Identification of significant gene expression changes in multiple perturbation experiments using knockoffs.

Large-scale multiple perturbation experiments have the potential to reveal a more detailed understanding of the molecular pathways that respond to genetic and environmental changes. A key question in these studies is which gene expression changes are important for the response to the perturbation. This problem is challenging because (i) the functional form of the nonlinear relationship between gene expression and the perturbation is unknown and (ii) identification of the most important genes is a high-dimensional variable selection problem. To deal with these challenges, we present here a method based on the model-X knockoffs framework and Deep Neural Networks to identify significant gene expression changes in multiple perturbation experiments. This approach makes no assumptions on the functional form of the dependence between the responses and the perturbations and it enjoys finite sample false discovery rate control for the selected set of important gene expression responses. We apply this approach to the Library of Integrated Network-Based Cellular Signature data sets which is a National Institutes of Health Common Fund program that catalogs how human cells globally respond to chemical, genetic and disease perturbations. We identified important genes whose expression is directly modulated in response to perturbation with anthracycline, vorinostat, trichostatin-a, geldanamycin and sirolimus. We compare the set of important genes that respond to these small molecules to identify co-responsive pathways. Identification of which genes respond to specific perturbation stressors can provide better understanding of the underlying mechanisms of disease and advance the identification of new drug targets.

Outcomes in Hematopoietic Cell Transplant and Chimeric Antigen Receptor T-Cell Therapy Recipients With Pre-Cellular Therapy SARS-CoV-2 Infection.

BACKGROUND: Hematopoietic cell transplant (HCT) or chimeric antigen receptor (CAR) T-cell therapy recipients have high morbidity from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. There are limited data on outcomes from SARS-CoV-2 infection shortly before cellular therapy and uncertainty whether to delay therapy. METHODS: We conducted a retrospective cohort study of patients with SARS-CoV-2 infection within 90 days before HCT or CAR-T-cell therapy between January 2020 and November 2022. We characterized the kinetics of SARS-CoV-2 detection, clinical outcomes following cellular therapy, and impact on delays in cellular therapy. RESULTS: We identified 37 patients (n = 15 allogeneic HCT, n = 11 autologous HCT, n = 11 CAR-T-cell therapy) with SARS-CoV-2 infections within 90 days of cellular therapy. Most infections (73%) occurred between March and November 2022, when Omicron strains were prevalent. Most patients had asymptomatic (27%) or mild (68%) coronavirus disease 2019 (COVID-19). SARS-CoV-2 positivity lasted a median of 20.0 days (interquartile range, 12.5-26.25 days). The median time from first positive SARS-CoV-2 test to cellular therapy was 45 days (interquartile range, 37.75-70 days); 1 patient tested positive on the day of infusion. After cellular therapy, no patients had recrudescent SARS-CoV-2 infection or COVID-19-related complications. Cellular therapy delays related to SARS-CoV-2 infection occurred in 70% of patients for a median of 37 days. Delays were more common after allogeneic (73%) and autologous (91%) HCT compared to CAR-T-cell therapy (45%). CONCLUSIONS: Patients with asymptomatic or mild COVID-19 may not require prolonged delays in cellular therapy in the context of contemporary circulating variants and availability of antiviral therapies.

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.

Dual inhibition of MEK1/2 and MEK5 suppresses the EMT/migration axis in triple-negative breast cancer through FRA-1 regulation.

Triple-negative breast cancer (TNBC) presents a clinical challenge due to the aggressive nature of the disease and a lack of targeted therapies. Constitutive activation of the mitogen-activated protein kinase (MAPK)/extracellular signal-regulated kinase (ERK) pathway has been linked to chemoresistance and metastatic progression through distinct mechanisms, including activation of epithelial-to-mesenchymal transition (EMT) when cells adopt a motile and invasive phenotype through loss of epithelial markers (CDH1), and acquisition of mesenchymal markers (VIM, CDH2). Although MAPK/ERK1/2 kinase inhibitors (MEKi) are useful antitumor agents in a clinical setting, including the Food and Drug Administration (FDA)-approved MEK1,2 dual inhibitors cobimetinib and trametinib, there are limitations to their clinical utility, primarily adaptation of the BRAF pathway and ocular toxicities. The MEK5 (HGNC: MAP2K5) pathway has important roles in metastatic progression of various cancer types, including those of the prostate, colon, bone and breast, and elevated levels of ERK5 expression in breast carcinomas are linked to a worse prognoses in TNBC patients. The purpose of this study is to explore MEK5 regulation of the EMT axis and to evaluate a novel pan-MEK inhibitor on clinically aggressive TNBC cells. Our results show a distinction between the MEK1/2 and MEK5 cascades in maintenance of the mesenchymal phenotype, suggesting that the MEK5 pathway may be necessary and sufficient in EMT regulation while MEK1/2 signaling further sustains the mesenchymal state of TNBC cells. Furthermore, additive effects on MET induction are evident through the inhibition of both MEK1/2 and MEK5. Taken together, these data demonstrate the need for a better understanding of the individual roles of MEK1/2 and MEK5 signaling in breast cancer and provide a rationale for the combined targeting of these pathways to circumvent compensatory signaling and subsequent therapeutic resistance.

