Allele-specific CRISPR-Cas9 editing of dominant epidermolysis bullosa simplex in human epidermal stem cells

Epidermolysis bullosa simplex (EBS) is a rare skin disease inherited mostly in an autosomal dominant manner. Patients display a skin fragility that leads to blisters and erosions caused by minor mechanical trauma. EBS phenotypic and genotypic variants are caused by genetic defects in intracellular proteins whose function is to provide the attachment of basal keratinocytes to the basement membrane zone and most EBS cases display mutations in keratin 5 (KRT5) and keratin 14 (KRT14) genes. Besides palliative treatments, there is still no long-lasting effective cure to correct the mutant gene and abolish the dominant negative effect of the pathogenic protein over its wild-type counterpart. Here, we propose a molecular strategy for EBS01 patient's keratinocytes carrying a monoallelic c.475/495del21 mutation in KRT14 exon 1. Through the CRISPR-Cas9 system, we perform a specific cleavage only on the mutant allele and restore a normal cellular phenotype and a correct intermediate filament network, without affecting the epidermal stem cell, referred to as holoclones, which play a crucial role in epidermal regeneration.

The signaling function of IDO1 incites the malignant progression of mouse B16 melanoma

Indoleamine 2,3 dioxygenase 1 (IDO1), a leader tryptophan-degrading enzyme, represents a recognized immune checkpoint molecule. In neoplasia, IDO1 is often highly expressed in dendritic cells infiltrating the tumor and/or in tumor cells themselves, particularly in human melanoma. In dendritic cells, IDO1 does not merely metabolize tryptophan into kynurenine but, after phosphorylation of critical tyrosine residues in the non-catalytic small domain, it triggers a signaling pathway prolonging its immunoregulatory effects by a feed-forward mechanism. We here investigated whether the non-enzymatic function of IDO1 could also play a role in tumor cells by using B16-F10 mouse melanoma cells transfected with either the wild-type Ido1 gene (Ido1^WT ) or a mutated variant lacking the catalytic, but not signaling activity (Ido1^H350A ). As compared to the Ido1^WT -transfected counterpart (B16^WT), B16-F10 cells expressing Ido1^H350A (B16^H350A) were characterized by an in vitro accelerated growth mediated by increased Ras and Erk activities. Faster growth and malignant progression of B16^H350A cells, also detectable in vivo, were found to be accompanied by a reduction in tumor-infiltrating CD8⁺ T cells and an increase in Foxp3⁺ regulatory T cells. Our data, therefore, suggest that the IDO1 signaling function can also occur in tumor cells and that alternative therapeutic approach strategies should be undertaken to effectively tackle this important immune checkpoint molecule.

A novel RNA aptamer identifies plasma membrane ATP synthase beta subunit as an early marker and therapeutic target in aggressive cancer

PURPOSE

Primary breast and prostate cancers can be cured, but metastatic disease cannot. Identifying cell factors that predict metastatic potential could guide both prognosis and treatment.

METHODS

We used Cell-SELEX to screen an RNA aptamer library for differential binding to prostate cancer cell lines with high vs. low metastatic potential. Mass spectroscopy, immunoblot, and immunohistochemistry were used to identify and validate aptamer targets. Aptamer properties were tested in vitro, in xenograft models, and in clinical biopsies. Gene expression datasets were queried for target associations in cancer.

RESULTS

We identified a novel aptamer (Apt63) that binds to the beta subunit of F1Fo ATP synthase (ATP5B), present on the plasma membrane of certain normal and cancer cells. Apt63 bound to plasma membranes of multiple aggressive breast and prostate cell lines, but not to normal breast and prostate epithelial cells, and weakly or not at all to non-metastasizing cancer cells; binding led to rapid cell death. A single intravenous injection of Apt63 induced rapid, tumor cell-selective binding and cytotoxicity in MDA-MB-231 xenograft tumors, associated with endonuclease G nuclear translocation and DNA fragmentation. Apt63 was not toxic to non-transformed epithelial cells in vitro or adjacent normal tissue in vivo. In breast cancer tissue arrays, plasma membrane staining with Apt63 correlated with tumor stage (p < 0.0001, n = 416) and was independent of other cancer markers. Across multiple datasets, ATP5B expression was significantly increased relative to normal tissue, and negatively correlated with metastasis-free (p = 0.0063, 0.00039, respectively) and overall (p = 0.050, 0.0198) survival.

