CD8+ T Cell-Dependent Antitumor Activity In Vivo of a Mass Spectrometry-Identified Neoepitope despite Undetectable CD8+ Immunogenicity In Vitro.

Identification of neoepitopes that can control tumor growth in vivo remains a challenge even 10 y after the first genomics-defined cancer neoepitopes were identified. In this study, we identify a neoepitope, resulting from a mutation in the junction plakoglobin (Jup) gene (chromosome 11), from the mouse colon cancer line MC38-FABF (C57BL/6). This neoepitope, Jup mutant (JupMUT), was detected during mass spectrometry of MHC class I-eluted peptides from the tumor. JupMUT has a predicted binding affinity of 564 nM for the Kb molecule and a higher predicted affinity of 82 nM for Db. However, whereas structural modeling of JupMUT and its unmutated counterpart Jup wild-type indicates that there are little conformational differences between the two epitopes bound to Db, large structural divergences are predicted between the two epitopes bound to Kb. Together with in vitro binding data with RMA-S cells, these data suggest that Kb rather than Db is the relevant MHC class I molecule of JupMUT. Immunization of naive C57BL/6 mice with JupMUT elicits CD8-dependent tumor control of a MC38-FABF challenge. Despite the CD8 dependence of JupMUT-mediated tumor control in vivo, CD8+ T cells from JupMUT-immunized mice do not produce higher levels of IFN-γ than do naive mice. The structural and immunological characteristics of JupMUT are substantially different from those of many other neoepitopes that have been shown to mediate tumor control.

GeNeo: A Bioinformatics Toolbox for Genomics-Guided Neoepitope Prediction.

High-throughput DNA and RNA sequencing are revolutionizing precision oncology, enabling personalized therapies such as cancer vaccines designed to target tumor-specific neoepitopes generated by somatic mutations expressed in cancer cells. Identification of these neoepitopes from next-generation sequencing data of clinical samples remains challenging and requires the use of complex bioinformatics pipelines. In this paper, we present GeNeo, a bioinformatics toolbox for genomics-guided neoepitope prediction. GeNeo includes a comprehensive set of tools for somatic variant calling and filtering, variant validation, and neoepitope prediction and filtering. For ease of use, GeNeo tools can be accessed via web-based interfaces deployed on a Galaxy portal publicly accessible at https://neo.engr.uconn.edu/. A virtual machine image for running GeNeo locally is also available to academic users upon request.

TNet: Transmission Network Inference Using Within-Host Strain Diversity and its Application to Geographical Tracking of COVID-19 Spread.

The inference of disease transmission networks is an important problem in epidemiology. One popular approach for building transmission networks is to reconstruct a phylogenetic tree using sequences from disease strains sampled from infected hosts and infer transmissions based on this tree. However, most existing phylogenetic approaches for transmission network inference are highly computationally intensive and cannot take within-host strain diversity into account. Here, we introduce a new phylogenetic approach for inferring transmission networks, TNet, that addresses these limitations. TNet uses multiple strain sequences from each sampled host to infer transmissions and is simpler and more accurate than existing approaches. Furthermore, TNet is highly scalable and able to distinguish between ambiguous and unambiguous transmission inferences. We evaluated TNet on a large collection of 560 simulated transmission networks of various sizes and diverse host, sequence, and transmission characteristics, as well as on 10 real transmission datasets with known transmission histories. Our results show that TNet outperforms two other recently developed methods, phyloscanner and SharpTNI, that also consider within-host strain diversity. We also applied TNet to a large collection of SARS-CoV-2 genomes sampled from infected individuals in many countries around the world, demonstrating how our inference framework can be adapted to accurately infer geographical transmission networks. TNet is freely available from https://compbio.engr.uconn.edu/software/TNet/.

Reversion analysis reveals the in vivo immunogenicity of a poorly MHC I-binding cancer neoepitope.

