The Genotype and Phenotypes in Families (GPF) platform manages the large and complex data at SFARI.

The exploration of genotypic variants impacting phenotypes is a cornerstone in genetics research. The emergence of vast collections containing deeply genotyped and phenotyped families has made it possible to pursue the search for variants associated with complex diseases. However, managing these large-scale datasets requires specialized computational tools tailored to organize and analyze the extensive data. GPF (Genotypes and Phenotypes in Families) is an open-source platform ( https://github.com/iossifovlab/gpf ) that manages genotypes and phenotypes derived from collections of families. The GPF interface allows interactive exploration of genetic variants, enrichment analysis for de novo mutations, and phenotype/genotype association tools. In addition, GPF allows researchers to share their data securely with the broader scientific community. GPF is used to disseminate two large-scale family collection datasets (SSC, SPARK) for the study of autism funded by the SFARI foundation. However, GPF is versatile and can manage genotypic data from other small or large family collections. Our GPF-SFARI GPF instance ( https://gpf.sfari.org/ ) provides protected access to comprehensive genotypic and phenotypic data for the SSC and SPARK. In addition, GPF-SFARI provides public access to an extensive collection of de novo mutations identified in individuals with autism and related disorders and to gene-level statistics of the protected datasets characterizing the genes' roles in autism. Here, we highlight the primary features of GPF within the context of GPF-SFARI.

BET inhibition induces vulnerability to MCL1 targeting through upregulation of fatty acid synthesis pathway in breast cancer.

Therapeutic options for treatment of basal-like breast cancers remain limited. Here, we demonstrate that bromodomain and extra-terminal (BET) inhibition induces an adaptive response leading to MCL1 protein-driven evasion of apoptosis in breast cancer cells. Consequently, co-targeting MCL1 and BET is highly synergistic in breast cancer models. The mechanism of adaptive response to BET inhibition involves the upregulation of lipid synthesis enzymes including the rate-limiting stearoyl-coenzyme A (CoA) desaturase. Changes in lipid synthesis pathway are associated with increases in cell motility and membrane fluidity as well as re-localization and activation of HER2/EGFR. In turn, the HER2/EGFR signaling results in the accumulation of and vulnerability to the inhibition of MCL1. Drug response and genomics analyses reveal that MCL1 copy-number alterations are associated with effective BET and MCL1 co-targeting. The high frequency of MCL1 chromosomal amplifications (>30%) in basal-like breast cancers suggests that BET and MCL1 co-targeting may have therapeutic utility in this aggressive subtype of breast cancer.

A novel, multitargeted endogenous metabolic modulator composition impacts metabolism, inflammation, and fibrosis in nonalcoholic steatohepatitis-relevant primary human cell models.

Nonalcoholic steatohepatitis (NASH) is a complex metabolic disease of heterogeneous and multifactorial pathogenesis that may benefit from coordinated multitargeted interventions. Endogenous metabolic modulators (EMMs) encompass a broad set of molecular families, including amino acids and related metabolites and precursors. EMMs often serve as master regulators and signaling agents for metabolic pathways throughout the body and hold the potential to impact a complex metabolic disease like NASH by targeting a multitude of pathologically relevant biologies. Here, we describe a study of a novel EMM composition comprising five amino acids and an amino acid derivative (Leucine, Isoleucine, Valine, Arginine, Glutamine, and N-acetylcysteine [LIVRQNac]) and its systematic evaluation across multiple NASH-relevant primary human cell model systems, including hepatocytes, macrophages, and stellate cells. In these model systems, LIVRQNac consistently and simultaneously impacted biology associated with all three core pathophysiological features of NASH-metabolic, inflammatory, and fibrotic. Importantly, it was observed that while the individual constituent amino acids in LIVRQNac can impact specific NASH-related phenotypes in select cell systems, the complete combination was necessary to impact the range of disease-associated drivers examined. These findings highlight the potential of specific and potent multitargeted amino acid combinations for the treatment of NASH.