CONCLUSION

Ecto-ATP5B binding by Apt63 may disrupt an essential survival mechanism in a subset of tumors with high metastatic potential, and defines a novel category of cancers with potential vulnerability to ATP5B-targeted therapy. Apt63 is a unique tool for elucidating the function of surface ATP synthase, and potentially for predicting and treating metastatic breast and prostate cancer.

Marker Identification and Classification of Cancer Types Using Gene Expression Data and SIMCA

Objectives: High-throughput technologies are radically boosting the understanding of living systems, thus creating enormous opportunities to elucidate the biological processes of cells in different physiological states. In particular, the application of DNA micro-arrays to monitor expression profiles from tumor cells is improving cancer analysis to levels that classical methods have been unable to reach. However, molecular diagnostics based on expression profiling requires addressing computational issues as the overwhelming number of variables and the complex, multi-class nature of tumor samples. Thus, the objective of the present research has been the development of a computational procedure for feature extraction and classification of gene expression data.

Methods: The Soft Independent Modeling of Class Analogy (SIMCA) approach has been implemented in a data mining scheme, which allows the identification of those genes that are most likely to confer robust and accurate classification of samples from multiple tumor types.

Results: The proposed method has been tested on two different microarray data sets, namely Golub’s analysis of acute human leukemia [1] and the small round blue cell tumors study presented by Khan et al. [2]. The identified features represent a rational and dimensionally reduced base for understanding the biology of diseases, defining targets of therapeutic intervention, and developing diagnostic tools for classification of pathological states.

Conclusions: The analysis of the SIMCA model residuals allows the identification of specific phenotype markers. At the same time, the class analogy approach provides the assignment to multiple classes, such as different pathological conditions or tissue samples, for previously unseen instances.

Abstract P6-01-05: Novel cytotoxic RNA aptamers that distinguish between metastasis-prone and indolent breast and prostate cancers

Background: Prostate and breast cancers are, respectively, the most common malignancies diagnosed in men and women worldwide. These cancers develop in different organs but have significant biological similarities: both are typically hormone-dependent, and both require early detection and treatment, as metastatic disease is incurable. At the same time, early stage tumors are often over-treated. Better markers for tumor aggressiveness would help to optimize treatment strategies in both breast and prostate cancer.

Objective: Develop high affinity nucleic acid oligomers (aptamers) that can distinguish between indolent tumors that will remain organ-confined and those with heightened potential to metastasize.

Methods: We performed subtractive RNA Cell-SELEX to select for surface ligands specific to aggressive tumors, using as a positive selector the highly metastasis-competent LN3 subclone of prostate cancer cell line LNCaP, and as negative selectors parental LNCaP and a non-metastasizing subclone, Pro5. . The RNA aptamer pool was PCR amplified from a 40-mer random nucleotide cDNA library with appropriate flanking sequences, and transcribed in vitro. After 11 SELEX cycles, aptamer pools from cycles 0, 4, 9, and 11 were subjected to high-throughput sequencing. Eight aptamers, representing 4 sequence families, were chosen for further study. Representative relevant and irrelevant aptamers were labeled with Cy3 and used to stain LNCaP-LN3 and LNCaP-Pro5 in culture and as xenografts in NOD-SCID-gamma mice. Additional cell and tumor lines from both breast and prostate cancer were used for validation.

Results: Two aptamers bound avidly to the surface of the aggressive LNCaP-LN3 subclone, both in culture and in fixed xenograft tumors, but not to the indolent Pro5 subclone. Aptamer binding led to rapid and specific cytotoxicity in vitro but had no effect on other cell lines to which the aptamer did not bind. The same aptamers showed similar high specificity for multiple other metastasis-competent cancer cells, including the prostate adenocarcinoma PC-3 and PC-3ML subclones, breast cancer cell lines MDA-MB436 and MDA-MB231, and the primary dissociated breast tumor DT28 , while exhibiting no detectable binding to the non-metastasizing MCF-7 breast cancer cell line and DT22 primary dissociated breast tumor cells, and the non-tumorigenic prostate epithelial cell line RPWE-1.

