Biology subjects

Robinson, D. G.

Publications and source records attributed to Robinson, D. G..

5 recordsLinked to original sources

bioRxiv2022

The nuclear GAPDH-HMGB cascade in cortical microglia regulates cognitive flexibility

We report a mechanism that underlies stress-induced cognitive inflexibility at the molecular level. In a mouse model under subacute stress in which deficits in rule shifting tasks were elicited, the nuclear glyceraldehyde dehydrogenase (N-GAPDH) cascade was activated specifically in microglia in the prelimbic cortex. The cognitive deficits were normalized with a pharmacological intervention with a compound (the RR compound) that selectively blocked the initiation of N-GAPDH cascade without affecting glycolytic activity. The normalization was also observed with a microglia-specific genetic intervention targeting the N-GAPDH cascade. Furthermore, hyperactivation of the prelimbic layer 5 excitatory neurons, which are known to be a neuronal substrate of cognitive inflexibility, was also normalized by the pharmacological and microglia-specific genetic interventions. The RR compound may offer a mechanism-driven, translational opportunity against stress-induced cognitive inflexibility. Taken together, we show a pivotal role of cortical microglia and microglia-neuron interaction in stress-induced cognitive inflexibility. We underscore the N-GAPDH cascade in microglia, which causally mediates stress-induced cognitive alteration.

molecular biology↗

bioRxiv2019

Determining sufficient sequencing depth in RNA-Seq differential expression studies

RNA-Seq studies require a sufficient read depth to detect biologically important genes. Sequencing below this threshold will reduce statistical power while sequencing above will provide only marginal improvements in power and incur unnecessary sequencing costs. Although existing methodologies can help assess whether there is sufficient read depth, they are unable to guide how many additional reads should be sequenced to reach this threshold. We provide a new method called superSeq that models the relationship between statistical power and read depth. We apply the superSeq framework to 393 RNA-Seq experiments (1,021 total contrasts) in the Expression Atlas and find the model accurately predicts the increase in statistical power gained by increasing the read depth. Based on our analysis, we find that most published studies (> 70%) are undersequenced, i.e., their statistical power can be improved by increasing the sequencing read depth. In addition, the extent of saturation is highly dependent on statistical methodology: only 9.5%, 29.5%, and 26.6% of contrasts are saturated when using DESeq2, edgeR, and limma, respectively. Finally, we also find that there is no clear minimum per-transcript read depth to guarantee saturation for an entire technology. Therefore, our framework not only delineates key differences among methods and their impact on determining saturation, but will also be needed even as technology improves and the read depth of experiments increases. Researchers can thus use superSeq to calculate the read depth to achieve required statistical power while avoiding unnecessary sequencing costs.

genomics↗

bioRxiv2019

Relapse prevention through health technology program reduces hospitalization in schizophrenia

ImportancePsychiatric hospitalization is a major driver of cost in the treatment of schizophrenia. Symptom relapses are a frequent cause of hospitalization and both are primary source of burden to patients and their supporters.\n\nObjectiveTo determine whether a novel, multicomponent, and technology-enhanced approach to relapse prevention in outpatients following a psychiatric hospitalization could reduce days spent in a hospital after discharge.\n\nDesignThe Improving Care and Reducing Cost (ICRC) study was a quasi-experimental clinical trial in outpatients with schizophrenia conducted between February 2013 and April 2015 at 10 different sites in the US. Data were obtained from 89 participants who received usual relapse prevention services, followed by a second cohort of 349 participants who received the technology-enhanced relapse prevention program. Both groups were followed for 6 months.\n\nSettingOutpatient setting.\n\nParticipantsPatients were between 18 and 60 years old; had a diagnosis of schizophrenia, schizoaffective disorder, or psychotic disorder not otherwise specified; and were currently hospitalized or had been hospitalized within the past 30 days.\n\nInterventionPatients received usual care or a technology-enhanced relapse prevention program during a 6-month period after discharge.\n\nMain Outcome(s) and Measure(s)Days spent in a psychiatric hospital during 6 months after discharge.\n\nResultsThe study included 438 patients. Control participants (N = 89; 37 females) were enrolled first and received usual care for relapse prevention, and followed by 349 participants (128 females) who received technology-enhanced relapse prevention. Days of hospitalization were reduced by 4 days (Mean days: b = -4.25, 95% CI: -8.29; -0.21, P = 0.039) during follow-up in the intervention condition compared to control.\n\nConclusions and RelevanceThe reduction in days spent in the hospital for participants in the technology-informed relapse prevention program compared to those who received usual care, and the previously reported high satisfaction and usability suggest that technology-enhanced relapse prevention is an effective and feasible way to reduce rehospitalization days among patients with schizophrenia.\n\nKey pointsO_ST_ABSQuestionC_ST_ABSCan rehospitalizations in schizophrenia be prevented at reduced cost by innovative mobile technology-delivered interventions?\n\nFindingsIn this clinical trial with 438 patients, a technology-enhanced relapse prevention program compared to usual services reduced an average of four days of hospitalization per patient during the first 6 months following an index hospitalization.\n\nMeaningRelapse prevention through a health technology may improve care while reducing costs associated with hospitalization.

