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The humoral antibody response against Kaposi sarcoma-associated herpesvirus (KSHV) in infected individuals has been characterized demonstrating the latency-associated nuclear antigen (LANA) as the most antigenic KSHV protein. Despite the antigenicity of the protein, specific LANA epitopes have not been systematically characterized. Here, we utilized a bacteriophage T7 library, which displays 56-amino acid KSHV LANA peptides with 28-amino acid overlap (VirScan), to define those epitopes in LANA targeted by antibodies from a cohort of 62 sub-Saharan African Kaposi sarcoma (KS) patients and 22 KSHV-infected asymptomatic controls. Intra- and inter-patient breadth and magnitude of the anti-LANA responses were quantified at the peptide and amino acid levels. From these data, we derived a detailed epitope annotation of the entire LANA protein, with a high-resolution focus on the N- and C-termini. Overall, the central repeat region was highly antigenic, but the responses to this region could not be confidently mapped due to its high variability. The highly conserved N-terminus was targeted with low breadth and magnitude. In a minority of individuals, antibodies specific to the nuclear localization sequence and a portion of the proline-rich regions of the N-terminus were evident. In contrast, the first half of the conserved C-terminal domain was consistently targeted with high magnitude. Unfortunately, this region was not included in LANA partial C-terminal crystal structures, however, it was predicted to adopt predominantly random-coil structure. Coupled with functional and secondary structure domain predictions, VirScan revealed fine resolution epitope mapping of the N- and C-terminal domains of LANA that is consistent with previous antigenicity studies and may prove useful to correlate KSHV humoral immunity with pathogenesis.

Background: Epigenetic repression mechanisms play an important role in gene regulation, specifically in cancer development. In many cases, a CpG island’s (CGI) susceptibility or resistance to methylation is shown to be contributed by local DNA sequence features. Objective: To develop unbiased machine learning models–individually and combined for different biological features–that predict the methylation propensity of a CGI. Methods: We developed our model consisting of CGI sequence features on a dataset of 75 sequences (28 prone, 47 resistant) representing a genome-wide methylation structure. We tested our model on two independent datasets that are chromosome (132 sequences) and disease (70 sequences) specific. Results: We provided improvements in prediction accuracy over previous models. Our results indicate that combined features better predict the methylation propensity of a CGI (area under the curve (AUC) ~0.81). Our global methylation classifier performs well on independent datasets reaching an AUC of ~0.82 for the complete model and an AUC of ~0.88 for the model using select sequences that better represent their classes in the training set. We report certain de novo motifs and transcription factor binding site (TFBS) motifs that are consistently better in separating prone and resistant CGIs. Conclusion: Predictive models for the methylation propensity of CGIs lead to a better understanding of disease mechanisms and can be used to classify genes based on their tendency to contain methylation prone CGIs, which may lead to preventative treatment strategies. MATLAB® and Python™ scripts used for model building, prediction, and downstream analyses are available at https://github.com/dicleyalcin/methylProp_predictor.

Individual cells within the same population show various degrees of heterogeneity, which may be better handled with single-cell analysis to address biological and clinical questions. Single-cell analysis is especially important in developmental biology as subtle spatial and temporal differences in cells have significant associations with cell fate decisions during differentiation and with the description of a particular state of a cell exhibiting an aberrant phenotype. Biotechnological advances, especially in the area of microfluidics, have led to a robust, massively parallel and multi-dimensional capturing, sorting, and lysis of single-cells and amplification of related macromolecules, which have enabled the use of imaging and omics techniques on single cells. There have been improvements in computational single-cell image analysis in developmental biology regarding feature extraction, segmentation, image enhancement and machine learning, handling limitations of optical resolution to gain new perspectives from the raw microscopy images. Omics approaches, such as transcriptomics, genomics and epigenomics, targeting gene and small RNA expression, single nucleotide and structural variations and methylation and histone modifications, rely heavily on high-throughput sequencing technologies. Although there are well-established bioinformatics methods for analysis of sequence data, there are limited bioinformatics approaches which address experimental design, sample size considerations, amplification bias, normalization, differential expression, coverage, clustering and classification issues, specifically applied at the single-cell level. In this review, we summarize biological and technological advancements, discuss challenges faced in the aforementioned data acquisition and analysis issues and present future prospects for application of single-cell analyses to developmental biology.