Interface-guided phenotyping of coding variants in the transcription factor RUNX1.

Single-gene missense mutations remain challenging to interpret. Here, we deploy scalable functional screening by sequencing (SEUSS), a Perturb-seq method, to generate mutations at protein interfaces of RUNX1 and quantify their effect on activities of downstream cellular programs. We evaluate single-cell RNA profiles of 115 mutations in myelogenous leukemia cells and categorize them into three functionally distinct groups, wild-type (WT)-like, loss-of-function (LoF)-like, and hypomorphic, that we validate in orthogonal assays. LoF-like variants dominate the DNA-binding site and are recurrent in cancer; however, recurrence alone does not predict functional impact. Hypomorphic variants share characteristics with LoF-like but favor protein interactions, promoting gene expression indicative of nerve growth factor (NGF) response and cytokine recruitment of neutrophils. Accessible DNA near differentially expressed genes frequently contains RUNX1-binding motifs. Finally, we reclassify 16 variants of uncertain significance and train a classifier to predict 103 more. Our work demonstrates the potential of targeting protein interactions to better define the landscape of phenotypes reachable by missense mutations.

Gemcitabine-loaded chitosan nanoparticles enhanced apoptotic and ferroptotic response of gemcitabine treatment alone in the pancreatic cancer cells in vitro.

Gemcitabine (GEM) is a first-line treatment for pancreatic ductal adenocarcinoma (PDAC) patients, causing side effects and poor overall survival. Eighty percent of patients often develop resistance rapidly to GEM. Developing therapeutic approaches and increasing sensitivity to gemcitabine in PDAC has become one of the challenges in cancer research. We synthesized GEM-loaded NPs prepared with a method that combines ultrasonication and ionotropic gelation to overcome GEM-related limitations in PDAC. CFPAC-1 cells were treated with increased concentrations of GEM, empty chitosan, and GEM-loaded NPs (0.66, 1.32, 2.64, 5.32 µg/ml) for up to 48 h. Empty chitosan NPs did not show toxicity on L929 cells. Antioxidant enzyme activities, including glucose 6-phosphate dehydrogenase (G6PD), 6-phosphogluconate dehydrogenase (6-PGD), glutathione reductase (GR), glutathione s-transferase (GST), and glutathione peroxidase (GPx), significantly reduced in GEM-loaded NPs compared to the GEM associated with increased oxidative stress, PPP, and glycolysis. Bcl-xL, NOXA/mcl-1, and Ca2+ levels significantly increased in GEM-loaded NP-administered cells compared to the GEM and control groups. In contrast, JNK, p38, STAT3, Akt, and CREB levels significantly decreased in the GEM-loaded NP group, addressing enhanced apoptotic response compared to the GEM alone. Increased ferroptosis activity in GEM-loaded NP-administered groups has been validated via decreased antioxidant enzyme activities, increased cytosolic Fe, Zn, Mg, and Mn levels, and reduced GPx activity compared to the GEM and control groups. For the first time in the literature, we showed biocompatible GEM-loaded NPs enhanced apoptotic and ferroptotic response in CFPAC-1 cells via downregulation of antioxidant, glycolysis, and PPP metabolism compared to the GEM alone.

Interface-guided phenotyping of coding variants in the transcription factor RUNX1 with SEUSS.

Understanding the consequences of single amino acid substitutions in cancer driver genes remains an unmet need. Perturb-seq provides a tool to investigate the effects of individual mutations on cellular programs. Here we deploy SEUSS, a Perturb-seq like approach, to generate and assay mutations at physical interfaces of the RUNX1 Runt domain. We measured the impact of 115 mutations on RNA profiles in single myelogenous leukemia cells and used the profiles to categorize mutations into three functionally distinct groups: wild-type (WT)-like, loss-of-function (LOF)-like and hypomorphic. Notably, the largest concentration of functional mutations (non-WT-like) clustered at the DNA binding site and contained many of the more frequently observed mutations in human cancers. Hypomorphic variants shared characteristics with loss of function variants but had gene expression profiles indicative of response to neural growth factor and cytokine recruitment of neutrophils. Additionally, DNA accessibility changes upon perturbations were enriched for RUNX1 binding motifs, particularly near differentially expressed genes. Overall, our work demonstrates the potential of targeting protein interaction interfaces to better define the landscape of prospective phenotypes reachable by amino acid substitutions.