Conclusion: We identified RNA aptamers that specifically bind to metastasis-prone prostate cancer and breast cancer cell surface targets, and exert cell-specific toxicity dependent upon aptamer binding. While the target(s) remain to be identified, we propose that these aptamers may discriminate between progressive and indolent breast and prostate cancers, and may have substantial promise as anticancer agents either alone or suitably liganded to toxic moieties.

Citation Format: Bishopric NH, Speransky S, Serafini P, De la Fuente AC, Bicciato S, El-Ashry D, Lippman ME. Novel cytotoxic RNA aptamers that distinguish between metastasis-prone and indolent breast and prostate cancers [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P6-01-05.

lncRNA profiling in early-stage chronic lymphocytic leukemia identifies transcriptional fingerprints with relevance in clinical outcome

Long non-coding RNAs (lncRNAs) represent a novel class of functional RNA molecules with an important emerging role in cancer. To elucidate their potential pathogenetic role in chronic lymphocytic leukemia (CLL), a biologically and clinically heterogeneous neoplasia, we investigated lncRNAs expression in a prospective series of 217 early-stage Binet A CLL patients and 26 different subpopulations of normal B-cells, through a custom annotation pipeline of microarray data. Our study identified a 24-lncRNA-signature specifically deregulated in CLL compared with the normal B-cell counterpart. Importantly, this classifier was validated on an independent data set of CLL samples. Belonging to the lncRNA signature characterizing distinct molecular CLL subgroups, we identified lncRNAs recurrently associated with adverse prognostic markers, such as unmutated IGHV status, CD38 expression, 11q and 17p deletions, and NOTCH1 mutations. In addition, correlation analyses predicted a putative lncRNAs interplay with genes and miRNAs expression. Finally, we generated a 2-lncRNA independent risk model, based on lnc-IRF2-3 and lnc-KIAA1755-4 expression, able to distinguish three different prognostic groups in our series of early-stage patients. Overall, our study provides an important resource for future studies on the functions of lncRNAs in CLL, and contributes to the discovery of novel molecular markers with clinical relevance associated with the disease.

APTANI: a computational tool to select aptamers through sequence-structure motif analysis of HT-SELEX data

MOTIVATION

Aptamers are synthetic nucleic acid molecules that can bind biological targets in virtue of both their sequence and three-dimensional structure. Aptamers are selected using SELEX, Systematic Evolution of Ligands by EXponential enrichment, a technique that exploits aptamer-target binding affinity. The SELEX procedure, coupled with high-throughput sequencing (HT-SELEX), creates billions of random sequences capable of binding different epitopes on specific targets. Since this technique produces enormous amounts of data, computational analysis represents a critical step to screen and select the most biologically relevant sequences.

RESULTS

Here, we present APTANI, a computational tool to identify target-specific aptamers from HT-SELEX data and secondary structure information. APTANI builds on AptaMotif algorithm, originally implemented to analyze SELEX data; extends the applicability of AptaMotif to HT-SELEX data and introduces new functionalities, as the possibility to identify binding motifs, to cluster aptamer families or to compare output results from different HT-SELEX cycles. Tabular and graphical representations facilitate the downstream biological interpretation of results.

AVAILABILITY AND IMPLEMENTATION

APTANI is available at http://aptani.unimore.it.

CONTACT

silvio.bicciato@unimore.it

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Integrated analysis of microRNA and mRNA expression profiles in physiological myelopoiesis: role of hsa-mir-299-5p in CD34+ progenitor cells commitment

Hematopoiesis entails a series of hierarchically organized events that proceed throughout cell specification and terminates with cell differentiation. Commitment needs the transcription factors' effort, which, in concert with microRNAs, drives cell fate and responds to promiscuous patterns of gene expression by turning on lineage-specific genes and repressing alternate lineage transcripts. We obtained microRNA profiles from human CD34+ hematopoietic progenitor cells and in vitro differentiated erythroblasts, megakaryoblasts, monoblasts and myeloblast precursors that we analyzed together with their gene expression profiles. The integrated analysis of microRNA-mRNA expression levels highlighted an inverse correlation between microRNAs specifically upregulated in one single-cell progeny and their putative target genes, which resulted in downregulation. Among the upregulated lineage-enriched microRNAs, hsa-miR-299-5p emerged as having a role in controlling CD34+ progenitor fate, grown in multilineage culture conditions. Gain- and loss-of-function experiments revealed that hsa-miR-299-5p participates in the regulation of hematopoietic progenitor fate, modulating megakaryocytic-granulocytic versus erythroid-monocytic differentiation.

