Faculty Bibliography

BACKGROUND:) pose a major challenge to curing HIV, with many of its mechanisms still unclear. HIV-1 DNA integration and immune responses may alter the host's epigenetic landscape, potentially silencing HIV-1 replication. METHODS:. RESULTS:-associated genes were enriched on the pathways related to immune defence, transcription repression and host-virus interactions. CONCLUSIONS:These findings suggest that HIV-1 reservoir is linked to aberrant DNA methylation in CD4+ T cells, offering new insights into epigenetic mechanisms of HIV-1 latency and potential molecular targets for eradication strategies. KEY POINTS/CONCLUSIONS:Study involved 427 women with HIV. Identified 245 aberrant DNA methylation sites and 85 methylation regions in CD4+ T cells linked to the HIV-1 reservoir. Highlighted genes are involved in viral replication, immune defence, and host genome integration. Findings suggest potential molecular targets for eradication strategies.

OBJECTIVE:This cross-sectional study investigated the associations of neighborhood-level factors with immune activation, systemic inflammation, and leukocyte telomere length in 110 sexual minority men with human immunodeficiency virus. METHOD/METHODS:From 2013 to 2017, sexual minority men with human immunodeficiency virus who used stimulants were recruited in San Francisco, California and provided blood samples to measure the markers of immune activation, systemic inflammation, and leukocyte telomere length. To measure neighborhood-level indices, the home address for each participant was geocoded and linked to data from the Centers for Disease Control and Prevention. Hierarchical linear modeling was employed to investigate the associations of neighborhood-level factors with systemic inflammation and leukocyte telomere length. RESULTS:After adjusting for age, stimulant use, self-reported income, level of education, and race and ethnicity, residing in neighborhoods with greater percentages of poverty (β = .33, p < .001) and a higher proportion of racial/ethnic minority residents (β = .26, p < .05) were independently associated with higher levels of interleukin-6. Additionally, residing in neighborhoods with higher percentage of uninsured individuals was independently associated with higher tumor necrosis factor-alpha (β = .24, p < .05). Indices of neighborhood-level adversity were additionally associated with providing a urine sample that was reactive for stimulants (OR = 1.31, p = .002), which was, in turn, associated with shorter leukocyte telomere length (β = -.31, p < .05). CONCLUSIONS:Future longitudinal research should examine the biobehavioral pathways linking neighborhood-level factors and stimulant use with systemic inflammation and cellular aging. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

BACKGROUND:Depression affects 33% of women with type 2 diabetes (T2D) and leads to increased risks of premature mortality. Fluctuation and variation of depressive presentations can hinder clinical identification. PURPOSE/OBJECTIVE:We aimed to identify and examine subgroups characterized by distinct depressive symptom trajectories among women with T2D. METHODS:This retrospective analysis leveraged the Women's Interagency HIV Study data to identify depressive symptom trajectories based on the Center for Epidemiological Studies Depression scores (2014-2019) among women with and without HIV. Descriptive statistics characterized sample demographics (eg, age, race, income), clinical indices (eg, hemoglobin A1C [HbA1c], BMI, HIV status), and psychosocial experiences (eg, discrimination, social support, anxiety, pain). We used growth mixture modeling to identify groups defined by distinct depressive symptom trajectories and parametric and non-parametric tests to examine demographic, clinical, and psychosocial differences across subgroups. RESULTS:Among the 630 women included, the mean age was 50.4 (SD = 8.3) years, 72.4% identified as Black and non-Hispanic, and 68.2% were living with HIV. Five subgroups were identified and distinguished by severity and symptom type. Participants with lower incomes (P = .01), lower employment (P < .0001), lower social support (P = .0001), and experiences of discrimination (P < .0001) showed greater membership in threshold, moderate, and severe depressive subgroups. Subgroup membership was not associated with metabolic indices (BMI, HbA1c) or HIV status. Anxiety, pain, and loneliness (all P = .0001) were worse in subgroups with higher depressive symptoms. CONCLUSIONS:Among women with T2D, depressive symptom trajectories differ across clinical and social contexts. This study advances precision by delineating subgroups within a broad clinical category.

Hypermutated proviruses, which arise in a single Human Immunodeficiency Virus (HIV) replication cycle when host antiviral APOBEC3 proteins introduce extensive guanine to adenine mutations throughout the viral genome, persist in all people living with HIV receiving antiretroviral therapy (ART). However, hypermutated sequences are routinely excluded from phylogenetic trees because their extensive mutations complicate phylogenetic inference, and as a result, we know relatively little about their within-host evolutionary origins and dynamics. Using >1400 longitudinal single-genome-amplified HIV env-gp120 sequences isolated from six women over a median of 18 years of follow-up-including plasma HIV RNA sequences collected over a median of 9 years between seroconversion and ART initiation, and >500 proviruses isolated over a median of 9 years on ART-we evaluated three approaches for masking hypermutation in nucleotide alignments. Our goals were to (i) reconstruct phylogenies that can be used for molecular dating and (ii) phylogenetically infer the integration dates of hypermutated proviruses persisting during ART. Two of the approaches (stripping all positions containing putative APOBEC3 mutations from the alignment or replacing individual putative APOBEC3 mutations in hypermutated sequences with the ambiguous base R) consistently normalized tree topologies, eliminated erroneous clustering of hypermutated proviruses, and brought env-intact and hypermutated proviruses into comparable ranges with respect to multiple tree-based metrics. Importantly, these corrected trees produced integration date estimates for env-intact proviruses that were highly concordant with those from benchmark trees that excluded hypermutated sequences, supporting the use of these corrected trees for molecular dating. Subsequent molecular dating of hypermutated proviruses revealed that these sequences spanned a wide within-host age range, with the oldest ones dating to shortly after infection. This indicates that hypermutated proviruses, like other provirus types, begin to be seeded into the proviral pool immediately following infection and can persist for decades. In two of the six participants, hypermutated proviruses differed from env-intact ones in terms of their age distributions, suggesting that different provirus types decay at heterogeneous rates in some hosts. These simple approaches to reconstruct hypermutated provirus' evolutionary histories reveal insights into their in vivo origins and longevity toward a more comprehensive understanding of HIV persistence during ART.