Author Correction: Antibiotic susceptibility signatures identify potential antimicrobial targets in the Acinetobacter baumannii cell envelope.

A Correction to this paper has been published: https://doi.org/10.1038/s41467-020-20098-z.

Silencing of survivin and cyclin B1 through siRNA-loaded arginine modified calcium phosphate nanoparticles for non-small-cell lung cancer therapy.

With the development of nanotechnology, various drug delivery systems including inorganic nanoparticles, liposomes, polymers, etc. have been developed over the past decade. Some of these nanoparticles are also forthcoming candidates for the successful delivery of small interfering RNA (siRNA) for targeted gene silencing. Upon its discovery, siRNA was perceived as a highly promising agent in the treatment of various diseases. However, it could not exhibit the expected clinical outcomes owing to the unfavorable challenges during delivery. One such challenge was identified as the lack of an effective carrier. Among the carriers, calcium phosphate (CaP) nanoparticles have attracted remarkable attention due to the superior biochemical properties and hold great promise for siRNA. It is well known that synthesis conditions influence the types of crystalline phases of CaPs as well as morphology. In this study, to address the influence of these parameters on the success of siRNA delivery, three different arginine (Arg) modified CaP nanoparticles having different chemical and morphological characteristics were synthesized as being the carriers of two specific siRNAs against survivin and cyclin B1. The functioning of CaP surfaces with Arg results in positive zeta potential on the surfaces. Functionalized nanoparticles have a higher loading capacity compared to unmodified particles, as they have a cationic surface that can be easily attached to negatively charged siRNAs. The gene silencing ability and the consequent in vitro antitumor activity of these CaP-Arg-siRNA complexes were investigated using A549 non-small-cell lung cancer cells. We found that high survivin and cyclin B1 expression is associated with worse survival in patients with lung cancer based on the Kaplan-Meier database. Considering the promoting role of survivin and cyclin B1 in cancer development and progression, CaP-Arg-siRNA mediated suppression of these genes resulted in a significant decrease in cell growth and induction of apoptosis. Our data suggest that all three CaP-Arg nanoparticles synthesized in this work can be used as safe and efficient nanocarriers for siRNA delivery, offering the opportunity to develop new therapeutic strategies for the treatment of lung cancer.

Antibiotic susceptibility signatures identify potential antimicrobial targets in the Acinetobacter baumannii cell envelope.

A unique, protective cell envelope contributes to the broad drug resistance of the nosocomial pathogen Acinetobacter baumannii. Here we use transposon insertion sequencing to identify A. baumannii mutants displaying altered susceptibility to a panel of diverse antibiotics. By examining mutants with antibiotic susceptibility profiles that parallel mutations in characterized genes, we infer the function of multiple uncharacterized envelope proteins, some of which have roles in cell division or cell elongation. Remarkably, mutations affecting a predicted cell wall hydrolase lead to alterations in lipooligosaccharide synthesis. In addition, the analysis of altered susceptibility signatures and antibiotic-induced morphology patterns allows us to predict drug synergies; for example, certain beta-lactams appear to work cooperatively due to their preferential targeting of specific cell wall assembly machineries. Our results indicate that the pathogen may be effectively inhibited by the combined targeting of multiple pathways critical for envelope growth.

Guided screen for synergistic three-drug combinations.

Combinations of three or more drugs are routinely used in various medical fields such as clinical oncology and infectious diseases to prevent resistance or to achieve synergistic therapeutic benefits. The very large number of possible high-order drug combinations presents a formidable challenge for discovering synergistic drug combinations. Here, we establish a guided screen to discover synergistic three-drug combinations. Using traditional checkerboard and recently developed diagonal methods, we experimentally measured all pairwise interactions among eight compounds in Erwinia amylovora, the causative agent of fire blight. Showing that synergy measurements of these two methods agree, we predicted synergy/antagonism scores for all possible three-drug combinations by averaging the synergy scores of pairwise interactions. We validated these predictions by experimentally measuring 35 three-drug interactions. Therefore, our guided screen for discovering three-drug synergies is (i) experimental screen of all pairwise interactions using diagonal method, (ii) averaging pairwise scores among components to predict three-drug interaction scores, (iii) experimental testing of top predictions. In our study, this strategy resulted in a five-fold reduction in screen size to find the most synergistic three-drug combinations.

