Biology subjects

Ketchesin, K. D.

Publications and source records attributed to Ketchesin, K. D..

7 recordsLinked to original sources

bioRxiv2026

Astrocyte molecular rhythm disruption in nucleus accumbens promotes increased binge-like drinking in mice

Alcohol misuse is a leading cause of preventable death worldwide. Chronic alcohol is associated with disrupted circadian rhythms, yet molecular mechanisms linking circadian rhythm dysregulation and alcohol consumption are poorly understood. Current FDA-approved treatments for alcohol use disorder (AUD) do not target molecular rhythms or sleep-wake cycles. Mammalian circadian rhythms are regulated by transcription-translation feedback loops that regulate expression of 'clock genes' (e.g., Arntl encoding for BMAL1). Both human and rodent studies demonstrate associations between clock gene variants and changes in reward-seeking behavior. Evidence suggests astrocytes, non-neuronal brain cells with cell-autonomous rhythms, may regulate both circadian rhythms and reward. In the nucleus accumbens (NAc), a region responsible for modulating alcohol- and reward-related behavior, over 43% of the astrocyte transcriptome is expressed rhythmically. However, no studies to date have investigated roles of NAc astrocyte rhythmicity in regulating alcohol drinking. We used AAV8-Gfap-Cre to functionally ablate molecular rhythms in NAc astrocytes of BMAL1 floxed mice. Two-bottle choice (2BC), drinking-in-the-light (DIL), and drinking-in-the-dark (DID) assessed alcohol drinking. Behavioral assays included locomotor response to novelty, sucrose preference, and social interaction. Disrupting molecular clock function in NAc astrocytes increased binge-like drinking during both DIL and DID paradigms (d = 1.44), but not with any other drinking paradigm or behavior. This study carves out a unique role for astrocytes in controlling the temporal organization of reward circuitry and susceptibility to binge-drinking. Future studies will investigate clock-controlled astrocyte mechanisms, such as glutamate uptake and ATP release, that may underlie binge-like drinking behavior.

neuroscience↗

bioRxiv2026

Gene-specific exponent-corrected normalization for library size in bulk RNA-seq

Correcting for library size is an essential step in bulk RNA-seq analyses, as differences in sequencing depth across samples can obscure biological signal with technical noise. While numerous normalization methods and model-based strategies have been proposed, we demonstrate here that library size-normalized counts and differential expression results obtained from such widely adopted approaches often remain strongly correlated with library size in large-scale RNA-seq experiments. Through a systematic analysis of over 100 publicly available GEO and TCGA RNA-seq datasets with raw count data, we show that library size association is observed for a substantial proportion of genes even after state-of-the-art library size correction approaches recommended by leading normalization tools. To address this issue, we propose gecco, a gene-specific exponent-corrected normalization method for RNA-seq counts that incorporates library size directly into the statistical framework via a gene-specific correction term, rather than applying a uniform adjustment factor across all genes. This formulation generalizes existing normalization approaches and yields normalized counts that are free of residual library size effects. Using both simulation studies and real large-scale RNA-seq datasets, we show that our method mitigates library size bias while preserving biological signal across a range of parameter settings. We further demonstrate that our approach leads to higher detection accuracy and more biologically meaningful pathway enrichment results in downstream differential expression and rhythmicity analyses without compromising false discovery rate control. Our method is implemented in R and is fully compatible with the widely used differential expression analysis methods DESeq2 and edgeR.

bioinformatics↗

bioRxiv2026

Unraveling Tissue-Specific Molecular Signatures and Convergent Pathway Enrichments in Suicidal Behavior

Suicide is a leading cause of death worldwide, yet the biological mechanisms underlying suicide remain poorly understood. A clearer understanding at the molecular level is essential for developing objective biomarkers and targeted interventions. In this study, we used transcriptomic profiling to investigate gene expression patterns associated with suicidal thoughts and behaviors across peripheral blood (n=264) and postmortem brain tissue from two prefrontal regions (dorsolateral prefrontal cortex, DLPFC; subgenual anterior cingulate cortex, sgACC) of individuals with and without psychiatric illness (n=249). Peripheral analyses revealed broad transcriptional changes associated with suicidal thoughts and behaviors, marked by dysregulated immune-related and inflammatory processes. Longitudinal modeling further revealed gene co-expression modules that predicted future suicide attempts over a 12-month follow-up, highlighting processes related to apoptosis, mitochondrial function, and immune regulation. By contrast, transcriptomic analyses of postmortem tissue derived from the DLPFC and sgACC revealed largely suppressed neuroimmune activity. Gene co-expression analyses in the brain identified suicide-associated modules enriched for synaptic plasticity, oxidative stress, and neuroimmune function, some of which displayed regional specificity. Cross-tissue comparison showed minimal gene-level overlap between brain and blood, although shared pathway-level themes emerged in immune, sensory, and cellular stress processes. Taken together, these findings suggest that suicide is associated with distinct but functionally convergent transcriptional alterations across brain and blood. By integrating tissue-specific and systems-level molecular signatures, this work provides insight into the biological architecture of suicide and lays the groundwork for developing novel biomarkers and therapeutic targets to improve prevention and treatment outcomes.

