Protein G-quadruplex interactions and their effects on phase transitions and protein aggregation.

The SERF family of proteins were originally discovered for their ability to accelerate amyloid formation. Znf706 is an uncharacterized protein whose N-terminus is homologous to SERF proteins. We show here that human Znf706 can promote protein aggregation and amyloid formation. Unexpectedly, Znf706 specifically interacts with stable, non-canonical nucleic acid structures known as G-quadruplexes. G-quadruplexes can affect gene regulation and suppress protein aggregation; however, it is unknown if and how these two activities are linked. We find Znf706 binds preferentially to parallel G-quadruplexes with low micromolar affinity, primarily using its N-terminus, and upon interaction, its dynamics are constrained. G-quadruplex binding suppresses Znf706's ability to promote protein aggregation. Znf706 in conjunction with G-quadruplexes therefore may play a role in regulating protein folding. RNAseq analysis shows that Znf706 depletion specifically impacts the mRNA abundance of genes that are predicted to contain high G-quadruplex density. Our studies give insight into how proteins and G-quadruplexes interact, and how these interactions affect both partners and lead to the modulation of protein aggregation and cellular mRNA levels. These observations suggest that the SERF family of proteins, in conjunction with G-quadruplexes, may have a broader role in regulating protein folding and gene expression than previously appreciated.

Risk-based approach to setting sterile filtration microbial bioburden limits - Focus on biotech-derived products.

Holistic concepts should be applied that reduce risks prior to final bioburden testing and sterile filtration, based on enhanced process and product attribute understanding, which could be key to successful bioburden risk management. Key findings of this paper include.

Protein G-quadruplex interactions and their effects on phase transitions and protein aggregation.

The SERF family of proteins were originally discovered for their ability to accelerate amyloid formation. Znf706 is an uncharacterized protein whose N-terminus is homologous to SERF proteins. We show here that human Znf706 can promote protein aggregation and amyloid formation. Unexpectedly, Znf706 specifically interacts with stable, non-canonical nucleic acid structures known as G-quadruplexes. G-quadruplexes can affect gene regulation and suppress protein aggregation; however, it is unknown if and how these two activities are linked. We find Znf706 binds preferentially to parallel G-quadruplexes with low micromolar affinity, primarily using its N-terminus, and upon interaction, its dynamics are constrained. G-quadruplex binding suppresses Znf706's ability to promote protein aggregation. Znf706 in conjunction with G-quadruplexes therefore may play a role in regulating protein folding. RNAseq analysis shows that Znf706 depletion specifically impacts the mRNA abundance of genes that are predicted to contain high G-quadruplex density. Our studies give insight into how proteins and G-quadruplexes interact, and how these interactions affect both partners and lead to the modulation of protein aggregation and cellular mRNA levels. These observations suggest that the SERF family of proteins, in conjunction with G-quadruplexes, may have a broader role in regulating protein folding and gene expression than previously appreciated.

Modeling immunogenecity data to establish screening bioassays cut point.

The response of immunogenecity anti-drug antibody (ADA) generally includes biological and analytical variability. The nature of biological and analytical variations may lead to a variety of symmetric and asymmetric ADA data. As a result, current statistical methods may yield unreliable results because these methods assume special types of symmetric or asymmetric ADA data. In this paper, we survey and compare parametric models that are useful for analyzing a variety of asymmetric data that have rarely been used to calculate assay cut points. These models include symmetric distributions as limiting case; therefore, they are useful in the analysis of a variety of symmetric data. We also investigate two nonparametric approaches that have received little attention in screening cut point calculations. A simulation study was conducted to compare the performance of the methods. We evaluate the methods using four published different types of data, and make recommendations concerning the use of the methods.

The RNA-binding proteins hnRNP H and F regulate splicing of a MYC-dependent HRAS exon in prostate cancer cells.