Conditional genetic screen in Physcomitrella patens reveals a novel microtubule depolymerizing-end-tracking protein.

Our ability to identify genes that participate in cell growth and division is limited because their loss often leads to lethality. A solution to this is to isolate conditional mutants where the phenotype is visible under restrictive conditions. Here, we capitalize on the haploid growth-phase of the moss Physcomitrella patens to identify conditional loss-of-growth (CLoG) mutants with impaired growth at high temperature. We used whole-genome sequencing of pooled segregants to pinpoint the lesion of one of these mutants (clog1) and validated the identified mutation by rescuing the conditional phenotype by homologous recombination. We found that CLoG1 is a novel and ancient gene conserved in plants. At the restrictive temperature, clog1 plants have smaller cells but can complete cell division, indicating an important role of CLoG1 in cell growth, but not an essential role in cell division. Fluorescent protein fusions of CLoG1 indicate it is localized to microtubules with a bias towards depolymerizing microtubule ends. Silencing CLoG1 decreases microtubule dynamics, suggesting that CLoG1 plays a critical role in regulating microtubule dynamics. By discovering a novel gene critical for plant growth, our work demonstrates that P. patens is an excellent genetic system to study genes with a fundamental role in plant cell growth.

SCSIM: Jointly simulating correlated single-cell and bulk next-generation DNA sequencing data.

BACKGROUND: Recently, it has become possible to collect next-generation DNA sequencing data sets that are composed of multiple samples from multiple biological units where each of these samples may be from a single cell or bulk tissue. Yet, there does not yet exist a tool for simulating DNA sequencing data from such a nested sampling arrangement with single-cell and bulk samples so that developers of analysis methods can assess accuracy and precision. RESULTS: We have developed a tool that simulates DNA sequencing data from hierarchically grouped (correlated) samples where each sample is designated bulk or single-cell. Our tool uses a simple configuration file to define the experimental arrangement and can be integrated into software pipelines for testing of variant callers or other genomic tools. CONCLUSIONS: The DNA sequencing data generated by our simulator is representative of real data and integrates seamlessly with standard downstream analysis tools.

Pharmacological inhibition of the MEK5/ERK5 and PI3K/Akt signaling pathways synergistically reduces viability in triple-negative breast cancer.

Triple-negative breast cancers (TNBCs) represent 15% to 20% of all breast cancers and are often associated with poor prognosis. The lack of targeted therapies for TNBCs contributes to higher mortality rates. Aberrations in the phosphoinositide-3-kinase (PI3K) and mitogen-activated protein kinase pathways have been linked to increased breast cancer proliferation and survival. It has been proposed that these survival characteristics are enhanced through compensatory signaling and crosstalk mechanisms. While the crosstalk between PI3K and extracellular signal-regulated kinase 1/2 (ERK1/2) pathways has been characterized in several systems, new evidence suggests that MEK5/ERK5 signaling is a key component in the proliferation and survival of several aggressive cancers. In this study, we examined the effects of dual inhibition of PI3K/protein kinase B (Akt) and MEK5/ERK5 in the MDA-MB-231, BT-549, and MDA-MB-468 TNBC cell lines. We used the Akt inhibitor ipatasertib, ERK5 inhibitors XMD8-92 and AX15836, and the novel MEK5 inhibitor SC-1-181 to investigate the effects of dual inhibition. Our results indicated that dual inhibition of PI3K/Akt and MEK5/ERK5 signaling was more effective at reducing the proliferation and survival of TNBCs than single inhibition of either pathway alone. In particular, a loss of Bad phosphorylation at two distinct sites was observed with dual inhibition. Furthermore, the inhibition of both pathways led to p21 restoration, decreased cell proliferation, and induced apoptosis. In addition, the dual inhibition strategy was determined to be synergistic in MDA-MB-231 and BT-549 cells and was relatively nontoxic in the nonneoplastic MCF-10 cell line. In summary, the results from this study provide a unique prospective into the utility of a novel dual inhibition strategy for targeting TNBCs.

