Biology subjects

Ives, A.

Publications and source records attributed to Ives, A..

7 recordsLinked to original sources

bioRxiv2026

ACB1801 enhances tumor immunogenicity by targeting glycolysis/ferroptosis vulnerability and activating STAT1-signaling to overcome anti-PD-1 resistance in MSS colorectal cancer

BackgroundImmune checkpoint blockade (ICB) therapies demonstrate low efficacy in microsatellite stable (MSS) colorectal cancer (CRC) due to an immune-desert tumor microenvironment (TME) characterized by low antigen presentation and limited tumor-infiltrating lymphocytes (TILs). Harmine, a natural small-molecule and its promising derivatives ACB1801 have shown anti-tumor potential in preclinical models; however, their potential to reprogram the TME and overcome ICB resistance in MSS CRC remains unexplored. This study investigates whether and how ACB1801 can reshape TME to sensitize MSS CRC to ICB therapies. MethodsWe used the CT26 MSS colorectal cancer mouse model to evaluate the ability of the harmine derivative ACB1801 to enhance the efficacy of anti-PD-1 therapy. To characterize its mode of action, we performed immune landscape analysis and transcriptomic profiling of both CD45- and CD45+ tumor-derived cells. In parallel, mechanistic studies were conducted in vitro using mouse and human MSS CRC cell lines. ResultsWe demonstrate that the harmine derivative ACB1801 enhances the effectiveness of anti-PD-1 therapy in an MSS CRC mouse model. Combination therapy significantly increased CD8+ T cell infiltration and reduced regulatory T-cell (Treg) density in the TME. Transcriptomic profiling of CRC cells isolated from tumors treated with either anti-PD-1 alone or in combination with ACB1801 revealed significant enrichment of metabolic pathways in the combination group, characterized by reduced glycolysis and enhanced ferroptosis signatures. These findings were supported by in vitro data showing that ACB1801 reduces tumor cell glycolytic activity and promotes ferroptotic vulnerability. Mechanistically, ACB1801 induced STAT1 signaling, promoted CXCL10 release, and enhanced major histocompatibility complex class I (MHC-I)-dependent antigen presentation on tumor cells, thereby increasing tumor susceptibility to anti-PD-1 therapy. ConclusionCollectively, our findings indicate that combination therapy with harmine derivatives and ICBs represents a promising strategy for treating MSS CRC patients.

cancer biology↗

bioRxiv2025

Single-cell nanodroplet processing proteomics pipeline for analysis of human-derived microglia

Single-cell omics tools provide unique insights into heterogeneous cell populations and their responses to stimuli. For example, single-cell RNA sequencing has identified several transcriptionally distinct populations of microglia, which are resident immune cells of the central nervous system (CNS) that are responsive to CNS injury, infection, and neurodegeneration. To date, single cell studies of microglia have focused on RNA-sequencing or cytometry by time of flight (CyTOF) which provide indirect readouts of protein abundance or quantification of a limited number of targets. Herein, we present a workflow based on FACS-assisted isolation, cryopreservation and nanodroplet-based processing for single-cell mass spectrometry proteomics analysis of the postmortem human brain cortex-derived microglia. From a single microglial cell, 1039 proteins could be identified on average. As a proof-of-principle we applied single-cell proteomics for exploring the heterogeneity of brain microglia at the cellular level. This pilot proteomics data partially recapitulates the prior microglia subtypes. Specifically, we determined mitochondrial proteins, in particular members of NADH dehydrogenase (Complex I), cytochrome b-c1 (Complex III), cytochrome c oxidase (Complex IV), F1-ATPase (Complex V), and Na+/K+-ATPase complex drive variation across microglia. This pipeline offers the potential for identifying functionally and analytically relevant protein targets for microglia in Alzheimers disease and other neurological disorders. Significance of StudyMicroglia are a key brain cell type that may contribute to pathogenesis in neurodegenerative disease. Transcriptomic profiling of microglia from the central nervous system of humans and animal models has identified several subtypes of microglia, and complementary proteomic profiling of microglia is likely to provide functionally and therapeutically relevant targets, however single-proteomics studies of human-derived microglia are lacking. This work describes a label-free, single-cell proteomics approach for microglia isolated by fluorescence-activated cell sorting from a human donor that yields comparable numbers of identifications in comparison to prior single-cell RNA sequencing studies of microglia. This approach holds promise for enabling large-scale proteomics-based subtyping of microglia and studying their roles in neurodegenerative diseases.

molecular biology↗

bioRxiv2023

Identifying environmental factors affecting the production of pea aphid dispersal morphs in field populations

O_LIMany species of insects show phenotypically plastic dispersal traits in response to suboptimal environmental conditions. These polyphenetic traits can have large impacts on the population dynamics of the dispersing insects. Therefore, it is important to understand what environmental factors affect the development of dispersal traits and how these factors affect population dynamics via immigration/emigration. C_LIO_LIPea aphids (Acyrthosiphon pisum) exhibit a polyphenetic wing dimorphism in which individuals are either winged (alate) or not (apterous). The production of alate offspring is determined by environmental cues experienced by the mother. Environmental factors associated with these cues have been intensively studied, with crowding, host plant quality, natural enemies, and fungal infection being shown to affect the production of pea aphid alates under controlled laboratory conditions. Nonetheless, environmental factors affecting alate production have rarely been studied in field populations of pea aphids. C_LIO_LIUsing data from a three-year study of alate production in 5-9 alfalfa (lucerne) fields, we examined the effects of (i) pea aphid abundance, (ii) host plant maturity, (iii) temperature, (iv) predator and herbivore abundance, (v) parasitism, and (vi) entomopathic fungal incidence. In twice-weekly samples taken over the growing season, alate production ranged from 0 to 83% of the nymphal population. C_LIO_LIPea aphid abundance, temperature, and alfalfa maturity together explained 67% of the variation in alate production. The other factors we investigated explained little variation. These results suggest there is a limited number of key environmental factors that consistently predict changes in alate production in field populations, while many factors identified in lab studies may be unimportant. Our results highlight the value of investigating factors affecting the expression of plastic traits in insect species at a broad spatiotemporal scale and under natural conditions. C_LI

ecology↗

bioRxiv2021

Can we put Humpty Dumpty back together again? What does protein quantification mean in bottom-up proteomics?