The MYC proto-oncogene contributes to the pathogenesis of more than half of human cancers. Malignant transformation by MYC transcriptionally up-regulates the core pre-mRNA splicing machinery and causes misregulation of alternative splicing. However, our understanding of how splicing changes are directed by MYC is limited. We performed a signaling pathway-guided splicing analysis to identify MYC-dependent splicing events. These included an HRAS cassette exon repressed by MYC across multiple tumor types. To molecularly dissect the regulation of this HRAS exon, we used antisense oligonucleotide tiling to identify splicing enhancers and silencers in its flanking introns. RNA-binding motif prediction indicated multiple binding sites for hnRNP H and hnRNP F within these cis-regulatory elements. Using siRNA knockdown and cDNA expression, we found that both hnRNP H and F activate the HRAS cassette exon. Mutagenesis and targeted RNA immunoprecipitation implicate two downstream G-rich elements in this splicing activation. Analyses of ENCODE RNA-seq datasets confirmed hnRNP H regulation of HRAS splicing. Analyses of RNA-seq datasets across multiple cancers showed a negative correlation of HNRNPH gene expression with MYC hallmark enrichment, consistent with the effect of hnRNP H on HRAS splicing. Interestingly, HNRNPF expression showed a positive correlation with MYC hallmarks and thus was not consistent with the observed effects of hnRNP F. Loss of hnRNP H/F altered cell cycle progression and induced apoptosis in the PC3 prostate cancer cell line. Collectively, our results reveal mechanisms for MYC-dependent regulation of splicing and point to possible therapeutic targets in prostate cancers.

IRIS: Discovery of cancer immunotherapy targets arising from pre-mRNA alternative splicing.

Alternative splicing (AS) is prevalent in cancer, generating an extensive but largely unexplored repertoire of novel immunotherapy targets. We describe Isoform peptides from RNA splicing for Immunotherapy target Screening (IRIS), a computational platform capable of discovering AS-derived tumor antigens (TAs) for T cell receptor (TCR) and chimeric antigen receptor T cell (CAR-T) therapies. IRIS leverages large-scale tumor and normal transcriptome data and incorporates multiple screening approaches to discover AS-derived TAs with tumor-associated or tumor-specific expression. In a proof-of-concept analysis integrating transcriptomics and immunopeptidomics data, we showed that hundreds of IRIS-predicted TCR targets are presented by human leukocyte antigen (HLA) molecules. We applied IRIS to RNA-seq data of neuroendocrine prostate cancer (NEPC). From 2,939 NEPC-associated AS events, IRIS predicted 1,651 epitopes from 808 events as potential TCR targets for two common HLA types (A*02:01 and A*03:01). A more stringent screening test prioritized 48 epitopes from 20 events with "neoantigen-like" NEPC-specific expression. Predicted epitopes are often encoded by microexons of ≤30 nucleotides. To validate the immunogenicity and T cell recognition of IRIS-predicted TCR epitopes, we performed in vitro T cell priming in combination with single-cell TCR sequencing. Seven TCRs transduced into human peripheral blood mononuclear cells (PBMCs) showed high activity against individual IRIS-predicted epitopes, providing strong evidence of isolated TCRs reactive to AS-derived peptides. One selected TCR showed efficient cytotoxicity against target cells expressing the target peptide. Our study illustrates the contribution of AS to the TA repertoire of cancer cells and demonstrates the utility of IRIS for discovering AS-derived TAs and expanding cancer immunotherapies.

RNA-seq data science: From raw data to effective interpretation.

RNA sequencing (RNA-seq) has become an exemplary technology in modern biology and clinical science. Its immense popularity is due in large part to the continuous efforts of the bioinformatics community to develop accurate and scalable computational tools to analyze the enormous amounts of transcriptomic data that it produces. RNA-seq analysis enables genes and their corresponding transcripts to be probed for a variety of purposes, such as detecting novel exons or whole transcripts, assessing expression of genes and alternative transcripts, and studying alternative splicing structure. It can be a challenge, however, to obtain meaningful biological signals from raw RNA-seq data because of the enormous scale of the data as well as the inherent limitations of different sequencing technologies, such as amplification bias or biases of library preparation. The need to overcome these technical challenges has pushed the rapid development of novel computational tools, which have evolved and diversified in accordance with technological advancements, leading to the current myriad of RNA-seq tools. These tools, combined with the diverse computational skill sets of biomedical researchers, help to unlock the full potential of RNA-seq. The purpose of this review is to explain basic concepts in the computational analysis of RNA-seq data and define discipline-specific jargon.

Technology dictates algorithms: recent developments in read alignment.

Aligning sequencing reads onto a reference is an essential step of the majority of genomic analysis pipelines. Computational algorithms for read alignment have evolved in accordance with technological advances, leading to today's diverse array of alignment methods. We provide a systematic survey of algorithmic foundations and methodologies across 107 alignment methods, for both short and long reads. We provide a rigorous experimental evaluation of 11 read aligners to demonstrate the effect of these underlying algorithms on speed and efficiency of read alignment. We discuss how general alignment algorithms have been tailored to the specific needs of various domains in biology.

