Biology subjects

Gibson, R. M.

Publications and source records attributed to Gibson, R. M..

4 recordsLinked to original sources

bioRxiv2026

Modelling shiftwork and sleep fragmentation in mice: behavioural phenotypes and implications for hippocampal neurogenesis

Sleep disturbances compromise cognition, and overall physical and mental health; however, animal models of these effects remain limited, and typically impose more extreme disturbances than those experienced by humans. Here, we use behavioural interventions in mice to model two common types of sleep disruption: (1) sleep displacement associated with shiftwork and (2) sleep fragmentation associated with aging and disease. C57BL6/J mice were subjected to either a sleep deprivation protocol (n = 8, females = 4) or a fragmentation protocol (n = 8, females = 4), in which they were completely deprived of sleep or awoken every 2 min respectively, for 12 hours during their typical rest phase (lights-on) across 7 consecutive days. Age-matched control mice (n = 14, females = 7) remained undisturbed in their home cages. Activity during the 12-hour active phase was recorded via infrared motion sensors. After protocol completion, mice were injected with bromodeoxyuridine (BrdU, 200mg/kg) 2 hours prior to brain and blood collection; hippocampal neurogenesis and plasma corticosterone were then assessed. Results show that the sleep deprivation and fragmentation protocols had different, dynamic effects on behaviour; sleep deprivation was associated with an accumulating sleep need, operationalized as progressively reduced locomotor activity across the week, whereas fragmentation produced only an acute reduction of locomotor activity. Cell proliferation rates and corticosterone did not differ from controls. These protocols provide a foundation for future research into the mechanistic links between shiftwork and sleep fragmentation on health and cognition.

neuroscience↗

bioRxiv2026

Metabolic disease-relevant stimuli unmask context-dependent genetic regulation of cardiometabolic loci in human adipocytes

Genome-wide association studies (GWAS) have identified thousands of loci associated with cardiometabolic disease, yet translating these associations into regulatory mechanisms, effector genes, and cellular programs remains a major challenge. A key limitation is that genetic effects are often highly context dependent, varying across cell states and environmental conditions that are difficult to model at scale. Here, we leverage CellGenBank, a population-scale biobank of primary human adipose-derived mesenchymal stem cells (AMSCs), to implement a multi-donor cell village in vitro system to map cardiometabolic disease genetic variation across adipocyte differentiation and metabolic stress conditions. We pooled AMSCs from 118 donors into multiplexed villages, differentiated them toward adipocytes, and profiled chromatin accessibility and gene expression using single-nucleus multiome sequencing under four disease-relevant conditions: basal, elevated free fatty acids, low glucose, and hypoxia. By combining with genetic demultiplexing, we quantify how regulatory element activity, gene expression, and higher-order cellular programs are modulated by both genotype and environmental context. Across conditions, we identify widespread context-specific cis-regulatory effects, including expression and chromatin accessibility quantitative trait loci that are masked in baseline states. Genetic effects frequently converge on coordinated transcriptional programs linked to lipid metabolism, insulin responsiveness, and stress adaptation, enabling the identification of cellular program QTLs that bridge variants, genes, and disease-relevant phenotypes. Integration with cardiometabolic GWAS reveals enhanced colocalization in condition- and state-resolved analyses, highlighting the importance of modeling environmental context to resolve disease mechanisms. Together, our study establishes large-scale adipocyte cell villages as a powerful and generalizable framework to map the context-dependent regulatory architecture of cardiometabolic disease and provides a resource linking human genetic variation to adipocyte cellular programs.

genomics↗

bioRxiv2020

Specialized DNA structures act as genomic beacons for integration by evolutionarily diverse retroviruses.

