Discovering Genetic Modulators of the Protein Homeostasis System through Multilevel Analysis.

Every protein progresses through a natural lifecycle from birth to maturation to death; this process is coordinated by the protein homeostasis system. Environmental or physiological conditions trigger pathways that maintain the homeostasis of the proteome. An open question is how these pathways are modulated to respond to the many stresses that an organism encounters during its lifetime. To address this question, we tested how the fitness landscape changes in response to environmental and genetic perturbations using directed and massively parallel transposon mutagenesis in Caulobacter crescentus. We developed a general computational pipeline for the analysis of gene-by-environment interactions in transposon mutagenesis experiments. This pipeline uses a combination of general linear models (GLMs), statistical knockoffs, and a nonparametric Bayesian statistical model to identify essential genetic network components that are shared across environmental perturbations. This analysis allows us to quantify the similarity of proteotoxic environmental perturbations from the perspective of the fitness landscape. We find that essential genes vary more by genetic background than by environmental conditions, with limited overlap among mutant strains targeting different facets of the protein homeostasis system. We also identified 146 unique fitness determinants across different strains, with 19 genes common to at least two strains, showing varying resilience to proteotoxic stresses. Experiments exposing cells to a combination of genetic perturbations and dual environmental stressors show that perturbations that are quantitatively dissimilar from the perspective of the fitness landscape are likely to have a synergistic effect on the growth defect.

ATP hydrolysis tunes specificity of a AAA+ protease.

In bacteria, AAA+ proteases such as Lon and ClpXP degrade substrates with exquisite specificity. These machines capture the energy of ATP hydrolysis to power unfolding and degradation of target substrates. Here, we show that a mutation in the ATP binding site of ClpX shifts protease specificity to promote degradation of normally Lon-restricted substrates. However, this ClpX mutant is worse at degrading ClpXP targets, suggesting an optimal balance in substrate preference for a given protease that is easy to alter. In vitro, wild-type ClpXP also degrades Lon-restricted substrates more readily when ATP levels are reduced, similar to the shifted specificity of mutant ClpXP, which has altered ATP hydrolysis kinetics. Based on these results, we suggest that the rates of ATP hydrolysis not only power substrate unfolding and degradation, but also tune protease specificity. We consider various models for this effect based on emerging structures of AAA+ machines showing conformationally distinct states.

Model-based identification of conditionally-essential genes from transposon-insertion sequencing data.

The understanding of bacterial gene function has been greatly enhanced by recent advancements in the deep sequencing of microbial genomes. Transposon insertion sequencing methods combines next-generation sequencing techniques with transposon mutagenesis for the exploration of the essentiality of genes under different environmental conditions. We propose a model-based method that uses regularized negative binomial regression to estimate the change in transposon insertions attributable to gene-environment changes in this genetic interaction study without transformations or uniform normalization. An empirical Bayes model for estimating the local false discovery rate combines unique and total count information to test for genes that show a statistically significant change in transposon counts. When applied to RB-TnSeq (randomized barcode transposon sequencing) and Tn-seq (transposon sequencing) libraries made in strains of Caulobacter crescentus using both total and unique count data the model was able to identify a set of conditionally beneficial or conditionally detrimental genes for each target condition that shed light on their functions and roles during various stress conditions.

Isolation of intact extracellular vesicles from cryopreserved samples.

Extracellular vesicles (EVs) have emerged as promising candidates in biomarker discovery and diagnostics. Protected by the lipid bilayer, the molecular content of EVs in diverse biofluids are protected from RNases and proteases in the surrounding environment that may rapidly degrade targets of interests. Nonetheless, cryopreservation of EV-containing samples to -80°C may expose the lipid bilayer to physical and biological stressors which may result in cryoinjury and contribute to changes in EV yield, function, or molecular cargo. In the present work, we systematically evaluate the effect of cryopreservation at -80°C for a relatively short duration of storage (up to 12 days) on plasma- and media-derived EV particle count and/or RNA yield/quality, as compared to paired fresh controls. On average, we found that the plasma-derived EV concentration of stored samples decreased to 23% of fresh samples. Further, this significant decrease in EV particle count was matched with a corresponding significant decrease in RNA yield whereby plasma-derived stored samples contained only 47-52% of the total RNA from fresh samples, depending on the extraction method used. Similarly, media-derived EVs showed a statistically significant decrease in RNA yield whereby stored samples were 58% of the total RNA from fresh samples. In contrast, we did not obtain clear evidence of decreased RNA quality through analysis of RNA traces. These results suggest that samples stored for up to 12 days can indeed produce high-quality RNA; however, we note that when directly comparing fresh versus cryopreserved samples without cryoprotective agents there are significant losses in total RNA. Finally, we demonstrate that the addition of the commonly used cryoprotectant agent, DMSO, alongside greater control of the rate of cooling/warming, can rescue EVs from damaging ice formation and improve RNA yield.

