Hydroa vacciniforme-like lymphoproliferative disorder: an EBV disease with a low risk of systemic illness in whites.

Patients with classic hydroa vacciniforme-like lymphoproliferative disorder (HVLPD) typically have high levels of Epstein-Barr virus (EBV) DNA in T cells and/or natural killer (NK) cells in blood and skin lesions induced by sun exposure that are infiltrated with EBV-infected lymphocytes. HVLPD is very rare in the United States and Europe but more common in Asia and South America. The disease can progress to a systemic form that may result in fatal lymphoma. We report our 11-year experience with 16 HVLPD patients from the United States and England and found that whites were less likely to develop systemic EBV disease (1/10) than nonwhites (5/6). All (10/10) of the white patients were generally in good health at last follow-up, while two-thirds (4/6) of the nonwhite patients required hematopoietic stem cell transplantation. Nonwhite patients had later age of onset of HVLPD than white patients (median age, 8 vs 5 years) and higher levels of EBV DNA (median, 1 515 000 vs 250 000 copies/ml) and more often had low numbers of NK cells (83% vs 50% of patients) and T-cell clones in the blood (83% vs 30% of patients). RNA-sequencing analysis of an HVLPD skin lesion in a white patient compared with his normal skin showed increased expression of interferon-γ and chemokines that attract T cells and NK cells. Thus, white patients with HVLPD were less likely to have systemic disease with EBV and had a much better prognosis than nonwhite patients. This trial was registered at www.clinicaltrials.gov as #NCT00369421 and #NCT00032513.

Accurate detection of RNA stem-loops in structurome data reveals widespread association with protein binding sites.

RNA molecules are known to fold into specific structures which often play a central role in their functions and regulation. In silico folding of RNA transcripts, especially when assisted with structure profiling (SP) data, is capable of accurately elucidating relevant structural conformations. However, such methods scale poorly to the swaths of SP data generated by transcriptome-wide experiments, which are becoming more commonplace and advancing our understanding of RNA structure and its regulation at global and local levels. This has created a need for tools capable of rapidly deriving structural assessments from SP data in a scalable manner. One such tool we previously introduced that aims to process such data is patteRNA, a statistical learning algorithm capable of rapidly mining big SP datasets for structural elements. Here, we present a reformulation of patteRNA's pattern recognition scheme that sees significantly improved precision without major compromises to computational overhead. Specifically, we developed a data-driven logistic classifier which interprets patteRNA's statistical characterizations of SP data in addition to local sequence properties as measured with a nearest neighbour thermodynamic model. Application of the classifier to human structurome data reveals a marked association between detected stem-loops and RNA binding protein (RBP) footprints. The results of our application demonstrate that upwards of 30% of RBP footprints occur within loops of stable stem-loop elements. Overall, our work arrives at a rapid and accurate method for automatically detecting families of RNA structure motifs and demonstrates the functional relevance of identifying them transcriptome-wide.

Rapid Emergence and Evolution of SARS-CoV-2 Variants in Advanced HIV Infection.

Previous studies have linked the evolution of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) genetic variants to persistent infections in people with immunocompromising conditions1-4, but the evolutionary processes underlying these observations are incompletely understood. Here we used high-throughput, single-genome amplification and sequencing (HT-SGS) to obtain up to ~103 SARS-CoV-2 spike gene sequences in each of 184 respiratory samples from 22 people with HIV (PWH) and 25 people without HIV (PWOH). Twelve of 22 PWH had advanced HIV infection, defined by peripheral blood CD4 T cell counts (i.e., CD4 counts) <200 cells/µL. In PWOH and PWH with CD4 counts ≥200 cells/µL, most single-genome spike sequences in each person matched one haplotype that predominated throughout the infection. By contrast, people with advanced HIV showed elevated intra-host spike diversity with a median of 46 haplotypes per person (IQR 14-114). Higher intra-host spike diversity immediately after COVID-19 symptom onset predicted longer SARS-CoV-2 RNA shedding among PWH, and intra-host spike diversity at this timepoint was significantly higher in people with advanced HIV than in PWOH. Composition of spike sequence populations in people with advanced HIV fluctuated rapidly over time, with founder sequences often replaced by groups of new haplotypes. These population-level changes were associated with a high total burden of intra-host mutations and positive selection at functionally important residues. In several cases, delayed emergence of detectable serum binding to spike was associated with positive selection for presumptive antibody-escape mutations. Taken together, our findings show remarkable intra-host genetic diversity of SARS-CoV-2 in advanced HIV infection and suggest that adaptive intra-host SARS-CoV-2 evolution in this setting may contribute to the emergence of new variants of concern (VOCs).