Author Correction: Profiling immunoglobulin repertoires across multiple human tissues using RNA sequencing.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

A new method for identification of outliers in immunogenicity assay cut point data.

The cut point is an important parameter for immunogenicity assay validation and critical to immunogenicity assessment in clinical trials. FDA (2019) recommends using a statistical approach to derive cut point, with an appropriate outlier removal procedure. In general, the industry follows the methods described in Shankar et al. (2008) and Zhang et al. (2013) among others to determine cut point. Outlier removal is a necessary step during the cut point determination exercise to reduce potential false negative classifications. However, the widely used statistical outlier removal method, namely, Tukey's box-plot method (1.5 times inter-quartile range, IQR), is often found to be overly conservative in the sense that it removes too many "outliers". Tukey's box-plot method can be used to flag potential outliers for further investigation, however, it is not a hypothesis testing based statistical method. Removing these suspected "outliers" will lead to lower cut point which might confound immunogenicity assessment due to the presence of many low false positives. Besides, the very nature of assay analytical variability has a non-negligible adverse impact on the reliability of ADA classification in terms of false positive and false negative, demanding as large as possible contribution from biological variability relative to analytical variability. A new outlier removal procedure, which takes into account the relative magnitude between biological variability and analytical variability within the sample population, is proposed and statistically justified. After sequential removal of analytical and biological outliers, a 5% false positive rate and 1% false positive rate in screening and confirmatory assays, respectively, are still targeted without increasing potential false negatives. Internal data shows that this practice has minimal impact on assay sensitivity and has the advantage of selecting true positive samples. It is shown that the new procedure is more appropriate for cut point determination.

Regional Variation of Splicing QTLs in Human Brain.

A major question in human genetics is how sequence variants of broadly expressed genes produce tissue- and cell type-specific molecular phenotypes. Genetic variation of alternative splicing is a prevalent source of transcriptomic and proteomic diversity in human populations. We investigated splicing quantitative trait loci (sQTLs) in 1,209 samples from 13 human brain regions, using RNA sequencing (RNA-seq) and genotype data from the Genotype-Tissue Expression (GTEx) project. Hundreds of sQTLs were identified in each brain region. Some sQTLs were shared across brain regions, whereas others displayed regional specificity. These "regionally ubiquitous" and "regionally specific" sQTLs showed distinct positional distributions of single-nucleotide polymorphisms (SNPs) within and outside essential splice sites, respectively, suggesting their regulation by distinct molecular mechanisms. Integrating the binding motifs and expression patterns of RNA binding proteins with exon splicing profiles, we uncovered likely causal variants underlying brain region-specific sQTLs. Notably, SNP rs17651213 created a putative binding site for the splicing factor RBFOX2 and was associated with increased splicing of MAPT exon 3 in cerebellar tissues, where RBFOX2 was highly expressed. Overall, our study reveals a more comprehensive spectrum and regional variation of sQTLs in human brain and demonstrates that such regional variation can be used to fine map potential causal variants of sQTLs and their associated neurological diseases.

Profiling immunoglobulin repertoires across multiple human tissues using RNA sequencing.

Profiling immunoglobulin (Ig) receptor repertoires with specialized assays can be cost-ineffective and time-consuming. Here we report ImReP, a computational method for rapid and accurate profiling of the Ig repertoire, including the complementary-determining region 3 (CDR3), using regular RNA sequencing data such as those from 8,555 samples across 53 tissues types from 544 individuals in the Genotype-Tissue Expression (GTEx v6) project. Using ImReP and GTEx v6 data, we generate a collection of 3.6 million Ig sequences, termed the atlas of immunoglobulin repertoires (TAIR), across a broad range of tissue types that often do not have reported Ig repertoires information. Moreover, the flow of Ig clonotypes and inter-tissue repertoire similarities across immune-related tissues are also evaluated. In summary, TAIR is one of the largest collections of CDR3 sequences and tissue types, and should serve as an important resource for studying immunological diseases.

Benchmarking of computational error-correction methods for next-generation sequencing data.