Bottom-up proteomics provides peptide measurements and has been invaluable for moving proteomics into large-scale analyses. In bottom-up proteomics, protein parsimony and protein inference derived from these measured peptides are important for determining which protein coding genes are present. However, given the complexity of RNA splicing processes, and how proteins can be modified post-translationally, it is overly simplistic to assume that all peptides that map to a singular protein coding gene will demonstrate the same quantitative response. Accordingly, by assuming all peptides from a protein coding sequence are representative of the same protein we may be missing out on detecting important biological differences. To better account for the complexity of the proteome we need to think of new or better ways of handling peptide data.

biochemistry↗

bioRxiv2021

Ecosystem engineering alters density-dependent feedbacks in an aquatic insect population

Ecosystem engineers have large impacts on the communities in which they live, and these impacts may feed back to populations of engineers themselves. In this study, we assessed the effect of ecosystem engineering on density-dependent feedbacks for midges in Lake Myvatn, Iceland. The midge larvae reside in the sediment and build silk tubes that provide a substrate for algal growth, thereby elevating benthic primary production. Benthic algae are in turn the primary food source for the midge larvae, setting the stage for the effects of engineering to feed back to the midges themselves. Using a field mesocosm experiment manipulating larval midge densities, we found a generally positive but nonlinear relationship between density and benthic production. Furthermore, adult emergence increased with the primary production per midge larva. By combining these two relationships in a simple model, we found that the positive effect of midges on benthic production weakened the negative density dependence at low to intermediate larval densities. However, this benefit disappeared at high densities when midge consumption of primary producers exceeded their positive effects on primary production through ecosystem engineering. Our results illustrate how ecosystem engineering can alter density-dependent feedbacks for engineer populations.

ecology↗

bioRxiv2017

Functional traits and community composition: multilevel models outperform community-weighted means

O_LIPlant functional traits are increasingly being used to infer mechanisms about community assembly and predict global change impacts. Of the several approaches that are used to analyze trait-environment relationships, one of the most popular is community-weighted means (CWM), in which species trait values are averaged at the site level. Other approaches that do not require averaging are being developed, including multilevel models (MLM, also called generalized linear mixed models). However, relative strengths and weaknesses of these methods have not been extensively compared.\nC_LIO_LIWe investigated three statistical models for trait-environment associations: CWM, a MLM in which traits were not included as fixed effects (MLM1), and a MLM with traits as fixed effects (MLM2). We analyzed a real plant community dataset to investigate associations between two traits and one environmental variable. We then analyzed permutations of the dataset to investigate sources of type I errors, and performed a simulation study to compare the statistical power of the methods.\nC_LIO_LIIn the analysis of real data, CWM gave highly significant associations for both traits, while MLM1 and MLM2 did not. Using P-values derived by simulating the data using the fitted MLM2, none of the models gave significant associations, showing that CWM had inflated type I errors (false positives). In the permutation tests, MLM2 performed the best of the three approaches. MLM2 still had inflated type I error rates in some situations, but this could be corrected using bootstrapping. The simulation study showed that MLM2 always had as good or better power than CWM. These simulations also confirmed the causes of type I errors from the permutation study.\nC_LIO_LIThe MLM that includes main effects of traits (MLM2) is the best method for identifying trait-environmental association in community assembly, with better type I error control and greater power. Analyses that regress CWMs on continuous environmental variables are not reliable because they are likely to produce type I errors.\nC_LI

ecology↗

bioRxiv2017

R2s for Correlated Data: Phylogenetic Models, LMMs, and GLMMs

Many researchers want to report an R2 to measure the variance explained by a model. When the model includes correlation among data, such as phylogenetic models and mixed models, defining an R2 faces two conceptual problems. (i) It is unclear how to measure the variance explained by predictor (independent) variables when the model contains covariances. (ii) Researchers may want the R2 to include the variance explained by the covariances by asking questions such as \"How much of the variance is explained by phylogeny?\" Here, I investigate three R2s for phylogenetic and mixed models. A least-squares R2ls is an extension of the ordinary least-squares R2 that weights residuals by variances and covariances estimated by the model; it is closely related to R2glmm proposed by Nakagawa & Schielzeth (2013). The conditional expectation R2ce is based on \"predicting\" each residual from the remaining residuals of the fitted model. The likelihood ratio R2lr was first used by Cragg & Uhler (1970) for logistic regression, and here is used with the standardization proposed by Nagelkerke (1991). These three R2s are formulated as partial R2s, making it possible to compare the contributions of mean components (regression coefficients in phylogenetic models and fixed effects in mixed models) and variance components (phylogenetic correlations and random effects) to the fit of models. The properties of the R2s for phylogenetic models were assessed using simulations for continuous and binary response data (phylogenetic generalized least squares and phylogenetic logistic regression). Because the R2s are designed broadly for any model for correlated data, the R2s were also compared for LMMs and GLMMs. R2ls, R2ce, and R2lr all have good performance, and each has advantages and disadvantages for different applications. These R2s are computed in the R package rr2 (https://github.com/arives/rr2). [Binomial regression, coefficient of determination, non-independent residuals, phylogenetic model, pseudo-likelihood]

evolutionary biology↗