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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.

The COVID-19 pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) impacted healthcare, the workforce, and worldwide socioeconomics. Multi-dose mono- or bivalent mRNA vaccine regimens have shown high efficacy in protection against SARS-CoV-2 and its emerging variants with varying degrees of efficacy. Amino acid changes, primarily in the receptor-binding domain (RBD), result in selection for viral infectivity, disease severity, and immune evasion. Therefore, many studies have centered around neutralizing antibodies that target the RBD and their generation achieved through infection or vaccination. Here, we conducted a unique longitudinal study, analyzing the effects of a three-dose mRNA vaccine regimen exclusively using the monovalent BNT162b2 (Pfizer/BioNTech) vaccine, systematically administered to nine previously uninfected (naïve) individuals. We compare changes in humoral antibody responses across the entire SARS-CoV-2 spike glycoprotein (S) using a high-throughput phage display technique (VirScan). Our data demonstrate that two doses of vaccination alone can achieve the broadest and highest magnitudes of anti-S response. Moreover, we present evidence of novel highly boosted non-RBD epitopes that strongly correlate with neutralization and recapitulate independent findings. These vaccine-boosted epitopes could facilitate multi-valent vaccine development and drug discovery.

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.

Whether the human brain is a robust reservoir for HIV-1 subtype C has yet to be established. We aimed to determine whether HIV-1 subtype C infection can be detected in the brain tissue of a viremic individual at post-mortem and whether the viral burden was differential between different brain regions. This study reports a 38-year-old Zambian female decedent with severe wasting who was on Atripla for antiretroviral therapy. The cause of death was determined to be HIV/AIDS end-stage disease. The QuantStudio 3 Real-Time PCR System analyzed formalin-fixed paraffin-embedded tissue DNA from a systematic sampling of the entire left-brain hemisphere. Plasma and cerebral spinal fluid HIV-1 RNA loads were 576,123 and 14,962 copies/mL, respectively. The lymph node DNA viral load was 2316 copies per 10

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.

DNA methylation and histone modifications form the epigenetic code. Cytosine methylation maintains an inactive-condensed chromatin state and is involved in many cellular events such as gene expression, chromosomal stability, cell differentiation, and carcinogenesis. CpG islands (CGIs), which are stretches of DNA in vertebrate organisms with an over-representation of the CpG dinucleotide, are naturally unmethylated and play an important role in gene repression. There is a lack, however, of a CGI database that provides a comprehensive genome-wide analysis of the distribution and annotation of CGIs across organisms. We introduce dbCGI, a web-application that identifies, annotates, and analyzes CGIs. We performed individual genome-wide analysis of human and mouse CGIs in addition to a comparative analysis of the two organisms' CGIs using homologue gene pairs.

AIM: The increase in antibiotic-resistant bacteria has dramatically revived the interest in plant products as alternative antimicrobial agents to prevent the efficiency of pathogenic microorganisms. Our aim in this study is to show the antimicrobial activities of commercially obtained thyme, rose, centaury and ozone oils against the clinically important bacteria and yeasts. METHODS: The antimicrobial activity of the thyme, rose, ozone and centaury oils were tested against Escherichia coli, Proteus vulgaris, Proteus mirabilis, Stenotrophomonas maltophilia, Enterococcus spp., Acinetobacter baumannii, Streptococcus spp., Citrobacter freundii, Staphylococcus aureus and Candida albicans strains. Disc diffusion method (Kirby-Bauer) was used to show the antimicrobial activity by measuring the zone diameters. RESULTS: Most bacteria including Stenotrophomonas maltophilia (which is only sensitive to a few antibiotics) are found sensitive to the thyme oil. Gram positive bacteria and yeasts found more resistant than the Gram negative bacteria to the thyme oil. Escherichia coli and Staphylococcus aureus found sensitive to the rose oil. The anti-microbial activities of some herbal oils and ozone oil and rose oil were tried to be shown. CONCLUSION: The thyme oil has a stronger antimicrobial activity than the rose, ozone and centaury oils. Herbal essential oils, especially thyme oil, are candidates to be alternatives in medical applications due to their anti-microbial effects., AMAÇ: Antibiyotiklere dirençli bakterilerin sayısındaki artış, tedavi basamağında alternatif bitkisel ürünlerin kullanılmasına olan ilginin dramatik bir şekilde artışına da sebep olmuştur. Bu çalışmadaki amacımız, ticari olarak elde ettiğimiz ozon, gül, kantaron ve kekik yağlarının klinik olarak önemli bakteri ve mantarlara karşı olan antimikrobiyal etkilerinin gösterilmesidir. YÖNTEM: Ozon, gül, kantaron ve kekik yağlarının Escherichia coli, Proteusvulgaris, Proteusmirabilis, Stenotrophomonas maltophilia, Enterococcus spp., Acinetobacter baumannii, Streptococcus spp., Citrobacter freundii, Staphylococcus aureus ve Candida albicans isimli mikroorganizmalara karşı antimikrobiyal aktiviteleri test edilmiştir; Disk difüzyon metodu (Kirby-Bauer) kullanılmış ve oluşan zon çapları ölçülerek anti-mikrobiyal etki ortaya konulmuştur. BULGULAR: Sadece birkaç antibiyotiğe karşı duyarlı olan Stenotrophomonas maltophilia başta olmak üzere birçok bakteri, kekik yağına karşı duyarlı olarak tespit edilmiştir. Gram pozitif bakterilerin ve Kandida suşlarının kekik yağına, Gram negatif bakterilerden daha dirençli oldukları görülmüştür. Bunun yanı sıra Escherichia coli ve Staphylococcus aureus suşlarının ise gül ya- ğına karşı duyarlı oldukları tespit edilmiştir. Bazı herbal yağların ve ozon yağının muhtemel antimikrobiyal etkileri bu çalışma ile ortaya koyulmaya çalışılmıştır. SONUÇ: Kekik yağının antimikrobiyal etkinliği ozon, gül ve kantaron yağından oldukça yüksek düzeyde tespit edilmiştir. Sonuç olarak başta kekik yağı olmak üzere bitkisel uçucu yağlar, antimikrobiyal aktivitelerinden dolayı tıbbi ilaç uygulamalarına iyi bir alternatif olabilme potansiyeli taşımaktadır.