Effects of Particle Geometry for PLGA-Based Nanoparticles: Preparation and In Vitro/In Vivo Evaluation.

The physicochemical properties (size, shape, zeta potential, porosity, elasticity, etc.) of nanocarriers influence their biological behavior directly, which may result in alterations of the therapeutic outcome. Understanding the effect of shape on the cellular interaction and biodistribution of intravenously injected particles could have fundamental importance for the rational design of drug delivery systems. In the present study, spherical, rod and elliptical disk-shaped PLGA nanoparticles were developed for examining systematically their behavior in vitro and in vivo. An important finding is that the release of the encapsulated human serum albumin (HSA) was significantly higher in spherical particles compared to rod and elliptical disks, indicating that the shape can make a difference. Safety studies showed that the toxicity of PLGA nanoparticles is not shape dependent in the studied concentration range. This study has pioneering findings on comparing spherical, rod and elliptical disk-shaped PLGA nanoparticles in terms of particle size, particle size distribution, colloidal stability, morphology, drug encapsulation, drug release, safety of nanoparticles, cellular uptake and biodistribution. Nude mice bearing non-small cell lung cancer were treated with 3 differently shaped nanoparticles, and the accumulation of nanoparticles in tumor tissue and other organs was not statistically different (p > 0.05). It was found that PLGA nanoparticles with 1.00, 4.0 ± 0.5, 7.5 ± 0.5 aspect ratios did not differ on total tumor accumulation in non-small cell lung cancer.

A novel combination for the treatment of small cell lung cancer: Active targeted irinotecan and stattic co-loaded PLGA nanoparticles.

Polymeric nanoparticles are widely used drug delivery systems for cancer treatment due to their properties such as ease of passing through biological membranes, opportunity to modify drug release, specifically targeting drugs to diseased areas, and potential of reducing side effects. Here, we formulated irinotecan and Stattic co-loaded PLGA nanoparticles targeted to small cell lung cancer. Nanoparticles were successfully conjugated with CD56 antibody with a conjugation efficiency of 84.39 ± 1.01%, and characterization of formulated nanoparticles was conducted with in-vitro and in-vivo studies. Formulated particles had sizes in the range of 130-180 nm with PDI values smaller than 0.3. Encapsulation and active targeting of irinotecan and Stattic resulted in increased cytotoxicity and anti-cancer efficiency in-vitro. Furthermore, it was shown with ex-vivo biodistribution studies that conjugated nanoparticles were successfully targeted to CD56-expressing SCLC cells and distributed mainly to tumor tissue and lungs. Compliant with our hypothesis and literature, the STAT3 pathway was successfully inhibited with Stattic solution and Stattic loaded nanoparticles. Additionally, intravenous injection of conjugated co-loaded nanoparticles resulted in decreased side effects and better anti-tumor activity than individual solutions of drugs in SCLC tumor-bearing mice. These results may indicate a new treatment option for clinically aggressive small cell lung cancer.

Predicting functional consequences of mutations using molecular interaction network features.

Variant interpretation remains a central challenge for precision medicine. Missense variants are particularly difficult to understand as they change only a single amino acid in a protein sequence yet can have large and varied effects on protein activity. Numerous tools have been developed to identify missense variants with putative disease consequences from protein sequence and structure. However, biological function arises through higher order interactions among proteins and molecules within cells. We therefore sought to capture information about the potential of missense mutations to perturb protein interaction networks by integrating protein structure and interaction data. We developed 16 network-based annotations for missense mutations that provide orthogonal information to features classically used to prioritize variants. We then evaluated them in the context of a proven machine-learning framework for variant effect prediction across multiple benchmark datasets to demonstrate their potential to improve variant classification. Interestingly, network features resulted in larger performance gains for classifying somatic mutations than for germline variants, possibly due to different constraints on what mutations are tolerated at the cellular versus organismal level. Our results suggest that modeling variant potential to perturb context-specific interactome networks is a fruitful strategy to advance in silico variant effect prediction.

Mixed micelles formulation for carvedilol delivery: In-vitro characterization and in-vivo evaluation.