Design of high-order antibiotic combinations against M. tuberculosis by ranking and exclusion.

Combinations of more than two drugs are routinely used for the treatment of pathogens and tumors. High-order combinations may be chosen due to their non-overlapping resistance mechanisms or for favorable drug interactions. Synergistic/antagonistic interactions occur when the combination has a higher/lower effect than the sum of individual drug effects. The standard treatment of Mycobacterium tuberculosis (Mtb) is an additive cocktail of three drugs which have different targets. Herein, we experimentally measured all 190 pairwise interactions among 20 antibiotics against Mtb growth. We used the pairwise interaction data to rank all possible high-order combinations by strength of synergy/antagonism. We used drug interaction profile correlation as a proxy for drug similarity to establish exclusion criteria for ideal combination therapies. Using this ranking and exclusion design (R/ED) framework, we modeled ways to improve the standard 3-drug combination with the addition of new drugs. We applied this framework to find the best 4-drug combinations against drug-resistant Mtb by adding new exclusion criteria to R/ED. Finally, we modeled alternating 2-order combinations as a cycling treatment and found optimized regimens significantly reduced the overall effective dose. R/ED provides an adaptable framework for the design of high-order drug combinations against any pathogen or tumor.

Characterizing ABC-Transporter Substrate-Likeness Using a Clean-Slate Genetic Background.

Mutations in ATP Binding Cassette (ABC)-transporter genes can have major effects on the bioavailability and toxicity of the drugs that are ABC-transporter substrates. Consequently, methods to predict if a drug is an ABC-transporter substrate are useful for drug development. Such methods traditionally relied on literature curated collections of ABC-transporter dependent membrane transfer assays. Here, we used a single large-scale dataset of 376 drugs with relative efficacy on an engineered yeast strain with all ABC-transporter genes deleted (ABC-16), to explore the relationship between a drug's chemical structure and ABC-transporter substrate-likeness. We represented a drug's chemical structure by an array of substructure keys and explored several machine learning methods to predict the drug's efficacy in an ABC-16 yeast strain. Gradient-Boosted Random Forest models outperformed all other methods with an AUC of 0.723. We prospectively validated the model using new experimental data and found significant agreement with predictions. Our analysis expands the previously reported chemical substructures associated with ABC-transporter substrates and provides an alternative means to investigate ABC-transporter substrate-likeness.

Miniaturized Checkerboard Assays to Measure Antibiotic Interactions.

Drugs may have synergistic or antagonistic interactions when combined. Checkerboard assays, where two drugs are combined in many doses, allow sensitive measurement of drug interactions. Here, we describe a protocol to measure the pairwise interactions among three antibiotics, in duplicate, in 5 days, using only two 96-well microplates and standard laboratory equipment.

Prediction of ultra-high-order antibiotic combinations based on pairwise interactions.

Drug combinations are a promising approach to achieve high efficacy at low doses and to overcome resistance. Drug combinations are especially useful when drugs cannot achieve effectiveness at tolerable doses, as occurs in cancer and tuberculosis (TB). However, discovery of effective drug combinations faces the challenge of combinatorial explosion, in which the number of possible combinations increases exponentially with the number of drugs and doses. A recent advance, called the dose model, uses a mathematical formula to overcome combinatorial explosion by reducing the problem to a feasible quadratic one: using data on drug pairs at a few doses, the dose model accurately predicts the effect of combinations of three and four drugs at all doses. The dose model has not yet been tested on higher-order combinations beyond four drugs. To address this, we measured the effect of combinations of up to ten antibiotics on E. coli growth, and of up to five tuberculosis (TB) drugs on the growth of M. tuberculosis. We find that the dose model accurately predicts the effect of these higher-order combinations, including cases of strong synergy and antagonism. This study supports the view that the interactions between drug pairs carries key information that largely determines higher-order interactions. Therefore, systematic study of pairwise drug interactions is a compelling strategy to prioritize drug regimens in high-dimensional spaces.