Transcriptional profiles in melanocytes from clinically unaffected skin distinguish the neoplastic growth pattern in patients with melanoma

BACKGROUND

It is generally accepted that sunlight may contribute to the development of melanoma.

OBJECTIVES

To analyse gene expression of melanocytes obtained from clinically unaffected skin of patients with melanoma and healthy controls before and after exposure to ultraviolet B radiation.

METHODS

Using GeneChip array technology, the gene expression of melanocytes obtained from the two donor groups was profiled, in order to identify transcriptional differences affecting susceptibility to melanoma.

RESULTS

The data collected did not show any difference between the expression profiles of melanocytes purified from normal donors and from patients with melanoma that was able to give a statistically significant class separation. However, by means of unsupervised clustering our data could be divided into two main classes. The first class included the transcriptome profiles of melanocytes obtained from skin samples of patients with a vertical growth phase (VGP) melanoma, while the second class included the transcriptome profiles of melanocytes obtained from skin samples of patients with a radial growth phase (RGP) melanoma.

CONCLUSIONS

These data suggest that melanocytes in patients with VGP and RGP melanomas show significant differences in gene expression profiles, which allow us to classify patients with melanoma also from clinically unaffected skin.

A locally adaptive statistical procedure (LAP) to identify differentially expressed chromosomal regions

MOTIVATION

The systematic integration of expression profiles and other types of gene information, such as chromosomal localization, ontological annotations and sequence characteristics, still represents a challenge in the gene expression arena. In particular, the analysis of transcriptional data in context of the physical location of genes in a genome appears promising in detecting chromosomal regions with transcriptional imbalances often characterizing cancer.

RESULTS

A computational tool named locally adaptive statistical procedure (LAP), which incorporates transcriptional data and structural information for the identification of differentially expressed chromosomal regions, is described. LAP accounts for variations in the distance between genes and in gene density by smoothing standard statistics on gene position before testing the significance of their differential levels of gene expression. The procedure smooths parameters and computes p-values locally to account for the complex structure of the genome and to more precisely estimate the differential expression of chromosomal regions. The application of LAP to three independent sets of raw expression data allowed identifying differentially expressed regions that are directly involved in known chromosomal aberrations characteristic of tumors.

AVAILABILITY

Functions in R for implementing the LAP method are available at http://www.dpci.unipd.it/Bioeng/Publications/LAP.htm

Identification of a molecular signature predictive of sensitivity to differentiation induction in acute myeloid leukemia

Acute myeloid leukemia (AML) blasts are immature committed myeloid cells unable to spontaneously undergo terminal maturation, and characterized by heterogeneous sensitivity to natural differentiation inducers. Here, we show a molecular signature predicting the resistance or sensitivity of six myeloid cell lines to differentiation induced in vitro with retinoic acid or vitamin D. The identified signature was further validated by TaqMan assay for the prediction of response to an in vitro differentiation assay performed on 28 freshly isolated AML blast populations. The TaqMan assay successfully predicts the in vitro resistance or responsiveness of AML blasts to differentiation inducers. Furthermore, performing a meta-analysis of publicly available microarray data sets, we also show the accuracy of our prediction on known phenotypes and suggest that our signature could become useful for the identification of patients eligible for new therapeutic strategies.