neuroscience↗

bioRxiv2018

Striatal volume and functional connectivity predict weight gain in early-phase psychosis

BackgroundSecond-generation antipsychotic drugs (SGAs) are essential in the treatment of psychotic disorders, but are well-known for inducing substantial weight gain and obesity. Critically, weight gain may reduce life expectancy for up to 20-30 years in patients with psychotic disorders, and prognostic bio-markers are generally lacking. The dorsal striatum, rich in dopamine D2 receptors which are antagonized by antipsychotic medications, plays a key role in the human reward system and in appetite regulation, suggesting that altered dopamine activity in the striatal reward circuitry may be responsible for increased food craving and resultant weight gain.\n\nMethodsHere, we measured striatal volume and striatal resting state functional connectivity at baseline, and weight gain over the course of 12 weeks of antipsychotic treatment in 81 patients with early-phase psychosis. We also included a sample of 58 healthy controls. Weight measurements were completed at baseline, and then weekly for 4 weeks, and every 2 weeks until week 12. We used linear mixed models to compute individual weight gain trajectories. Striatal volume and whole-brain striatal connectivity were then calculated for each subject, and used to assess the relationship between striatal structure and function and individual weight gain in multiple regression models.\n\nOutcomesPatients had similar baseline weights and body mass indices (BMI) compared to healthy controls. There was no evidence that prior drug exposure or duration of untreated psychosis correlated with baseline BMI. Higher left putamen volume and lower frontopolar connectivity predicted magnitude of weight gain in patients, and these effects multiplied when the structure-function interaction was considered.\n\nInterpretationThese results provide evidence for a synergistic effect of striatal structure and function on antipsychotics-induced weight gain. Lower fronto-striatal connectivity, implicated in less optimal long-term decision making, was associated with more weight gain, and this relationship was stronger for higher compared to lower left putamen volumes.\n\nFundingSupported by NIMH grant P50MH080173 to Dr. Malhotra, grant R01MH060004 to Dr. Robinson, grant R01MH076995 to Dr. Szeszko and R21MH101746 to Drs. Robinson and Szeszko.

neuroscience↗

bioRxiv2017

The Functional False Discovery Rate with Applications to Genomics

The false discovery rate measures the proportion of false discoveries among a set of hypothesis tests called significant. This quantity is typically estimated based on p-values or test statistics. In some scenarios, there is additional information available that may be used to more accurately estimate the false discovery rate. We develop a new framework for formulating and estimating false discovery rates and q-values when an additional piece of information, which we call an \"informative variable\", is available. For a given test, the informative variable provides information about the prior probability a null hypothesis is true or the power of that particular test. The false discovery rate is then treated as a function of this informative variable. We consider two applications in genomics. Our first is a genetics of gene expression (eQTL) experiment in yeast where every genetic marker and gene expression trait pair are tested for associations. The informative variable in this case is the distance between each genetic marker and gene. Our second application is to detect differentially expressed genes in an RNA-seq study carried out in mice. The informative variable in this study is the per-gene read depth. The framework we develop is quite general, and it should be useful in a broad range of scientific applications.

genomics↗