Modeling the impact of drug interactions on therapeutic selectivity.

Combination therapies that produce synergistic growth inhibition are widely sought after for the pharmacotherapy of many pathological conditions. Therapeutic selectivity, however, depends on the difference between potency on disease-causing cells and potency on non-target cell types that cause toxic side effects. Here, we examine a model system of antimicrobial compound combinations applied to two highly diverged yeast species. We find that even though the drug interactions correlate between the two species, cell-type-specific differences in drug interactions are common and can dramatically alter the selectivity of compounds when applied in combination vs. single-drug activity-enhancing, diminishing, or inverting therapeutic windows. This study identifies drug combinations with enhanced cell-type-selectivity with a range of interaction types, which we experimentally validate using multiplexed drug-interaction assays for heterogeneous cell cultures. This analysis presents a model framework for evaluating drug combinations with increased efficacy and selectivity against pathogens or tumors.

Diagonal Method to Measure Synergy Among Any Number of Drugs.

A synergistic drug combination has a higher efficacy compared to the effects of individual drugs. Checkerboard assays, where drugs are combined in many doses, allow sensitive measurement of drug interactions. However, these assays are costly and do not scale well for measuring interaction among many drugs. Several recent studies have reported drug interaction measurements using a diagonal sampling of the traditional checkerboard assay. This alternative methodology greatly decreases the cost of drug interaction experiments and allows interaction measurement for combinations with many drugs. Here, we describe a protocol to measure the three pairwise interactions and one three-way interaction among three antibiotics in duplicate, in five days, using only three 96-well microplates and standard laboratory equipment. We present representative results showing that the three-antibiotic combination of Levofloxacin + Nalidixic Acid + Penicillin G is synergistic. Our protocol scales up to measure interactions among many drugs and in other biological contexts, allowing for efficient screens for multi-drug synergies against pathogens and tumors.

Chemogenomic model identifies synergistic drug combinations robust to the pathogen microenvironment.

Antibiotics need to be effective in diverse environments in vivo. However, the pathogen microenvironment can have a significant impact on antibiotic potency. Further, antibiotics are increasingly used in combinations to combat resistance, yet, the effect of microenvironments on drug-combination efficacy is unknown. To exhaustively explore the impact of diverse microenvironments on drug-combinations, here we develop a computational framework-Metabolism And GENomics-based Tailoring of Antibiotic regimens (MAGENTA). MAGENTA uses chemogenomic profiles of individual drugs and metabolic perturbations to predict synergistic or antagonistic drug-interactions in different microenvironments. We uncovered antibiotic combinations with robust synergy across nine distinct environments against both E. coli and A. baumannii by searching through 2556 drug-combinations of 72 drugs. MAGENTA also accurately predicted the change in efficacy of bacteriostatic and bactericidal drug-combinations during growth in glycerol media, which we confirmed experimentally in both microbes. Our approach identified genes in glycolysis and glyoxylate pathway as top predictors of synergy and antagonism respectively. Our systems approach enables tailoring of antibiotic therapies based on the pathogen microenvironment.

A Parallel Adder Coordinates Mycobacterial Cell-Cycle Progression and Cell-Size Homeostasis in the Context of Asymmetric Growth and Organization.