Rapid structure-function insights via hairpin-centric analysis of big RNA structure probing datasets.

The functions of RNA are often tied to its structure, hence analyzing structure is of significant interest when studying cellular processes. Recently, large-scale structure probing (SP) studies have enabled assessment of global structure-function relationships via standard data summarizations or local folding. Here, we approach structure quantification from a hairpin-centric perspective where putative hairpins are identified in SP datasets and used as a means to capture local structural effects. This has the advantage of rapid processing of big (e.g. transcriptome-wide) data as RNA folding is circumvented, yet it captures more information than simple data summarizations. We reformulate a statistical learning algorithm we previously developed to significantly improve precision of hairpin detection, then introduce a novel nucleotide-wise measure, termed the hairpin-derived structure level (HDSL), which captures local structuredness by accounting for the presence of likely hairpin elements. Applying HDSL to data from recent studies recapitulates, strengthens and expands on their findings which were obtained by more comprehensive folding algorithms, yet our analyses are orders of magnitude faster. These results demonstrate that hairpin detection is a promising avenue for global and rapid structure-function analysis, furthering our understanding of RNA biology and the principal features which drive biological insights from SP data.

Automated Recognition of RNA Structure Motifs by Their SHAPE Data Signatures.

High-throughput structure profiling (SP) experiments that provide information at nucleotide resolution are revolutionizing our ability to study RNA structures. Of particular interest are RNA elements whose underlying structures are necessary for their biological functions. We previously introduced patteRNA, an algorithm for rapidly mining SP data for patterns characteristic of such motifs. This work provided a proof-of-concept for the detection of motifs and the capability of distinguishing structures displaying pronounced conformational changes. Here, we describe several improvements and automation routines to patteRNA. We then consider more elaborate biological situations starting with the comparison or integration of results from searches for distinct motifs and across datasets. To facilitate such analyses, we characterize patteRNA’s outputs and describe a normalization framework that regularizes results. We then demonstrate that our algorithm successfully discerns between highly similar structural variants of the human immunodeficiency virus type 1 (HIV-1) Rev response element (RRE) and readily identifies its exact location in whole-genome structure profiles of HIV-1. This work highlights the breadth of information that can be gleaned from SP data and broadens the utility of data-driven methods as tools for the detection of novel RNA elements.

Kinetics of Epstein-Barr Virus (EBV) Neutralizing and Virus-Specific Antibodies after Primary Infection with EBV.

Prospective studies of antibodies to multiple Epstein-Barr virus (EBV) proteins and EBV neutralizing antibodies in the same individuals before, during, and after primary EBV infection have not been reported. We studied antibody responses to EBV in college students who acquired primary EBV infection during prospective surveillance and correlated the kinetics of antibody response with the severity of disease. Neutralizing antibodies and enzyme-linked immunosorbent assay (ELISA) antibodies to gp350, the major target of neutralizing antibody, reached peak levels at medians of 179 and 333 days after the onset of symptoms of infectious mononucleosis, respectively. No clear correlation was found between the severity of the symptoms of infectious mononucleosis and the peak levels of antibody to individual viral proteins or to neutralizing antibody. In summary, we found that titers of neutralizing antibody and antibodies to multiple EBV proteins increase over many months after primary infection with EBV.