Biology subjects

Class, C. A.

Publications and source records attributed to Class, C. A..

3 recordsLinked to original sources

bioRxiv2024

Gene expression signatures of response to fluoxetine treatment: systematic review and meta-analyses

BackgroundSelecting the best antidepressant for a patient with major depressive disorder (MDD) remains a challenge, and some have turned to genomic (and other omic) data to identify an optimal therapy. In this work, we synthesized gene expression data for fluoxetine treatment in both human patients and rodent models, to better understand biological pathways affected by treatment, as well as those that may distinguish clinical or behavioral response. MethodsFollowing the PRISMA guidelines, we searched the Gene Expression Omnibus (GEO) for studies profiling humans or rodent models with treatment of the antidepressant fluoxetine, excluding those not done in the context of depression or anxiety, in an irrelevant tissue type, or with fewer than three samples per group. Included studies were systematically reanalyzed by differential expression analysis and Gene Set Enrichment Analysis (GSEA). Individual pathway and gene statistics were synthesized across studies by three p-value combination methods, and then corrected for false discovery. ResultsOf the 74 data sets that were screened, 20 were included: 18 in rodents, and two in tissue from human patients. Studies were highly heterogeneous in the comparisons of both treated vs. control samples and responders vs. non-responders, with 737 and 356 pathways, respectively, identified as significantly different between groups in at least one study. However, 19 pathways were identified as consistently different in responders vs. non-responders, including toll-like receptor (TLR) and other immune pathways. Signal transduction pathways were identified as consistently affected by fluoxetine treatment in depressed patients and rodent models. DiscussionThese meta-analyses confirm known pathways and provide new hints toward antidepressant resistance, but more work is needed. Most included studies involved rodent models, and both patient studies had small cohorts. Additional large-cohort studies applying additional omics technologies are necessary to understand the intricacies and heterogeneity of antidepressant response.

genomics↗

bioRxiv2023

Sequence features of transcriptional activation domains are consistent with the surfactant mechanism of gene activation

Transcriptional activation domains (ADs) of gene activators remain enigmatic for decades as they are short, extremely variable in sequence, structurally disordered, and interact fuzzily to a spectrum of targets. We showed that the single required characteristic of the most common acidic ADs is an amphiphilic aromatic-acidic surfactant-like property which is the key for the local gene-promoter chromatin phase transition and the formation of "transcription factory" condensates. We demonstrate that the presence of tryptophan and aspartic acid residues in the AD sequence is sufficient for in vivo functionality, even when present only as a single pair of residues within a 20-amino-acid sequence containing only 18 additional glycine residues. We demonstrate that breaking the amphipathic -helix in AD by prolines increases AD functionality. The proposed mechanism is paradigm-shifting for gene activation area and generally for biochemistry as it relies on near-stochastic allosteric interactions critical for the key biological function.

molecular biology↗

bioRxiv2020

Th17 cells contribute to combination MEK inhibitor and anti-PD-L1 therapy resistance in KRAS/p53 mutant lung cancers

Understanding resistance mechanisms to targeted therapies and immune checkpoint blockade in mutant KRAS lung cancers is critical to developing novel combination therapies and improving patient survival. Here, we show that MEK inhibition enhanced PD-L1 expression while PD-L1 blockade upregulated MAPK signaling in mutant KRAS lung tumors. Combined MEK inhibition with anti-PD-L1 synergistically reduced lung tumor growth and metastasis, but tumors eventually developed resistance to sustained combinatorial therapy. Multi-platform profiling revealed that resistant lung tumors have increased infiltration of Th17 cells, which secrete IL-17 and IL-22 cytokines to promote lung cancer cell invasiveness and MEK inhibitor resistance. Antibody depletion of IL-17A in combination with MEK inhibition and PD-L1 blockade markedly reduced therapy-resistance in vivo. Clinically, increased expression of Th17-associated genes in melanoma patients treated with PD-1 blockade predicted poorer overall survival and response. Our study validates a triple combinatorial therapeutic strategy to overcome resistance to combined MEK inhibitor and PD-L1 blockade.

cancer biology↗