High-affinity MHC I-peptide interactions are considered essential for immunogenicity. However, some neo-epitopes with low affinity for MHC I have been reported to elicit CD8 T cell dependent tumor rejection in immunization-challenge studies. Here we show in a mouse model that a neo-epitope that poorly binds to MHC I is able to enhance the immunogenicity of a tumor in the absence of immunization. Fibrosarcoma cells with a naturally occurring mutation are edited to their wild type counterpart; the mutation is then re-introduced in order to obtain a cell line that is genetically identical to the wild type except for the neo-epitope-encoding mutation. Upon transplantation into syngeneic mice, all three cell lines form tumors that are infiltrated with activated T cells. However, lymphocytes from the two tumors that harbor the mutation show significantly stronger transcriptional signatures of cytotoxicity and TCR engagement, and induce greater breadth of TCR reactivity than those of the wild type tumors. Structural modeling of the neo-epitope peptide/MHC I pairs suggests increased hydrophobicity of the neo-epitope surface, consistent with higher TCR reactivity. These results confirm the in vivo immunogenicity of low affinity or 'non-binding' epitopes that do not follow the canonical concept of MHC I-peptide recognition.

Pipeline for Analyzing Activity of Metabolic Pathways in Planktonic Communities Using Metatranscriptomic Data.

In this article, we present our novel pipeline for analysis of metabolic activity using a microbial community's metatranscriptome sequence data set for validation. Our method is based on expectation-maximization (EM) algorithm and provides enzyme expression and pathway activity levels. Further expanding our analysis, we consider individual enzymatic activity and compute enzyme participation coefficients to approximate the metabolic pathway activity more accurately. We apply our EM pathways pipeline to a metatranscriptomic data set of a plankton community from surface waters of the Northern Gulf of Mexico. The data set consists of RNA-seq data and respective environmental parameters, which were sampled at two depths, six times a day over multiple 24-hour cycles. Furthermore, we discuss microbial dependence on day-night cycle within our findings based on a three-way correlation of the enzyme expression during antipodal times-midnight and noon. We show that the enzyme participation levels strongly affect the metabolic activity estimates: that is, marginal and multiple linear regression of enzymatic and metabolic pathway activity correlated significantly with the recorded environmental parameters. Our analysis statistically validates that EM-based methods produce meaningful results, as our method confirms statistically significant dependence of metabolic pathway activity on the environmental parameters, such as salinity, temperature, brightness, and a few others.

SC1: A Tool for Interactive Web-Based Single-Cell RNA-Seq Data Analysis.

Single-cell RNA-Seq (scRNA-Seq) is critical for studying cellular function and phenotypic heterogeneity as well as the development of tissues and tumors. In this study, we present SC1 a web-based highly interactive scRNA-Seq data analysis tool publicly accessible at https://sc1.engr.uconn.edu. The tool presents an integrated workflow for scRNA-Seq analysis, implements a novel method of selecting informative genes based on term-frequency inverse-document-frequency scores, and provides a broad range of methods for clustering, differential expression analysis, gene enrichment, interactive visualization, and cell cycle analysis. The tool integrates other single-cell omics data modalities such as T-cell receptor (TCR)-Seq and supports several single-cell sequencing technologies. In just a few steps, researchers can generate a comprehensive analysis and gain powerful insights from their scRNA-Seq data.

Direct reprogramming of oligodendrocyte precursor cells into GABAergic inhibitory neurons by a single homeodomain transcription factor Dlx2.

Oligodendrocyte precursor cells (NG2 glia) are uniformly distributed proliferative cells in the mammalian central nervous system and generate myelinating oligodendrocytes throughout life. A subpopulation of OPCs in the neocortex arises from progenitor cells in the embryonic ganglionic eminences that also produce inhibitory neurons. The neuronal fate of some progenitor cells is sealed before birth as they become committed to the oligodendrocyte lineage, marked by sustained expression of the oligodendrocyte transcription factor Olig2, which represses the interneuron transcription factor Dlx2. Here we show that misexpression of Dlx2 alone in postnatal mouse OPCs caused them to switch their fate to GABAergic neurons within 2 days by downregulating Olig2 and upregulating a network of inhibitory neuron transcripts. After two weeks, some OPC-derived neurons generated trains of action potentials and formed clusters of GABAergic synaptic proteins. Our study revealed that the developmental molecular logic can be applied to promote neuronal reprogramming from OPCs.

