapies is limited primarily because of the paucity of our understanding of their mechanism of action. Here, we demonstrate that the administration of a probiotic, VSL#3, prevented and treated obesity and diabetes in several mouse models. VSL#3 suppressed body weight gain and insulin resistance via modulation of the gut flora composition. VSL#3 promoted the release of the hormone GLP-1, resulting in reduced food intake and improved glucose tolerance. The VSL#3-induced changes were associated with an increase in the levels of a short chain fatty acid (SCFA), butyrate. Using a cell culture system, we demonstrate that butyrate stimulated the release of GLP-1 from intestinal L-cells, thereby providing a plausible mechanism for VSL#3 action. These findings suggest that probiotics such as VSL#3 can modulate the gut microbiota-SCFA-hormone axis. Moreover, our results indicate that probiotics are of potential therapeutic utility to counter obesity and diabetes.

Design, synthesis and evaluation of phenethylaminoheterocycles as K(v)1.5 inhibitors.

Phenethylaminoheterocycles have been prepared and assayed for inhibition of the Kv1.5 potassium ion channel as a potential approach to the treatment of atrial fibrillation. A diverse set of heterocycles were identified as potent Kv1.5 inhibitors and were advanced to pharmacodynamic evaluation based on selectivity and pharmacokinetic profile. Heterocycle optimization and template modification lead to the identification of compound 24 which demonstrated increased atrial effective refractory period in the rabbit pharmacodynamic model with mild effects on blood pressure and heart rate.