Carvedilol (CAR) is a widely studied, beta and alpha-1 blocker, antihypertensive drug due to its poor water solubility and low oral bioavailability (25-35%). The aim of this work is to improve poor water solubility and the pharmacokinetic parameters of carvedilol by using an optimized and self-assembly prepared micelle formulation. Optimized micelle formulation composed of Pluronic® F127, D-α-tocopheryl polyethylene glycol 1000 succinate, L-cysteine HCl in a ratio of 4:3:3. Micellar size, polydispersity index, zeta potential, morphology, critical micelle concentration, thermal behaviors, in-vitro dissolution of micelles and pharmacokinetic parameters in rats were characterized in this study. Carvedilol aqueous solubility increased (up to 271-fold) as a result of its encapsulation within a mixed micelle formulation. The measured micellar sizes of blank and carvedilol loaded mixed micelles are lower than 30 nm with size distributions of 26.69 ±â€¯2.93 nm and 24.16 ±â€¯4.89 nm, respectively. Transmission electron microscopy revealed that the micelles were spherically shaped. There is a significant enhancement of carvedilol dissolution compared to commercially available tablet formulation (f2 < 50). The in-vivo test demonstrated that the t1/2 and AUC0-∞ values of micelles were approximately 10.89- and 2.65-fold greater than that of the commercial tablets, respectively. Based on our study, bring such applications into being may provide effective new drugs for treatment armamentarium of cardiovascular diseases and hypertension in near future.

A phenotypically supervised single-cell analysis protocol to study within-cell-type heterogeneity of cultured mammalian cells.

Here, we describe a protocol combining functional metrics with genomic data to elucidate drivers of within-cell-type heterogeneity via the phenotype-to-genotype link. This technique involves using fluorescence tagging to label and isolate cells grown in 3D culture, enabling high-throughput enrichment of phenotypically defined cell subpopulations by fluorescence-activated cell sorting. We then perform a validated phenotypically supervised single-cell analysis pipeline to reveal unique functional cell states, including genes and pathways that contribute to cellular heterogeneity and were undetectable by unsupervised analysis. For complete details on the use and execution of this protocol, please refer to Chen et al. (2020).

In silico analysis suggests less effective MHC-II presentation of SARS-CoV-2 RBM peptides: Implication for neutralizing antibody responses.

SARS-CoV-2 antibodies develop within two weeks of infection, but wane relatively rapidly post-infection, raising concerns about whether antibody responses will provide protection upon re-exposure. Here we revisit T-B cooperation as a prerequisite for effective and durable neutralizing antibody responses centered on a mutationally constrained RBM B cell epitope. T-B cooperation requires co-processing of B and T cell epitopes by the same B cell and is subject to MHC-II restriction. We evaluated MHC-II constraints relevant to the neutralizing antibody response to a mutationally-constrained B cell epitope in the receptor binding motif (RBM) of the spike protein. Examining common MHC-II alleles, we found that peptides surrounding this key B cell epitope are predicted to bind poorly, suggesting a lack MHC-II support in T-B cooperation, impacting generation of high-potency neutralizing antibodies in the general population. Additionally, we found that multiple microbial peptides had potential for RBM cross-reactivity, supporting previous exposures as a possible source of T cell memory.

Phenotypically supervised single-cell sequencing parses within-cell-type heterogeneity.

To better understand cellular communication driving diverse behaviors, we need to uncover the molecular mechanisms of within-cell-type functional heterogeneity. While single-cell RNA sequencing (scRNAseq) has advanced our understanding of cell heterogeneity, linking individual cell phenotypes to transcriptomic data remains challenging. Here, we used a phenotypic cell sorting technique to ask whether phenotypically supervised scRNAseq analysis (pheno-scRNAseq) can provide more insight into heterogeneous cell behaviors than unsupervised scRNAseq. Using a simple 3D in vitro breast cancer (BRCA) model, we conducted pheno-scRNAseq on invasive and non-invasive cells and compared the results to phenotype-agnostic scRNAseq analysis. Pheno-scRNAseq identified unique and more selective differentially expressed genes than unsupervised scRNAseq analysis. Functional studies validated the utility of pheno-scRNAseq in understanding within-cell-type functional heterogeneity and revealed that migration phenotypes were coordinated with specific metabolic, proliferation, stress, and immune phenotypes. This approach lends new insight into the molecular systems underlying BRCA cell phenotypic heterogeneity.