An unbiased approach to defining bona fide cancer neoepitopes that elicit immune-mediated cancer rejection.

Identification of neoepitopes that are effective in cancer therapy is a major challenge in creating cancer vaccines. Here, using an entirely unbiased approach, we queried all possible neoepitopes in a mouse cancer model and asked which of those are effective in mediating tumor rejection and, independently, in eliciting a measurable CD8 response. This analysis uncovered a large trove of effective anticancer neoepitopes that have strikingly different properties from conventional epitopes and suggested an algorithm to predict them. It also revealed that our current methods of prediction discard the overwhelming majority of true anticancer neoepitopes. These results from a single mouse model were validated in another antigenically distinct mouse cancer model and are consistent with data reported in human studies. Structural modeling showed how the MHC I-presented neoepitopes had an altered conformation, higher stability, or increased exposure to T cell receptors as compared with the unmutated counterparts. T cells elicited by the active neoepitopes identified here demonstrated a stem-like early dysfunctional phenotype associated with effective responses against viruses and tumors of transgenic mice. These abundant anticancer neoepitopes, which have not been tested in human studies thus far, can be exploited for generation of personalized human cancer vaccines.

Semi-supervised learning for somatic variant calling and peptide identification in personalized cancer immunotherapy.

BACKGROUND: Personalized cancer vaccines are emerging as one of the most promising approaches to immunotherapy of advanced cancers. However, only a small proportion of the neoepitopes generated by somatic DNA mutations in cancer cells lead to tumor rejection. Since it is impractical to experimentally assess all candidate neoepitopes prior to vaccination, developing accurate methods for predicting tumor-rejection mediating neoepitopes (TRMNs) is critical for enabling routine clinical use of cancer vaccines. RESULTS: In this paper we introduce Positive-unlabeled Learning using AuTOml (PLATO), a general semi-supervised approach to improving accuracy of model-based classifiers. PLATO generates a set of high confidence positive calls by applying a stringent filter to model-based predictions, then rescores remaining candidates by using positive-unlabeled learning. To achieve robust performance on clinical samples with large patient-to-patient variation, PLATO further integrates AutoML hyper-parameter tuning, classification threshold selection based on spies, and support for bootstrapping. CONCLUSIONS: Experimental results on real datasets demonstrate that PLATO has improved performance compared to model-based approaches for two key steps in TRMN prediction, namely somatic variant calling from exome sequencing data and peptide identification from MS/MS data.

Sympathetic nervous tone limits the development of myeloid-derived suppressor cells.

Sympathetic nerves that innervate lymphoid organs regulate immune development and function by releasing norepinephrine that is sensed by immune cells via their expression of adrenergic receptors. Here, we demonstrate that ablation of sympathetic nervous system (SNS) signaling suppresses tumor immunity, and we dissect the mechanism of such immune suppression. We report that disruption of the SNS in mice removes a critical α-adrenergic signal required for maturation of myeloid cells in normal and tumor-bearing mice. In tumor-bearing mice, disruption of the α-adrenergic signal leads to the accumulation of immature myeloid-derived suppressor cells (MDSCs) that suppress tumor immunity and promote tumor growth. Furthermore, we show that these SNS-responsive MDSCs drive expansion of regulatory T cells via secretion of the alarmin heterodimer S100A8/A9, thereby compounding their immunosuppressive activity. Our results describe a regulatory framework in which sympathetic tone controls the development of innate and adaptive immune cells and influences their activity in health and disease.

Statistical Mitogenome Assembly with RepeaTs.