Retroviral integration site targeting is not random and plays a critical role in expression and long-term survival of the integrated provirus. To better understand the genomic environment surrounding retroviral integration sites, we performed an extensive comparative analysis of new and previously published integration site data from evolutionarily diverse retroviruses from seven genera, including different HIV-1 subtypes. We showed that evolutionarily divergent retroviruses exhibited distinct integration site profiles with strong preferences for non-canonical B-form DNA (non-B DNA). Whereas all lentiviruses and most retroviruses integrate within or near genes and non-B DNA, MMTV and ERV integration sites were highly enriched in heterochromatin and transcription-silencing non-B DNA features (e.g. G4, triplex and Z-DNA). Compared to in vitro-derived HIV-1 integration sites, in vivo-derived sites are significantly more enriched in transcriptionally silent regions of the genome and transcription-silencing non-B DNA features. Integration sites from individuals infected with HIV-1 subtype A, C or D viruses exhibited different preferences for non-B DNA and were more enriched in transcriptionally active regions of the genome compared to subtype B virus. In addition, we identified several integration site hotspots shared between different HIV-1 subtypes with specific non-B DNA sequence motifs present at these hotspots. Together, these data highlight important similarities and differences in retroviral integration site targeting and provides new insight into how retroviruses integrate into genomes for long-term survival. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=164 SRC="FIGDIR/small/959932v2_ufig1.gif" ALT="Figure 1"> View larger version (50K): org.highwire.dtl.DTLVardef@107ba89org.highwire.dtl.DTLVardef@678221org.highwire.dtl.DTLVardef@19075f9org.highwire.dtl.DTLVardef@13144c7_HPS_FORMAT_FIGEXP M_FIG C_FIG Schematic comparing integration site profiles from evolutionarily diverse retroviruses. Upper left, heatmaps showing the fold-enrichment (blue) and fold-depletion (red) of integration sites near non-B DNA features (lower left). Lower right, circa plot showing integration site hotspots shared between HIV-1 subtype A, B, C and D virus.

molecular biology↗

bioRxiv2019

Detection of novel HIV-1 drug resistance mutations by support vector analysis of deep sequence data and experimental validation

The global HIV-1 pandemic comprises many genetically divergent subtypes. Most of our understanding of drug resistance in HIV-1 derives from subtype B, which predominates in North America and western Europe. However, about 90% of the pandemic represents non-subtype B infections. Here, we use deep sequencing to analyze HIV-1 from infected individuals in Uganda who were either treatment-naive or who experienced virologic failure on ART without the expected patterns of drug resistance. Our objective was to detect potentially novel associations between mutations in HIV-1 integrase and treatment outcomes in Uganda, where most infections are subtypes A or D. We retrieved a total of 380 archived plasma samples from patients at the Joint Clinical Research Centre (Kampala), of which 328 were integrase inhibitor-naive and 52 were raltegravir (RAL)-based treatment failures. Next, we developed a bioinformatic pipeline for alignment and variant calling of the deep sequence data obtained from these samples from a MiSeq platform (Illumina). To detect associations between within-patient polymorphisms and treatment outcomes, we used a support vector machine (SVM) for feature selection with multiple imputation to account for partial reads and low quality base calls. Candidate point mutations of interest were experimentally introduced into the HIV-1 subtype B NL4-3 backbone to determine susceptibility to RAL in U87.CD4.CXCR4 cells. Finally, we carried out replication capacity experiments with wild-type and mutant viruses in TZM-bl cells in the presence and absence of RAL. Our analyses not only identified the known major mutation N155H and accessory mutations G163R and V151I, but also novel mutations I203M and I208L as most highly associated with RAL failure. The I203M and I208L mutations resulted in significantly decreased susceptibility to RAL (44.0-fold and 54.9-fold, respectively) compared to wild-type virus (EC50=0.32 nM), and may represent novel pathways of HIV-1 resistance to modern treatments.\n\nAuthor summaryThere are many different types of HIV-1 around the world. Most of the research on how HIV-1 can become resistant to drug treatment has focused on the type (B) that is the most common in high-income countries. However, about 90% of infections around the world are caused by a type other than B. We used next-generation sequencing to analyze samples of HIV-1 from patients in Uganda (mostly infected by types A and D) for whom drug treatment failed to work, and whose infections did not fit the classic pattern of adaptation based on B. Next, we used machine learning to detect mutations in these virus populations that could explain the treatment outcomes. Finally, we experimentally added two candidate mutations identified by our analysis to a laboratory strain of HIV-1 and confirmed that they conferred drug resistance to the virus. Our study reveals new pathways that other types of HIV-1 may use to evolve resistance to drugs that make up the current recommended treatment for newly diagnosed individuals.

microbiology↗