Aggregation of chitosan nanoparticles in cell culture: Reasons and resolutions.

Nanoparticles are promising drug delivery systems which are flexible for targeting specific tissues to reduce therapeutic doses and minimize side effects. Nanoparticles should be maintained with high stability and uniformity; however, aggregation is a major challenge which commonly impairs stability and efficacy of nanocarriers. In this study, we revisited the factors that influence the stability of chitosan (Protasan™ UP CL113) nanoparticles prepared with ionotropic gelation, widely recognized to be prone to aggregation, and proposed a model to overcome the negative influence of aggregation while testing in vitro efficacy. Decrease in pH due to cell proliferation, 37 °C cell culture temperature, serum in culture media, and incubation time were considered as factors causing chitosan nanoparticles' aggregation which deteriorates cell culture assay readouts, increases optical density values and leads to false-positive results. Size and stability studies were not sufficient to avoid misleading results in cell culture. The chitosan nanoparticle aggregation was almost inevitable under standard culture conditions; nevertheless, the removal of nanoparticles before aggregation but after an incubation period long enough for efficient cellular uptake was determined as a feasible and inexpensive method for testing the in vitro efficacy of polymeric nanoformulations. This approach was used with blank and gemcitabine-loaded chitosan nanoparticles on pancreatic cancer cells and proved to be useful for reliable cytotoxicity results.

MHC-II constrains the natural neutralizing antibody response to the SARS-CoV-2 spike RBM in humans.

SARS-CoV-2 antibodies develop within two weeks of infection, but wane relatively rapidly post-infection, raising concerns about whether antibody responses will provide protection upon re-exposure. Here we revisit T-B cooperation as a prerequisite for effective and durable neutralizing antibody responses centered on a mutationally constrained RBM B cell epitope. T-B cooperation requires co-processing of B and T cell epitopes by the same B cell and is subject to MHC-II restriction. We evaluated MHC-II constraints relevant to the neutralizing antibody response to a mutationally-constrained B cell epitope in the receptor binding motif (RBM) of the spike protein. Examining common MHC-II alleles, we found that peptides surrounding this key B cell epitope are predicted to bind poorly, suggesting a lack MHC-II support in T-B cooperation, impacting generation of high-potency neutralizing antibodies in the general population. Additionally, we found that multiple microbial peptides had potential for RBM cross-reactivity, supporting previous exposures as a possible source of T cell memory.

Elevated neoantigen levels in tumors with somatic mutations in the HLA-A, HLA-B, HLA-C and B2M genes.

BACKGROUND: The major histocompatibility complex class I (MHC-I) molecule is a protein complex that displays intracellular peptides to T cells, allowing the immune system to recognize and destroy infected or cancerous cells. MHC-I is composed of a highly polymorphic HLA-encoded alpha chain that binds the peptide and a Beta-2-microglobulin (B2M) protein that acts as a stabilizing scaffold. HLA mutations have been implicated as a mechanism of immune evasion during tumorigenesis, and B2M is considered a tumor suppressor gene. However, the implications of somatic HLA and B2M mutations have not been fully explored in the context of antigen presentation via the MHC-I molecule during tumor development. To understand the effect that B2M and HLA MHC-I molecule mutations have on mutagenesis, we analyzed the accumulation of mutations in patients from The Cancer Genome Atlas according to their MHC-I molecule mutation status. RESULTS: Somatic B2M and HLA mutations in microsatellite stable tumors were associated with higher overall mutation burden and a larger fraction of HLA-binding neoantigens when compared to B2M and HLA wild type tumors. B2M and HLA mutations were highly enriched in patients with microsatellite instability. B2M mutations tended to occur relatively early during patients' respective tumor development, whereas HLA mutations were either early or late events. In addition, B2M and HLA mutated patients had higher levels of immune infiltration by natural killer and CD8+ T cells and higher levels of cytotoxicity. CONCLUSIONS: Our findings add to a growing body of evidence that somatic B2M and HLA mutations are a mechanism of immune evasion by demonstrating that such mutations are associated with a higher load of neoantigens that should be presented via MHC-I.

Performance of computational methods for the evaluation of pericentriolar material 1 missense variants in CAGI-5.