Reverse Chemical Genetics: Comprehensive Fitness Profiling Reveals the Spectrum of Drug Target Interactions.

The emergence and prevalence of drug resistance demands streamlined strategies to identify drug resistant variants in a fast, systematic and cost-effective way. Methods commonly used to understand and predict drug resistance rely on limited clinical studies from patients who are refractory to drugs or on laborious evolution experiments with poor coverage of the gene variants. Here, we report an integrative functional variomics methodology combining deep sequencing and a Bayesian statistical model to provide a comprehensive list of drug resistance alleles from complex variant populations. Dihydrofolate reductase, the target of methotrexate chemotherapy drug, was used as a model to identify functional mutant alleles correlated with methotrexate resistance. This systematic approach identified previously reported resistance mutations, as well as novel point mutations that were validated in vivo. Use of this systematic strategy as a routine diagnostics tool widens the scope of successful drug research and development.

Variational inference for rare variant detection in deep, heterogeneous next-generation sequencing data.

BACKGROUND: The detection of rare single nucleotide variants (SNVs) is important for understanding genetic heterogeneity using next-generation sequencing (NGS) data. Various computational algorithms have been proposed to detect variants at the single nucleotide level in mixed samples. Yet, the noise inherent in the biological processes involved in NGS technology necessitates the development of statistically accurate methods to identify true rare variants. RESULTS: We propose a Bayesian statistical model and a variational expectation maximization (EM) algorithm to estimate non-reference allele frequency (NRAF) and identify SNVs in heterogeneous cell populations. We demonstrate that our variational EM algorithm has comparable sensitivity and specificity compared with a Markov Chain Monte Carlo (MCMC) sampling inference algorithm, and is more computationally efficient on tests of relatively low coverage (27× and 298×) data. Furthermore, we show that our model with a variational EM inference algorithm has higher specificity than many state-of-the-art algorithms. In an analysis of a directed evolution longitudinal yeast data set, we are able to identify a time-series trend in non-reference allele frequency and detect novel variants that have not yet been reported. Our model also detects the emergence of a beneficial variant earlier than was previously shown, and a pair of concomitant variants. CONCLUSIONS: We developed a variational EM algorithm for a hierarchical Bayesian model to identify rare variants in heterogeneous next-generation sequencing data. Our algorithm is able to identify variants in a broad range of read depths and non-reference allele frequencies with high sensitivity and specificity.

N-Acetyl-l-Cysteine Protects Astrocytes against Proteotoxicity without Recourse to Glutathione.

N-acetyl-l-cysteine (NAC) exhibits protective properties in brain injury models and has undergone a number of clinical trials. Most studies of NAC have focused on neurons. However, neuroprotection may be complemented by the protection of astrocytes because healthier astrocytes can better support the viability of neurons. Here, we show that NAC can protect astrocytes against protein misfolding stress (proteotoxicity), the hallmark of neurodegenerative disorders. Although NAC is thought to be a glutathione precursor, NAC protected primary astrocytes from the toxicity of the proteasome inhibitor MG132 without eliciting any increase in glutathione. Furthermore, glutathione depletion failed to attenuate the protective effects of NAC. MG132 elicited a robust increase in the folding chaperone heat shock protein 70 (Hsp70), and NAC mitigated this effect. Nevertheless, three independent inhibitors of Hsp70 function ablated the protective effects of NAC, suggesting that NAC may help preserve Hsp70 chaperone activity and improve protein quality control without need for Hsp70 induction. Consistent with this view, NAC abolished an increase in ubiquitinated proteins in MG132-treated astrocytes. However, NAC did not affect the loss of proteasome activity in response to MG132, demonstrating that it boosted protein homeostasis and cell viability without directly interfering with the efficacy of this proteasome inhibitor. The thiol-containing molecules l-cysteine and d-cysteine both mimicked the protective effects of NAC, whereas the thiol-lacking molecule N-acetyl-S-methyl-l-cysteine failed to exert protection or blunt the rise in ubiquitinated proteins. Collectively, these findings suggest that the thiol group in NAC is required for its effects on glial viability and protein quality control.

GEMINI: a computationally-efficient search engine for large gene expression datasets.

