Miranda L. Lynch

Background: Antiretroviral therapy (ART) suppresses HIV replication and partially restores immune function, but immunologic abnormalities often persist. Methods: We performed longitudinal multiparametric flow cytometry on peripheral blood mononuclear cells from 79 people with HIV-1 (51 women, 28 men) who were virologically suppressed and followed over a median 6 years of ART. We assessed T-cell counts and expression of activation (CD38⁺HLA-DR⁺), cycling (Ki67⁺), exhaustion (TIGIT⁺PD-1⁺), cytotoxicity (CD107a⁺), and regulatory (FoxP3⁺CD25⁺) markers across memory subsets, and we examined associations with sex and HIV reservoir size and activity. Results: CD4⁺ T-cell counts increased and CD8⁺ T-cell counts declined over time, improving CD4/CD8 ratios. Immune activation and cycling markers decreased in both T-cell compartments. TIGIT/PD-1 expression declined significantly in CD4⁺ memory subsets but not in CD8⁺ T cells, while CD107a expression remained elevated in effector memory CD8⁺ and CD4⁺ T cells. Regulatory CD4⁺ T cells declined over time, and no significant associations were observed between T-cell phenotypes and HIV reservoir measures or between sexes. Conclusions: Long-term ART promotes partial immune normalization, including reduced activation and reversal of CD4⁺ T-cell exhaustion. However, persistent expression of CD8⁺ T-cell surrogate markers of exhaustion and stable cytotoxic profiles suggest ongoing antigenic stimulation, potentially driven by HIV or chronic coinfections.

Rubrerythrins are a group of proteins within the Ferritin-like superfamily that display a defining four-helix bundle domain. They also show multiple structural features that are crucial to their functionality as iron storage proteins and in detoxification and oxidative stress response. Here we investigate rubrerythrin (Rbr) in multiple metalated states, from the pathogen Burkholderia pseudomallei ( Bp ). We use X-ray crystallography for structure determination of Rbr to probe the capacity and specificity of metal binding. Bp Rbr lacks the rubredoxin moiety found in canonical Rbrs from anaerobic lineages, and we demonstrate that Bp Rbr also possesses a domain-swapped dimer, which has functional implications for its putative role in oxidative stress response. We also carry out in crystallo spectroscopic assessment of Bp Rbr with various metals, using energy dispersive X-ray (EDX) spectroscopy. We observe that samples can contain metals other than those supplied in crystallization conditions, and developed a strategy of utilizing EDX spectroscopy to select those samples with single metal incorporation for downstream diffraction data collection. Our work underscores the importance of spectroscopic probing for definitive metal identification and characterization. Highlights: Present structures of apo-, Fe-, Mn-, and Co-bound rubrerythrin from B. pseudomallei Bp Rbr lacks rubredoxin domain and possesses unique domain swap fold in the dimer Bp Rbr features include promiscuous metal binding and pre-formed metal binding sites Highlights importance of in crystallo spectroscopy in investigating metalloproteins Mutant Rbr with abrogated metal binding supports premise of pre-formed binding sites

Diffraction-based structural methods contribute a large fraction of the biomolecular structural models available, providing a critical understanding of macromolecular architecture. These methods require crystallization of the target molecule, which remains a primary bottleneck in crystal-based structure determination. The National High-Throughput Crystallization Center at Hauptman–Woodward Medical Research Institute has focused on overcoming obstacles to crystallization through a combination of robotics-enabled high-throughput screening and advanced imaging to increase the success of finding crystallization conditions. This paper will describe the lessons learned from over 20 years of operation of our high-throughput crystallization services. The current experimental pipelines, instrumentation, imaging capabilities and software for image viewing and crystal scoring are detailed. New developments in the field and opportunities for further improvements in biomolecular crystallization are reflected on.

The HIV-1 protease is one of several common key targets of combination drug therapies for human immunodeficiency virus infection and acquired immunodeficiency syndrome (HIV/AIDS). During the progression of the disease, some individual patients acquire -drug resistance due to mutational hotspots on the viral proteins targeted by combination drug therapies. It has recently been discovered that drug-resistant mutations accumulate on the ‘flap region’ of the HIV-1 protease, which is a critical dynamic region involved in non-specific polypeptide binding during invasion and infection of the host cell. In this study, we utilize machine learning assisted comparative molecular dynamics, conducted at single amino acid site resolution, to investigate the dynamic changes that occur during functional dimerization and polypeptide binding of the main protease. We use a multi-agent machine learning model to identify conserved dynamics of the HIV-1 main protease that are preserved across simian and feline protease orthologs (SIV and FIV). We also investigate changes in dynamics due to common drug-resistant mutations in many patients. We find that a key functional site in the flap region, a solvent-exposed isoleucine (ILE50) and surrounding sites that control flap dynamics is often targeted by drug-resistance mutations, likely leading to malfunctional molecular dynamics affecting the overall flexibility of the flap region. We conclude that better long term patient outcomes may be achieved by designing drugs that target protease regions which are less dependent upon single sites with large functional binding effects.

This paper describes an ensemble cluster analysis of bivariate profiles of HIV biomarkers, viral load and CD4 cell counts, which jointly measure disease progression. Data are from a prevalent cohort of HIV positive participants in a clinical trial of vitamin supplementation in Botswana. These individuals were HIV positive upon enrollment, but with unknown times of infection. To categorize groups of participants based on their patterns of progression of HIV infection using both biomarkers, we combine univariate shape-based cluster results for multiple biomarkers through the use of ensemble clustering methods. We first describe univariate clustering for each of the individual biomarker profiles, and make use of shape-respecting distances for clustering the longitudinal profile data. In our data, profiles are subject to either missing or irregular measurements as well as unobserved initiation times of the process of interest. Shape-respecting distances that can handle such data issues, preserve time-ordering, and identify similar profile shapes are useful in identifying patterns of disease progression from longitudinal biomarker data. However, their performance with regard to clustering differs by severity of the data issues mentioned above. We provide an empirical investigation of shape-respecting distances (Fréchet and dynamic time warping (DTW)) on benchmark shape data, and use DTW in cluster analysis of biomarker profile observations. These reveal a primary group of ‘typical progressors,’ as well as a smaller group that shows relatively rapid progression. We then refine the analysis using ensemble clustering for both markers to obtain a single classification. The information from joint evaluation of the two biomarkers combined with ensemble clustering reveals subgroups of patients not identifiable through univariate analyses; noteworthy subgroups are those that appear to represent recently and chronically infected subsets.