By using next-generation sequencing technologies, it is possible to quickly and inexpensively generate large numbers of relatively short reads from both the nuclear and mitochondrial DNA (mtDNA) contained in a biological sample. Unfortunately, assembling such whole-genome sequencing (WGS) data with standard de novo assemblers often fails to generate high-quality mitochondrial genome sequences due to the large difference in copy number (and hence sequencing depth) between the mitochondrial and nuclear genomes. Assembly of complete mitochondrial genome sequences is further complicated by the fact that many de novo assemblers are not designed for circular genomes and by the presence of repeats in the mitochondrial genomes of some species. In this article, we describe the Statistical Mitogenome Assembly with RepeaTs (SMART) pipeline for automated assembly of mitochondrial genomes from WGS data. SMART uses an efficient coverage-based filter to first select a subset of reads enriched in mtDNA sequences. Contigs produced by an initial assembly step are filtered using the Basic Local Alignment Search Tool searches against a comprehensive mitochondrial genome database and are used as "baits" for an alignment-based filter that produces the set of reads used in a second de novo assembly and scaffolding step. In the presence of repeats, the possible paths through the assembly graph are evaluated using a maximum likelihood model. Additionally, the assembly process is repeated for a user-specified number of times on resampled subsets of reads to select for annotation of the reconstructed sequences with highest bootstrap support. Experiments on WGS data sets from a variety of species show that the SMART pipeline produces complete circular mitochondrial genome sequences with a higher success rate than current state-of-the-art tools, particularly for low-coverage WGS data sets.

Eleven grand challenges in single-cell data science.

The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands-or even millions-of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years.

Complete mitochondrial genome of the water vole, Microtus richardsoni (Cricetidae, Rodentia).

Water voles (Microtus richardsoni) are sensitive species distributed in the mountains of Canada (Alberta, British Columbia), and the United States of America (Idaho, Montana, Oregon, Utah, Washington, and Wyoming). We assembled the complete circular M. richardsoni mitogenome, which is 16,285 bp in length and encodes 13 protein-coding genes, 22 tRNA genes, and two rRNA genes. We estimated the phylogenetic tree of M. richardsoni and 24 related arvicoline species with two outgroup species: Phodopus roborovskii and Cricetus cricetus.

Mass spectrometry driven exploration reveals nuances of neoepitope-driven tumor rejection.

Neoepitopes are the only truly tumor-specific antigens. Although potential neoepitopes can be readily identified using genomics, the neoepitopes that mediate tumor rejection constitute a small minority, and there is little consensus on how to identify them. Here, for the first time, we use a combination of genomics, unbiased discovery MS immunopeptidomics and targeted MS to directly identify neoepitopes that elicit actual tumor rejection in mice. We report that MS-identified neoepitopes are an astonishingly rich source of tumor rejection mediating neoepitopes. MS has also demonstrated unambiguously the presentation by MHC I, of confirmed tumor rejection neoepitopes which bind weakly to MHC I; this was done using DCs exogenously loaded with long peptides containing the weakly binding neoepitopes. Such weakly MHC I-binding neoepitopes are routinely excluded from analysis, and our demonstration of their presentation, and their activity in tumor rejection, reveals a broader universe of tumor-rejection neoepitopes than presently imagined. Modeling studies show that a mutation in the active neoepitope alters its conformation such that its T cell receptor-facing surface is significantly altered, increasing its exposed hydrophobicity. No such changes are observed in the inactive neoepitope. These results broaden our understanding of antigen presentation and help prioritize neoepitopes for personalized cancer immunotherapy.

CD91 on dendritic cells governs immunosurveillance of nascent, emerging tumors.