BACKGROUND: Low-cost DNA sequencing allows organizations to accumulate massive amounts of genomic data and use that data to answer a diverse range of research questions. Presently, users must search for relevant genomic data using a keyword, accession number of meta-data tag. However, in this search paradigm the form of the query - a text-based string - is mismatched with the form of the target - a genomic profile. RESULTS: To improve access to massive genomic data resources, we have developed a fast search engine, GEMINI, that uses a genomic profile as a query to search for similar genomic profiles. GEMINI implements a nearest-neighbor search algorithm using a vantage-point tree to store a database of n profiles and in certain circumstances achieves an [Formula: see text] expected query time in the limit. We tested GEMINI on breast and ovarian cancer gene expression data from The Cancer Genome Atlas project and show that it achieves a query time that scales as the logarithm of the number of records in practice on genomic data. In a database with 10(5) samples, GEMINI identifies the nearest neighbor in 0.05 sec compared to a brute force search time of 0.6 sec. CONCLUSIONS: GEMINI is a fast search engine that uses a query genomic profile to search for similar profiles in a very large genomic database. It enables users to identify similar profiles independent of sample label, data origin or other meta-data information.

GLAD: a mixed-membership model for heterogeneous tumor subtype classification.

MOTIVATION: Genomic analyses of many solid cancers have demonstrated extensive genetic heterogeneity between as well as within individual tumors. However, statistical methods for classifying tumors by subtype based on genomic biomarkers generally entail an all-or-none decision, which may be misleading for clinical samples containing a mixture of subtypes and/or normal cell contamination. RESULTS: We have developed a mixed-membership classification model, called glad, that simultaneously learns a sparse biomarker signature for each subtype as well as a distribution over subtypes for each sample. We demonstrate the accuracy of this model on simulated data, in-vitro mixture experiments, and clinical samples from the Cancer Genome Atlas (TCGA) project. We show that many TCGA samples are likely a mixture of multiple subtypes. AVAILABILITY: A python module implementing our algorithm is available from http://genomics.wpi.edu/glad/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

RVD2: an ultra-sensitive variant detection model for low-depth heterogeneous next-generation sequencing data.

MOTIVATION: Next-generation sequencing technology is increasingly being used for clinical diagnostic tests. Clinical samples are often genomically heterogeneous due to low sample purity or the presence of genetic subpopulations. Therefore, a variant calling algorithm for calling low-frequency polymorphisms in heterogeneous samples is needed. RESULTS: We present a novel variant calling algorithm that uses a hierarchical Bayesian model to estimate allele frequency and call variants in heterogeneous samples. We show that our algorithm improves upon current classifiers and has higher sensitivity and specificity over a wide range of median read depth and minor allele fraction. We apply our model and identify 15 mutated loci in the PAXP1 gene in a matched clinical breast ductal carcinoma tumor sample; two of which are likely loss-of-heterozygosity events. AVAILABILITY AND IMPLEMENTATION: http://genomics.wpi.edu/rvd2/. CONTACT: pjflaherty@wpi.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Prediction of multiple infections after severe burn trauma: a prospective cohort study.

OBJECTIVE: To develop predictive models for early triage of burn patients based on hypersusceptibility to repeated infections. BACKGROUND: Infection remains a major cause of mortality and morbidity after severe trauma, demanding new strategies to combat infections. Models for infection prediction are lacking. METHODS: Secondary analysis of 459 burn patients (≥16 years old) with 20% or more total body surface area burns recruited from 6 US burn centers. We compared blood transcriptomes with a 180-hour cutoff on the injury-to-transcriptome interval of 47 patients (≤1 infection episode) to those of 66 hypersusceptible patients [multiple (≥2) infection episodes (MIE)]. We used LASSO regression to select biomarkers and multivariate logistic regression to built models, accuracy of which were assessed by area under receiver operating characteristic curve (AUROC) and cross-validation. RESULTS: Three predictive models were developed using covariates of (1) clinical characteristics; (2) expression profiles of 14 genomic probes; (3) combining (1) and (2). The genomic and clinical models were highly predictive of MIE status [AUROCGenomic = 0.946 (95% CI: 0.906-0.986); AUROCClinical = 0.864 (CI: 0.794-0.933); AUROCGenomic/AUROCClinical P = 0.044]. Combined model has an increased AUROCCombined of 0.967 (CI: 0.940-0.993) compared with the individual models (AUROCCombined/AUROCClinical P = 0.0069). Hypersusceptible patients show early alterations in immune-related signaling pathways, epigenetic modulation, and chromatin remodeling. CONCLUSIONS: Early triage of burn patients more susceptible to infections can be made using clinical characteristics and/or genomic signatures. Genomic signature suggests new insights into the pathophysiology of hypersusceptibility to infection may lead to novel potential therapeutic or prophylactic targets.