In model bacteria, such as E. coli and B. subtilis, regulation of cell-cycle progression and cellular organization achieves consistency in cell size, replication dynamics, and chromosome positioning [1-3]. Mycobacteria elongate and divide asymmetrically, giving rise to significant variation in cell size and elongation rate among closely related cells [4, 5]. Given the physical asymmetry of mycobacteria, the models that describe coordination of cellular organization and cell-cycle progression in model bacteria are not directly translatable [1, 2, 6-8]. Here, we used time-lapse microscopy and fluorescent reporters of DNA replication and chromosome positioning to examine the coordination of growth, division, and chromosome dynamics at a single-cell level in Mycobacterium smegmatis (M. smegmatis) and Mycobacterium bovis Bacillus Calmette-Guérin (BCG). By analyzing chromosome and replisome localization, we demonstrated that chromosome positioning is asymmetric and proportional to cell size. Furthermore, we found that cellular asymmetry is maintained throughout the cell cycle and is not established at division. Using measurements and stochastic modeling of mycobacterial cell size and cell-cycle timing in both slow and fast growth conditions, we found that well-studied models of cell-size control are insufficient to explain the mycobacterial cell cycle. Instead, we showed that mycobacterial cell-cycle progression is regulated by an unprecedented mechanism involving parallel adders (i.e., constant growth increments) that start at replication initiation. Together, these adders enable mycobacterial populations to regulate cell size, growth, and heterogeneity in the face of varying environments.

Efficient measurement and factorization of high-order drug interactions in Mycobacterium tuberculosis.

Combinations of three or more drugs are used to treat many diseases, including tuberculosis. Thus, it is important to understand how synergistic or antagonistic drug interactions affect the efficacy of combination therapies. However, our understanding of high-order drug interactions is limited because of the lack of both efficient measurement methods and theoretical framework for analysis and interpretation. We developed an efficient experimental sampling and scoring method [diagonal measurement of n-way drug interactions (DiaMOND)] to measure drug interactions for combinations of any number of drugs. DiaMOND provides an efficient alternative to checkerboard assays, which are commonly used to measure drug interactions. We established a geometric framework to factorize high-order drug interactions into lower-order components, thereby establishing a road map of how to use lower-order measurements to predict high-order interactions. Our framework is a generalized Loewe additivity model for high-order drug interactions. Using DiaMOND, we identified and analyzed synergistic and antagonistic antibiotic combinations against Mycobacteriumtuberculosis. Efficient measurement and factorization of high-order drug interactions by DiaMOND are broadly applicable to other cell types and disease models.

Synthesis and characterization of amino acid-functionalized calcium phosphate nanoparticles for siRNA delivery.

Small interfering RNAs (siRNA) are short nucleic acid fragments of about 20-27 nucleotides, which can inhibit the expression of specific genes. siRNA based RNAi technology has emerged as a promising method for the treatment of a variety of diseases. However, a major limitation in the therapeutic use of siRNA is its rapid degradation in plasma and cellular cytoplasm, resulting in short half-life. In addition, as siRNA molecules cannot penetrate into the cell efficiently, it is required to use a carrier system for its delivery. In this work, chemically and morphologically different calcium phosphate (CaP) nanoparticles, including spherical-like hydroxyapatite (HA-s), needle-like hydroxyapatite (HA-n) and calcium deficient hydroxyapatite (CDHA) nanoparticles were synthesized by the sol-gel technique and the effects of particle characteristics on the binding capacity of siRNA were investigated. In order to enhance the gene loading efficiency, the nanoparticles were functionalized with arginine and the morphological and their structural characteristics were analyzed. The addition of arginine did not significantly change the particle sizes; however, it provided a significantly increased binding of siRNA for all types of CaP nanoparticles, as revealed by spectrophotometric measurements analysis. Arginine functionalized HA-n nanoparticles showed the best binding behavior with siRNA among the other nanoparticles due to its high, positive zeta potential (+18.8mV) and high surface area of Ca++ rich "c" plane. MTT cytotoxicity assays demonstrated that all the nanoparticles tested herein were biocompatible. Our results suggest that high siRNA entrapment in each of the three modified non-toxic CaP nanoparticles make them promising candidates as a non-viral vector for delivering therapeutic siRNA molecules to treat cancer.