The immune system detects aberrant, premalignant cells and eliminates them before the development of cancer. Immune cells, including T cells, have been shown to be critical components in eradicating these aberrant cells, and when absent in the host, incidence of cancer increases. Here, we show that CD91, a receptor expressed on antigen-presenting cells, is required for priming immune responses to nascent, emerging tumors. In the absence of CD91, effector immune responses are subdued, and tumor incidence and progression are amplified. We also show that, consequently, tumors that arise in the absence of CD91 express neo-epitopes with indices that are indicative of greater immunogenicity. Polymorphisms in human CD91 that are expected to affect ligand binding are shown to influence antitumor immune responses in cancer patients. This study presents a molecular mechanism for priming immune responses to nascent, emerging tumors that becomes a predictor of cancer susceptibility and progression.

Locality Sensitive Imputation for Single Cell RNA-Seq Data.

One of the most notable challenges in single cell RNA-Seq data analysis is the so called drop-out effect, where only a fraction of the transcriptome of each cell is captured. The random nature of dropouts, however, makes it possible to consider imputation methods as means of correcting for dropouts. In this article, we study some existing single cell RNA sequencing (scRNA-Seq) imputation methods and propose a novel iterative imputation approach based on efficiently computing highly similar cells. We then present the results of a comprehensive assessment of existing and proposed methods on real scRNA-Seq data sets with varying per cell sequencing depth.

Single cell RNA-seq data clustering using TF-IDF based methods.

BACKGROUND: Single cell transcriptomics is critical for understanding cellular heterogeneity and identification of novel cell types. Leveraging the recent advances in single cell RNA sequencing (scRNA-Seq) technology requires novel unsupervised clustering algorithms that are robust to high levels of technical and biological noise and scale to datasets of millions of cells. RESULTS: We present novel computational approaches for clustering scRNA-seq data based on the Term Frequency - Inverse Document Frequency (TF-IDF) transformation that has been successfully used in the field of text analysis. CONCLUSIONS: Empirical experimental results show that TF-IDF methods consistently outperform commonly used scRNA-Seq clustering approaches.

CD11c+ MHCIIlo GM-CSF-bone marrow-derived dendritic cells act as antigen donor cells and as antigen presenting cells in neoepitope-elicited tumor immunity against a mouse fibrosarcoma.

Dendritic cells play a critical role in initiating T-cell responses. In spite of this recognition, they have not been used widely as adjuvants, nor is the mechanism of their adjuvanticity fully understood. Here, using a mutated neoepitope of a mouse fibrosarcoma as the antigen, and tumor rejection as the end point, we show that dendritic cells but not macrophages possess superior adjuvanticity. Several types of dendritic cells, such as bone marrow-derived dendritic cells (GM-CSF cultured or FLT3-ligand induced) or monocyte-derived ones, are powerful adjuvants, although GM-CSF-cultured cells show the highest activity. Among these, the CD11c+ MHCIIlo sub-set, distinguishable by a distinct transcriptional profile including a higher expression of heat shock protein receptors CD91 and LOX1, mannose receptors and TLRs, is significantly superior to the CD11c+ MHCIIhi sub-set. Finally, dendritic cells exert their adjuvanticity by acting as both antigen donor cells (i.e., antigen reservoirs) as well as antigen presenting cells.

Fast bootstrapping-based estimation of confidence intervals of expression levels and differential expression from RNA-Seq data.

SUMMARY: This note presents IsoEM2 and IsoDE2, new versions with enhanced features and faster runtime of the IsoEM and IsoDE packages for expression level estimation and differential expression. IsoEM2 estimates fragments per kilobase million (FPKM) and transcript per million (TPM) levels for genes and isoforms with confidence intervals through bootstrapping, while IsoDE2 performs differential expression analysis using the bootstrap samples generated by IsoEM2. Both tools are available with a command line interface as well as a graphical user interface (GUI) through wrappers for the Galaxy platform. AVAILABILITY AND IMPLEMENTATION: The source code of this software suite is available at https://github.com/mandricigor/isoem2. The Galaxy wrappers are available at https://toolshed.g2.bx.psu.edu/view/saharlcc/isoem2_isode2/. CONTACT: imandric1@student.gsu.edu or ion@engr.uconn.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.