The unlikely presence of deep vein thrombosis in a patient with low pretest probability and a negative D-dimer: a case report.

BACKGROUND: Deep vein thrombosis (DVT) is commonly encountered in the emergency department. Clinical models, such as the Wells criteria, allow physicians to estimate the probability of DVT in a patient. Current literature suggests a low pretest probability combined with a negative D-dimer laboratory study rules out DVT approximately 99% of the time. CASE REPORT: This case discusses a 37-year-old male patient who had a low pretest probability and a negative D-dimer, but was found to have a DVT on Doppler ultrasound. WHY SHOULD AN EMERGENCY PHYSICIAN BE AWARE OF THIS?: The astute emergency physician must not discount clinical suspicion in order to decide when radiographic imaging is warranted for a possible venous thromboembolism. New adjuncts, such as bedside ultrasonography, can also be implemented to further risk stratify patients, potentially decreasing morbidity and mortality associated with DVT.

Ultrasensitive detection of rare mutations using next-generation targeted resequencing.

With next-generation DNA sequencing technologies, one can interrogate a specific genomic region of interest at very high depth of coverage and identify less prevalent, rare mutations in heterogeneous clinical samples. However, the mutation detection levels are limited by the error rate of the sequencing technology as well as by the availability of variant-calling algorithms with high statistical power and low false positive rates. We demonstrate that we can robustly detect mutations at 0.1% fractional representation. This represents accurate detection of one mutant per every 1000 wild-type alleles. To achieve this sensitive level of mutation detection, we integrate a high accuracy indexing strategy and reference replication for estimating sequencing error variance. We employ a statistical model to estimate the error rate at each position of the reference and to quantify the fraction of variant base in the sample. Our method is highly specific (99%) and sensitive (100%) when applied to a known 0.1% sample fraction admixture of two synthetic DNA samples to validate our method. As a clinical application of this method, we analyzed nine clinical samples of H1N1 influenza A and detected an oseltamivir (antiviral therapy) resistance mutation in the H1N1 neuraminidase gene at a sample fraction of 0.18%.

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.

Development of a biomarker prediction model for post-trauma multiple organ failure/dysfunction syndrome based on the blood transcriptome.

BACKGROUND: Multiple organ failure/dysfunction syndrome (MOF/MODS) is a major cause of mortality and morbidity among severe trauma patients. Current clinical practices entail monitoring physiological measurements and applying clinical score systems to diagnose its onset. Instead, we aimed to develop an early prediction model for MOF outcome evaluated soon after traumatic injury by performing machine learning analysis of genome-wide transcriptome data from blood samples drawn within 24 h of traumatic injury. We then compared its performance to baseline injury severity scores and detection of infections. METHODS: Buffy coat transcriptome and linked clinical datasets from blunt trauma patients from the Inflammation and the Host Response to Injury Study ("Glue Grant") multi-center cohort were used. According to the inclusion/exclusion criteria, 141 adult (age ≥ 16 years old) blunt trauma patients (excluding penetrating) with early buffy coat (≤ 24 h since trauma injury) samples were analyzed, with 58 MOF-cases and 83 non-cases. We applied the Least Absolute Shrinkage and Selection Operator (LASSO) and eXtreme Gradient Boosting (XGBoost) algorithms to select features and develop models for MOF early outcome prediction. RESULTS: The LASSO model included 18 transcripts (AUROC [95% CI]: 0.938 [0.890-0.987] (training) and 0.833 [0.699-0.967] (test)), and the XGBoost model included 41 transcripts (0.999 [0.997-1.000] (training) and 0.907 [0.816-0.998] (test)). There were 16 overlapping transcripts comparing the two panels (0.935 [0.884-0.985] (training) and 0.836 [0.703-0.968] (test)). The biomarker models notably outperformed models based on injury severity scores and sex, which we found to be significantly associated with MOF (APACHEII + sex-0.649 [0.537-0.762] (training) and 0.493 [0.301-0.685] (test); ISS + sex-0.630 [0.516-0.744] (training) and 0.482 [0.293-0.670] (test); NISS + sex-0.651 [0.540-0.763] (training) and 0.525 [0.335-0.714] (test)). CONCLUSIONS: The accurate assessment of MOF from blood samples immediately after trauma is expected to aid in improving clinical decision-making and may contribute to reduced morbidity, mortality and healthcare costs. Moreover, understanding the molecular mechanisms involving the transcripts identified as important for MOF prediction may eventually aid in developing novel interventions.