neuroscience↗

bioRxiv2024

Comparative transcriptomic rhythms in the mouse and human prefrontal cortex

Alterations in multiple subregions of the human prefrontal cortex (PFC) have been heavily implicated in psychiatric diseases. Moreover, emerging evidence suggests that circadian rhythms in gene expression are present across the brain, including in the PFC, and that these rhythms are altered in disease. However, investigation into the potential circadian mechanisms underlying these diseases in animal models must contend with the fact that the human PFC is highly evolved and specialized relative to that of rodents. Here, we use RNA sequencing to lay the groundwork for translational studies of molecular rhythms through a sex-specific, cross species comparison of transcriptomic rhythms between the mouse medial PFC (mPFC) and two subregions of the human PFC, the anterior cingulate cortex (ACC) and the dorsolateral PFC (DLPFC). We find that while circadian rhythm signaling is conserved across species and subregions, there is a phase shift in the expression of core clock genes between the mouse mPFC and human PFC subregions that differs by sex. Furthermore, we find that the identity of rhythmic transcripts is largely unique between the mouse mPFC and human PFC subregions, with the most overlap (20%, 236 transcripts) between the mouse mPFC and the human ACC in females. Nevertheless, we find that basic biological processes are enriched for rhythmic transcripts across species, with key differences between regions and sexes. Together, this work highlights both the evolutionary conservation of transcriptomic rhythms and the advancement of the human PFC, underscoring the importance of considering cross-species differences when using animal models.

neuroscience↗

bioRxiv2023

Sexually dimorphic mechanisms of VGLUT-mediated protection from dopaminergic neurodegeneration

Parkinsons disease (PD) targets some dopamine (DA) neurons more than others. Sex differences offer insights, with females more protected from DA neurodegeneration. The mammalian vesicular glutamate transporter VGLUT2 and Drosophila ortholog dVGLUT have been implicated as modulators of DA neuron resilience. However, the mechanisms by which VGLUT2/dVGLUT protects DA neurons remain unknown. We discovered DA neuron dVGLUT knockdown increased mitochondrial reactive oxygen species in a sexually dimorphic manner in response to depolarization or paraquat-induced stress, males being especially affected. DA neuron dVGLUT also reduced ATP biosynthetic burden during depolarization. RNA sequencing of VGLUT+ DA neurons in mice and flies identified candidate genes that we functionally screened to further dissect VGLUT-mediated DA neuron resilience across PD models. We discovered transcription factors modulating dVGLUT-dependent DA neuroprotection and identified dj-1{beta} as a regulator of sex-specific DA neuron dVGLUT expression. Overall, VGLUT protects DA neurons from PD-associated degeneration by maintaining mitochondrial health.

neuroscience↗

bioRxiv2022

The ultradian transcriptome of the human prefrontal cortex and abnormalities in schizophrenia

Twelve-hour (12 h) ultradian rhythms are a well-known phenomenon in coastal marine organisms. While 12 h cycles are observed in human behavior and physiology, no study has measured 12 h rhythms in the human brain. Here we identify 12 h rhythms in transcripts which either peak at sleep/wake transitions (~9 AM/PM) or static times (~3 PM/AM) in the dorsolateral prefrontal cortex, a region involved in cognition. Subjects with schizophrenia lose 12 h rhythms in genes associated with the unfolded protein response and neuronal structural maintenance. Moreover, genes involved in mitochondrial function and protein translation, which normally peak at sleep/wake transitions, peak instead at static times in schizophrenia, suggesting suboptimal timing of these essential processes that may contribute to cognitive deficits commonly found in schizophrenia.

neuroscience↗

bioRxiv2022

Experimental Design and Power Calculation in Omics Circadian Rhythmicity Detection

Circadian clocks are 24-hour endogenous oscillators in physiological and behavioral processes. Though recent transcriptomic studies have been successful in revealing the circadian rhythmicity in gene expression, the power calculation for omics circadian analysis have not been fully explored. In this paper, we develop a statistical method, namely CircaPower, to perform power calculation for circadian pattern detection. Our theoretical framework is determined by three key factors in circadian gene detection: sample size, intrinsic effect size and sampling design. Via simulations, we systematically investigate the impact of these key factors on circadian power calculation. We not only demonstrate that CircaPower is fast and accurate, but also show its underlying cosinor model is robust against variety of violations of model assumptions. In real applications, we demonstrate the performance of CircaPower using mouse pan-tissue data and human post-mortem brain data, and illustrate how to perform circadian power calculation using mouse skeleton muscle RNA-Seq pilot as case study. Our method CircaPower has been implemented in an R package, which is made publicly available on GitHub (https://github.com/circaPower/circaPower).

genomics↗