Prediction of Antibiotic Interactions Using Descriptors Derived from Molecular Structure.

Combination antibiotic therapies are clinically important in the fight against bacterial infections. However, the search space of drug combinations is large, making the identification of effective combinations a challenging task. Here, we present a computational framework that uses substructure profiles derived from the molecular structures of drugs and predicts antibiotic interactions. Using a previously published data set of 153 drug pairs, we showed that substructure profiles are useful in predicting synergy. We experimentally measured the interaction of 123 new drug pairs, as a prospective validation set for our approach, and identified 37 new synergistic pairs. Of the 12 pairs predicted to be synergistic, 10 were experimentally validated, corresponding to a 2.8-fold enrichment. Having thus validated our methodology, we produced a compendium of interaction predictions for all pairwise combinations among 100 antibiotics. Our methodology can make reliable antibiotic interaction predictions for any antibiotic pair within the applicability domain of the model since it solely requires chemical structures as an input.

Synergistic activity of vorinostat combined with gefitinib but not with sorafenib in mutant KRAS human non-small cell lung cancers and hepatocarcinoma.

Development of drug resistance limits the efficacy of targeted therapies. Alternative approaches using different combinations of therapeutic agents to inhibit several pathways could be a more effective strategy for treating cancer. The effects of the approved epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (gefitinib) or a multi-targeted kinase inhibitor (sorafenib) in combination with a histone deacetylase inhibitor (vorinostat) on cell proliferation, cell cycle distribution, apoptosis, and signaling pathway activation in human lung adenocarcinoma and hepatocarcinoma cells with wild-type EGFR and mutant KRAS were investigated. The effects of the synergistic drug combinations were also studied in human lung adenocarcinoma and hepatocarcinoma cells in vivo. The combination of gefitinib and vorinostat synergistically reduced cell growth and strongly induced apoptosis through inhibition of the insulin-like growth factor-1 receptor/protein kinase B (IGF-1R/AKT)-dependent signaling pathway. Moreover, the gefitinib and vorinostat combination strongly inhibited tumor growth in mice with lung adenocarcinoma or hepatocarcinoma tumor xenografts. In contrast, the combination of sorafenib and vorinostat did not inhibit cell proliferation compared to a single treatment and induced G2/M cell cycle arrest without apoptosis. The sorafenib and vorinostat combination sustained the IGF-1R-, AKT-, and mitogen-activated protein kinase-dependent signaling pathways. These results showed that there was synergistic cytotoxicity when vorinostat was combined with gefitinib for both lung adenocarcinoma and hepatocarcinoma with mutant KRAS in vitro and in vivo but that the combination of vorinostat with sorafenib did not show any benefit. These findings highlight the important role of the IGF-1R/AKT pathway in the resistance to targeted therapies and support the use of histone deacetylase inhibitors in combination with EGFR-tyrosine kinase inhibitors, especially for treating patients with mutant KRAS resistant to other treatments.

Temporal and intrinsic factors of rifampicin tolerance in mycobacteria.

Mycobacteria grow and divide asymmetrically, creating variability in growth pole age, growth properties, and antibiotic susceptibilities. Here, we investigate the importance of growth pole age and other growth properties in determining the spectrum of responses of Mycobacterium smegmatis to challenge with rifampicin. We used a combination of live-cell microscopy and modeling to prospectively identify subpopulations with altered rifampicin susceptibility. We found two subpopulations that had increased susceptibility. At the initiation of treatment, susceptible cells were either small and at early stages of the cell cycle, or large and in later stages of their cell cycle. In contrast to this temporal window of susceptibility, tolerance was associated with factors inherited at division: long birth length and mature growth poles. Thus, rifampicin response is complex and due to a combination of differences established from both asymmetric division and the timing of treatment relative to cell birth.