Algorithm for automatic genotype calling of single nucleotide polymorphisms using the full course of TaqMan real-time data

Single nucleotide polymorphisms (SNPs) are often determined using TaqMan real-time PCR assays (Applied Biosystems) and commercial software that assigns genotypes based on reporter probe signals at the end of amplification. Limitations to the large-scale application of this approach include the need for positive controls or operator intervention to set signal thresholds when one allele is rare. In the interest of optimizing real-time PCR genotyping, we developed an algorithm for automatic genotype calling based on the full course of real-time PCR data. Best cycle genotyping algorithm (BCGA), written in the open source language R, is based on the assumptions that classification depends on the time (cycle) of amplification and that it is possible to identify a best discriminating cycle for each SNP assay. The algorithm is unique in that it classifies samples according to the behavior of blanks (no DNA samples), which cluster with heterozygous samples. This method of classification eliminates the need for positive controls and permits accurate genotyping even in the absence of a genotype class, for example when one allele is rare. Here, we describe the algorithm and test its validity, compared to the standard end-point method and to DNA sequencing.

Marker identification and classification of cancer types using gene expression data and SIMCA

OBJECTIVES

High-throughput technologies are radically boosting the understanding of living systems, thus creating enormous opportunities to elucidate the biological processes of cells in different physiological states. In particular, the application of DNA micro-arrays to monitor expression profiles from tumor cells is improving cancer analysis to levels that classical methods have been unable to reach. However, molecular diagnostics based on expression profiling requires addressing computational issues as the overwhelming number of variables and the complex, multi-class nature of tumor samples. Thus, the objective of the present research has been the development of a computational procedure for feature extraction and classification of gene expression data.

METHODS

The Soft Independent Modeling of Class Analogy (SIMCA) approach has been implemented in a data mining scheme, which allows the identification of those genes that are most likely to confer robust and accurate classification of samples from multiple tumor types.

RESULTS

The proposed method has been tested on two different microarray data sets, namely Golub's analysis of acute human leukemia and the small round blue cell tumors study presented by Khan et al.. The identified features represent a rational and dimensionally reduced base for understanding the biology of diseases, defining targets of therapeutic intervention, and developing diagnostic tools for classification of pathological states.

CONCLUSIONS

The analysis of the SIMCA model residuals allows the identification of specific phenotype markers. At the same time, the class analogy approach provides the assignment to multiple classes, such as different pathological conditions or tissue samples, for previously unseen instances.

Computational analysis of flow-cytometry antigen expression profiles in childhood acute lymphoblastic leukemia: an MLL/AF4 identification

Precursor B-acute lymphoblastic leukemia (pB-ALL) is a heterogeneous disease and multiparameter flow cytometry, molecular genetics, and cytogenetic studies have all contributed to classification of subgroups with prognostic significance. Recently, gene expression microarray technology has been used to investigate lymphoblastic leukemias, demonstrating that known and novel pB-ALL subclasses can be separated on the basis of gene expression profiles. The strength of microarray technique lays in part in the multivariate nature of the expression data. We propose a parallel multiparametric approach based on immunophenotypic flow-cytometry expression data for the analysis of leukemia patients. Specifically, we tested the potential of this approach on a data set of 145 samples of pediatric pB-ALL that included 46 samples positive for mixed lineage leukemia (MLL) translocations (MLL+) and 99 control pB-ALLs, negative for this translocation (MLL-). The expression levels of 16 marker proteins have been monitored by four-color flow cytometry using a standardized diagnostic panel of antibodies. The protein expression database has been then analyzed using those univariate and multivariate computational techniques normally applied to mine and model large microarray data sets. Marker protein expression profiling not only allowed separating pB-ALL cases with an MLL rearrangement from other ALLs, but also demonstrates that MLL+ leukemias constitute a heterogeneous group in which MLL/AF4 leukemias represent a homogenous subclass described by a specific expression fingerprint.

PCA disjoint models for multiclass cancer analysis using gene expression data

MOTIVATION

Microarray expression profiling appears particularly promising for a deeper understanding of cancer biology and to identify molecular signatures supporting the histological classification schemes of neoplastic specimens. However, molecular diagnostics based on microarray data presents major challenges due to the overwhelming number of variables and the complex, multiclass nature of tumor samples. Thus, the development of marker selection methods, that allow the identification of those genes that are most likely to confer high classification accuracy of multiple tumor types, and of multiclass classification schemes is of paramount importance.