Transcriptomic signature of painful human neurofibromatosis type 2 schwannomas.

Schwannomas are benign neoplasms that can cause gain- and loss-of-function neurological phenotypes, including severe, intractable pain. To investigate the molecular mechanisms underlying schwannoma-associated pain we compared the RNA sequencing profile of painful and non-painful schwannomas from NF2 patients. Distinct segregation of painful and non-painful tumors by gene expression patterns was observed. Differential expression analysis showed the upregulation of fibroblast growth factor 7 (FGF7) in painful schwannomas. Behavioral support for this finding was observed using a xenograft human NF2-schwannoma model in nude mice. In this model, over-expression of FGF7 in intra-sciatically implanted NF2 tumor cells generated pain behavior compared with controls.

Microfluidic concentration and separation of circulating tumor cell clusters from large blood volumes.

Circulating tumor cells (CTCs) are extremely rare in the blood, yet they account for metastasis. Notably, it was reported that CTC clusters (CTCCs) can be 50-100 times more metastatic than single CTCs, making them particularly salient as a liquid biopsy target. Yet they can split apart and are even rarer, complicating their recovery. Isolation by filtration risks loss when clusters squeeze through filter pores over time, and release of captured clusters can be difficult. Deterministic lateral displacement is continuous but requires channels not much larger than clusters, leading to clogging. Spiral inertial focusing requires large blood dilution factors (or lysis). Here, we report a microfluidic chip that continuously isolates untouched CTC clusters from large volumes of minimally (or undiluted) whole blood. An array of 100 µm-wide channels first concentrates clusters in the blood, and then a similar array transfers them into a small volume of buffer. The microscope-slide-sized PDMS device isolates individually-spiked CTC clusters from >30 mL per hour of whole blood with 80% efficiency into enumeration (fluorescence imaging), and on-chip yield approaches 100% (high speed video). Median blood cell removal (in base-10 logs) is 4.2 for leukocytes, 5.5 for red blood cells, and 4.9 for platelets, leaving less than 0.01% of leukocytes alongside CTC clusters in the product. We also demonstrate that cluster configurations are preserved. Gentle, high throughput concentration and separation of circulating tumor cell clusters from large blood volumes will enable cluster-specific diagnostics and speed the generation of patient-specific CTC cluster lines.

Engineered nanointerfaces for microfluidic isolation and molecular profiling of tumor-specific extracellular vesicles.

Extracellular vesicles (EVs) carry RNA, DNA, proteins, and lipids. Specifically, tumor-derived EVs have the potential to be utilized as disease-specific biomarkers. However, a lack of methods to isolate tumor-specific EVs has limited their use in clinical settings. Here we report a sensitive analytical microfluidic platform (EVHB-Chip) that enables tumor-specific EV-RNA isolation within 3 h. Using the EVHB-Chip, we achieve 94% tumor-EV specificity, a limit of detection of 100 EVs per µL, and a 10-fold increase in tumor RNA enrichment in comparison to other methods. Our approach allows for the subsequent release of captured tumor EVs, enabling downstream characterization and functional studies. Processing serum and plasma samples from glioblastoma multiforme (GBM) patients, we can detect the mutant EGFRvIII mRNA. Moreover, using next-generation RNA sequencing, we identify genes specific to GBM as well as transcripts that are hallmarks for the four genetic subtypes of the disease.