While immunotherapy tends to be ineffective against pancreatic ductal adenocarcinoma (PDAC), this cancer type often elicits B-cell immunity. However, the exact antigens responsible for these spontaneous immune responses are still unclear. This study used a unique high-dimensional ELISA to analyze IgG responses to 93 post-translational modifications and other chemical determinants in PDAC patients at the time of diagnosis and before therapy. Results identified 13 specific targets of serum IgG that distinguished PDAC patients from healthy donors. Phosphorylated-serine, -threonine, and -tyrosine emerged as the primary targets, with most patients showing high-titer IgG, predominantly of the IgG1 and IgG3 subclasses. Moreover, serum reactivity to these phosphorylated residues was higher in patients with metastatic disease, suggesting a relation between B cell immunity and tumor burden. Lastly, immunofluorescence staining and phosphoproteomic analysis provided evidence of the accumulation of phosphorylated amino acids in PDAC cells and identified a series of consensus abnormal phosphosites. Overall, our findings reveal for the first time the development of robust antibody responses targeting phosphorylated residues in PDAC.

Methylation quantitative trait loci (meQTLs) quantify the effects of genetic variants on DNA methylation levels. However, most published studies utilize bulk methylation datasets composed of different cell types and limit our understanding of cell-type-specific methylation regulation. We propose a hierarchical Bayesian interaction (HBI) model to infer cell-type-specific meQTLs, which integrates a large-scale bulk methylation data and a small-scale cell-type-specific methylation data. Through simulations, we show that HBI enhances the estimation of cell-type-specific meQTLs. In real data analyses, we demonstrate that HBI can further improve the functional annotation of genetic variants and identify biologically relevant cell types for complex traits.

Oral squamous cell carcinoma (OSCC) is a highly unpredictable disease with devastating mortality rates that have not changed over the past decades, in the face of advancements in treatments and biomarkers, which have improved survival for other cancers. Delays in diagnosis are frequent, leading to more disfiguring treatments and poor outcomes for patients. The clinical challenge lies in identifying those patients at the highest risk of developing OSCC. Oral epithelial dysplasia (OED) is a precursor of OSCC with highly variable behavior across patients. There is no reliable clinical, pathological, histological, or molecular biomarker to determine individual risk in OED patients. Similarly, there are no robust biomarkers to predict treatment outcomes or mortality in OSCC patients. This review aims to highlight advancements in artificial intelligence (AI)-based methods to develop predictive biomarkers of OED transformation to OSCC or predictive biomarkers of OSCC mortality and treatment response. Biomarkers such as S100A7 demonstrate promising appraisal for the risk of malignant transformation of OED. Machine learning-enhanced multiplex immunohistochemistry workflows examine immune cell patterns and organization within the tumor immune microenvironment to generate outcome predictions in immunotherapy. Deep learning (DL) is an AI-based method using an extended neural network or related architecture with multiple "hidden" layers of simulated neurons to combine simple visual features into complex patterns. DL-based digital pathology is currently being developed to assess OED and OSCC outcomes. The integration of machine learning in epigenomics aims to examine the epigenetic modification of diseases and improve our ability to detect, classify, and predict outcomes associated with epigenetic marks. Collectively, these tools showcase promising advancements in discovery and technology, which may provide a potential solution to addressing the current limitations in predicting OED transformation and OSCC behavior, both of which are clinical challenges that must be addressed in order to improve OSCC survival.

Head and neck squamous cell carcinoma (HNSCC) is painful, and perineural invasion (PNI) has been associated with the worst pain. Pain due to HNSCC is diverse and may vary based on clinicopathological factors. This study aims to characterize different pain patterns linked with PNI, its influence on daily functioning, and gain insights into molecular changes and pathways associated with PNI-related pain in HNSCC patients. We conducted a cross-sectional study across 3 medical centers (n = 114), assessing pain phenotypes and their impact on daily functioning using 2 self-reported pain questionnaires, given to patients prior to their cancer surgery. Furthermore, we conducted RNA-seq analysis utilizing the The Cancer Genome Atlas dataset of HNSCC tumor from patients (n = 192) to identify genes relevant to both PNI and pain. Upon adjusting for demographic and clinicopathological variables using linear regression models, we found that PNI independently predicted function-evoked pain according to the University of Calfornia San Francisco Oral Cancer Pain Questionnaire, as well as the worst pain intensity reported in the Brief Pain Inventory. Distinct pain patterns were observed to be associated with daily activities in varying manners. Our molecular analyses revealed significant disruptions in pathways associated with the extracellular matrix structure and organization. The top differentially expressed genes linked to the extracellular matrix are implicated in cancer development, pain, and neurodegenerative diseases. Our data underscore the importance of properly categorizing pain phenotypes in future studies aiming to uncover mechanistic underpinnings of pain. Additionally, we have compiled a list of genes of interest that could serve as targets for both cancer and cancer pain management. PERSPECTIVE: PNI independently predicts function-evoked pain. Different pain phenotypes affect daily activities differently. We identified a list of candidate genes involved in the extracellular matrix structure and function that can be targeted for both cancer and cancer pain control.