The CAGI-5 pericentriolar material 1 (PCM1) challenge aimed to predict the effect of 38 transgenic human missense mutations in the PCM1 protein implicated in schizophrenia. Participants were provided with 16 benign variants (negative controls), 10 hypomorphic, and 12 loss of function variants. Six groups participated and were asked to predict the probability of effect and standard deviation associated to each mutation. Here, we present the challenge assessment. Prediction performance was evaluated using different measures to conclude in a final ranking which highlights the strengths and weaknesses of each group. The results show a great variety of predictions where some methods performed significantly better than others. Benign variants played an important role as negative controls, highlighting predictors biased to identify disease phenotypes. The best predictor, Bromberg lab, used a neural-network-based method able to discriminate between neutral and non-neutral single nucleotide polymorphisms. The CAGI-5 PCM1 challenge allowed us to evaluate the state of the art techniques for interpreting the effect of novel variants for a difficult target protein.

Strategies for Network GWAS Evaluated Using Classroom Crowd Science.

Biological networks can substantially boost power to identify disease genes in genome-wide association studies. To explore different network GWAS methods, we challenged students of a UC San Diego graduate level bioinformatics course, Network Biology and Biomedicine, to explore and improve such algorithms during a four-week-long classroom competition. Here, we report the many creative solutions and share our experiences in conducting classroom crowd science as both a research and pedagogical tool.

Identifying Driver Interfaces Enriched for Somatic Missense Mutations in Tumors.

Human cancers often harbor large numbers of somatic mutations. However, only a small proportion of these mutations are expected to contribute to tumor growth and progression. Therefore, determining causal driver mutations and the genes they target is becoming an important challenge in cancer genomics. Here we describe an approach for mapping somatic mutations onto 3D structures of human proteins in complex to identify "driver interfaces." Our strategy relies on identifying protein-interaction interfaces that are unexpectedly biased toward nonsynonymous mutations, which suggests that these interfaces are subject to positive selection during tumorigenesis, implicating the interacting proteins as candidate drivers.

Integrating molecular networks with genetic variant interpretation for precision medicine.

More reliable and cheaper sequencing technologies have revealed the vast mutational landscapes characteristic of many phenotypes. The analysis of such genetic variants has led to successful identification of altered proteins underlying many Mendelian disorders. Nevertheless the simple one-variant one-phenotype model valid for many monogenic diseases does not capture the complexity of polygenic traits and disorders. Although experimental and computational approaches have improved detection of functionally deleterious variants and important interactions between gene products, the development of comprehensive models relating genotype and phenotypes remains a challenge in the field of genomic medicine. In this context, a new view of the pathologic state as significant perturbation of the network of interactions between biomolecules is crucial for the identification of biochemical pathways associated with complex phenotypes. Seminal studies in systems biology combined the analysis of genetic variation with protein-protein interaction networks to demonstrate that even as biological systems evolve to be robust to genetic variation, their topologies create disease vulnerabilities. More recent analyses model the impact of genetic variants as changes to the "wiring" of the interactome to better capture heterogeneity in genotype-phenotype relationships. These studies lay the foundation for using networks to predict variant effects at scale using machine-learning or algorithmic approaches. A wealth of databases and resources for the annotation of genotype-phenotype relationships have been developed to support developments in this area. This overview describes how study of the molecular interactome has generated insights linking the organization of biological systems to disease mechanism, and how this information can enable precision medicine. This article is categorized under: Translational, Genomic, and Systems Medicine > Translational Medicine Biological Mechanisms > Cell Signaling Models of Systems Properties and Processes > Mechanistic Models Analytical and Computational Methods > Computational Methods.

The Emerging Potential for Network Analysis to Inform Precision Cancer Medicine.

Precision cancer medicine promises to tailor clinical decisions to patients using genomic information. Indeed, successes of drugs targeting genetic alterations in tumors, such as imatinib that targets BCR-ABL in chronic myelogenous leukemia, have demonstrated the power of this approach. However, biological systems are complex, and patients may differ not only by the specific genetic alterations in their tumor, but also by more subtle interactions among such alterations. Systems biology and more specifically, network analysis, provides a framework for advancing precision medicine beyond clinical actionability of individual mutations. Here we discuss applications of network analysis to study tumor biology, early methods for N-of-1 tumor genome analysis, and the path for such tools to the clinic.