BACKGROUND: Recent advancements in next-generation sequencing have rapidly improved our ability to study genomic material at an unprecedented scale. Despite substantial improvements in sequencing technologies, errors present in the data still risk confounding downstream analysis and limiting the applicability of sequencing technologies in clinical tools. Computational error correction promises to eliminate sequencing errors, but the relative accuracy of error correction algorithms remains unknown. RESULTS: In this paper, we evaluate the ability of error correction algorithms to fix errors across different types of datasets that contain various levels of heterogeneity. We highlight the advantages and limitations of computational error correction techniques across different domains of biology, including immunogenomics and virology. To demonstrate the efficacy of our technique, we apply the UMI-based high-fidelity sequencing protocol to eliminate sequencing errors from both simulated data and the raw reads. We then perform a realistic evaluation of error-correction methods. CONCLUSIONS: In terms of accuracy, we find that method performance varies substantially across different types of datasets with no single method performing best on all types of examined data. Finally, we also identify the techniques that offer a good balance between precision and sensitivity.

Pathway-guided analysis identifies Myc-dependent alternative pre-mRNA splicing in aggressive prostate cancers.

We sought to define the landscape of alternative pre-mRNA splicing in prostate cancers and the relationship of exon choice to known cancer driver alterations. To do so, we compiled a metadataset composed of 876 RNA-sequencing (RNA-Seq) samples from five publicly available sources representing a range of prostate phenotypes from normal tissue to drug-resistant metastases. We subjected these samples to exon-level analysis with rMATS-turbo, purpose-built software designed for large-scale analyses of splicing, and identified 13,149 high-confidence cassette exon events with variable incorporation across samples. We then developed a computational framework, pathway enrichment-guided activity study of alternative splicing (PEGASAS), to correlate transcriptional signatures of 50 different cancer driver pathways with these alternative splicing events. We discovered that Myc signaling was correlated with incorporation of a set of 1,039 cassette exons enriched in genes encoding RNA binding proteins. Using a human prostate epithelial transformation assay, we confirmed the Myc regulation of 147 of these exons, many of which introduced frameshifts or encoded premature stop codons. Our results connect changes in alternative pre-mRNA splicing to oncogenic alterations common in prostate and many other cancers. We also establish a role for Myc in regulating RNA splicing by controlling the incorporation of nonsense-mediated decay-determinant exons in genes encoding RNA binding proteins.

Comparisons of outlier tests for potency bioassays.

Potency bioassays are used to measure biological activity. Consequently, potency is considered a critical quality attribute in manufacturing. Relative potency is measured by comparing the concentration-response curves of a manufactured test batch with that of a reference standard. If the curve shapes are deemed similar, the test batch is said to exhibit constant relative potency with the reference standard, a critical requirement for calibrating the potency of the final drug product. Outliers in bioassay potency data may result in the false acceptance/rejection of a bad/good sample and, if accepted, may yield a biased relative potency estimate. To avoid these issues, the USP<1032> recommends the screening of bioassay data for outliers prior to performing a relative potency analysis. In a recently published work, the effects of one or more outliers, outlier size, and outlier type on similarity testing and estimation of relative potency were thoroughly examined, confirming the USP<1032> outlier guidance. As a follow-up, several outlier detection methods, including those proposed by the USP<1010>, are evaluated and compared in this work through computer simulation. Two novel outlier detection methods are also proposed. The effects of outlier removal on similarity testing and estimation of relative potency were evaluated, resulting in recommendations for best practice.

A note on the determination of non-inferiority margins with application in oncology clinical trials.

The goal of a non-inferiority trial is to evaluate whether the effect of an experimental treatment is not inferior to that of the active control. Determination of an appropriate non-inferiority margin is critical to the demonstration of non-inferiority. A commonly used method is called the fixed-margin approach recommended by the FDA. The fixed-margin approach consists of two steps: first the lower limit of the 1 - α * two-sided confidence interval (CI) of the active-control effect versus placebo is calculated from relevant historical trials or meta-analysis; second, the non-inferiority margin is obtained as a fraction of the lower confidence limit of the control effect to preserve partial control effect. An alternative method is to use the point estimate, instead of the lower confidence limit, of the active-control effect. The fixed-margin approach based on the lower limit may be ultra-conservative with unconditional Type 1 error rate much smaller than target α / 2 level, while the margin based on the point estimate is liberal. We derive the Type 1 error rate as a function of variances of the effect estimates in the historical and the current non-inferiority trials. We also propose an alternative approach for the non-inferiority margin that maintains the target Type 1 error rate. For the endpoint of landmark survival, we conduct simulations to compare the fixed-margin methods and the proposed method. For illustration, we apply the proposed method to an oncology non-inferiority clinical trial to determine an alternative non-inferiority margin.