RESULTS

A computational procedure for marker identification and for classification of multiclass gene expression data through the application of disjoint principal component models is described. The identified features represent a rational and dimensionally reduced base for understanding the basic biology of diseases, defining targets for therapeutic intervention, and developing diagnostic tools for the identification and classification of multiple pathological states. The method has been tested on different microarray data sets obtained from various human tumor samples. The results demonstrate that this procedure allows the identification of specific phenotype markers and can classify previously unseen instances in the presence of multiple classes.

Mining of biological data I: identifying discriminating features via mean hypothesis testing

Large volumes of data are routinely collected during bioprocess operations and, more recently, in basic biological research using genomics-based technologies. While these data often lack sufficient detail to be used for mechanism identification, it is possible that the underlying mechanisms affecting cell phenotype or process outcome are reflected as specific patterns in the overall or temporal sensor logs. This raises the possibility of identifying outcome-specific fingerprints that can be used for process or phenotype classification and the identification of discriminating characteristics, such as specific genes or process variables. The aim of this work is to provide a systematic approach to identifying and modeling patterns in historical records and using this information for process classification. This approach differs from others in that emphasis is placed on analyzing the data structure first and thereby extracting potentially relevant features prior to model creation. The initial step in this overall approach is to first identify the discriminating features of the relevant measurements and time windows, which can then be subsequently used to discriminate among different classes of process behavior. This is achieved via a mean hypothesis testing algorithm. Next, the homogeneity of the multivariate data in each class is explored via a novel cluster analysis technique called PC1 Time Series Clustering to ensure that the data subsets used accurately reflect the variability displayed in the historical records. This will be the topic of the second paper in this series. We present here the method for identifying discriminating features in data via mean hypothesis testing along with results from the analysis of case studies from industrial fermentations

Mining of biological data II: assessing data structure and class homogeneity by cluster analysis

An important step in data analysis is class assignment which is usually done on the basis of a macroscopic phenotypic or bioprocess characteristic, such as high vs low growth, healthy vs diseased state, or high vs. low productivity. Unfortunately, such an assignment may lump together samples, which when derived from a more detailed phenotypic or bioprocess description are dissimilar, giving rise to models of lower quality and predictive power. In this paper we present a clustering algorithm for data preprocessing which involves the identification of fundamentally similar lots on the basis of the extent of similarity among the system variables. The algorithm combines aspects of cluster analysis and principal component analysis by applying agglomerative clustering methods to the first principal component of the system data matrix. As part of a rational strategy for developing empirical models, this technique selects lots (samples) which are most appropriate for inclusion in a training set by analyzing multivariate data homogeneity. Samples with similar data structures are identified and grouped together into distinct clusters. This knowledge is used in the formation of potential training sets. Additionally, this technique can identify atypical lots, i.e., samples that are not simply outliers but exhibit the general properties of one class but have been given the assignment of the other. The method is presented along with examples from its application to fermentation data sets.

A novel algorithm for the coupling control in solid-phase peptide synthesis

This paper describes a new method for the evaluation of conductimetric data collected during the in-line monitoring of the coupling step in solid-phase peptide synthesis. The control scheme relies on a feed-forward artificial neural network algorithm which can predict the final yield of the reaction within its initial 5 min by analyzing the conductivity signal profile. The yield values predicted by the artificial neural network algorithm result in good accordance with the data obtained by the commonly used ninhydrin test.

SPPS of difficult sequences. A comparison of chemical conditions, synthetic strategies and on-line monitoring

The H-Ala-Arg-(Ala)6-Lys-OH sequence is a biologically interesting 'difficult sequence' presenting N alpha-Fmoc deprotection and coupling problems. Different chemical conditions and synthetic strategies have been tested in order to overcome the problems due to sequence-dependent interactions. In particular, it was confirmed that different solvents in the deprotection step did not provide any significant improvement, but the use of a more efficient base in the deprotection mixture avoided insufficient unblocking of N alpha-protecting group; problems due to partial coupling in the last steps of the synthesis were solved by double coupling techniques. Moreover, the synthesis of the model peptide was carried out using both "continuous flow' and "batch' techniques. The present results demonstrate that on-line monitoring of the deprotection step by absorbance measurements represents a very effective tool to detect the onset of internal aggregations during the synthesis.