Detecting bliss synergy in in vivo combination studies with a tumor kinetic model.

Linear models are generally reliable methods for analyzing tumor growth in vivo, with drug effectiveness being represented by the steepness of the regression slope. With immunotherapy, however, not all tumor growth follows a linear pattern, even after log transformation. Tumor kinetics models are mechanistic models that describe tumor proliferation and tumor killing macroscopically, through a set of differential equations. In drug combination studies, although an additional drug-drug interaction term can be added to such models, however, the drug interactions suggested by tumor kinetics models cannot be translated directly into synergistic effects. We have developed a novel statistical approach that simultaneously models tumor growth in control, monotherapy, and combination therapy groups. This approach makes it possible to test for synergistic effects directly and to compare such effects among different studies.

A fast and reliable test for parallelism in bioassay.

Parallelism in bioassay is a synonym of similarity between two concentration-response curves. Before the determination of relative potency in bioassays, it is necessary to test for and claim parallelism between the pair of concentration-response curves of reference standard and test sample. Methods for parallelism testing include p-value-based significance tests and interval-based equivalence tests. Most of the latter approaches make statistical inference about the equivalence of parameters of the concentration-response curve models. An apparent drawback of such methods is that equivalence in model parameters does not guarantee similarity between the reference and test sample. In contrast, a Bayesian method was recently proposed that directly tests the parallelism hypothesis that the concentration-response curve of the test sample is a horizontal shift of that of the reference. In other words, the testing sample is a dilution or concentration of the reference standard. The Bayesian approach is shown to protect against type I error and provides sufficient statistical power for parallelism testing. In practice, however, it is challenging to implement the method as it requires both specialized Bayesian software and a relatively long run time. In this paper, we propose a frequentist version of the test with split-second run time. The empirical properties of the frequentist parallelism test method are evaluated and compared with the original Bayesian method. It is demonstrated that the frequentist method is both fast and reliable for parallelism testing for a variety of concentration-response models.

Excessive outlier removal may result in cut points that are not suitable for immunogenicity assessments.

Cut point determination is an important aspect of immunogenicity assay development. The cut point can be influenced by a myriad of factors. Key among those is the analytical variability of the assay itself and biological variation due to test samples. Since a smaller cut point value may result in improved sensitivity, the existing procedures often employ statistical techniques such as outlier removal to produce a conservative cut point. Although such practices are intended to yield acceptable assay sensitivity, they may fail to fully account for biological variability in the data, thus generating higher than expected number of false positive results. In this paper, we introduce the concept of minimum cut point. It is defined as the cut point that is determined in the absence of biological variability. Under the log-normal assumption of the data used for cut point analysis, closed-form formulas are derived for the minimum cut point. This minimum cut point can be used to benchmark whether a cut point derived from a procedure can compromise assay specificity by being too low.

Effect of a statistical outlier in potency bioassays.

The USP<1032> guidelines recommend the screening of bioassay data for outliers prior to performing a relative potency (RP) analysis. The guidelines, however, do not offer advice on the size or type of outlier that should be removed prior to model fitting and calculation of RP. Computer simulation was used to investigate the consequences of ignoring the USP<1032> guidance to remove outliers. For biotherapeutics and vaccines, outliers in potency data may result in the false acceptance/rejection of a bad/good lot of drug product. Biological activity, measured through a potency bioassay, is considered a critical quality attribute in manufacturing. If the concentration-response potency curve of a test sample is deemed to be similar in shape to that of the reference standard, the curves are said to exhibit constant RP, an essential criterion for the interpretation of a RP. One or more outliers in the concentration-response data, however, may result in a failure to declare similarity or may yield a biased RP estimate. Concentration-response curves for test and reference were computer generated with constant RP from four-parameter logistic curves. Single outlier, multiple outlier, and whole-curve outlier scenarios were explored for their effects on the similarity testing and on the RP estimation. Though the simulations point to situations for which outlier removal is unnecessary, the results generally support the USP<1032> recommendation and illustrate the impact on the RP calculation when application of outlier removal procedures are discounted.