RAB11FIP5 Expression and Altered Natural Killer Cell Function Are Associated with Induction of HIV Broadly Neutralizing Antibody Responses.

HIV-1 broadly neutralizing antibodies (bnAbs) are difficult to induce with vaccines but are generated in ∼50% of HIV-1-infected individuals. Understanding the molecular mechanisms of host control of bnAb induction is critical to vaccine design. Here, we performed a transcriptome analysis of blood mononuclear cells from 47 HIV-1-infected individuals who made bnAbs and 46 HIV-1-infected individuals who did not and identified in bnAb individuals upregulation of RAB11FIP5, encoding a Rab effector protein associated with recycling endosomes. Natural killer (NK) cells had the highest differential expression of RAB11FIP5, which was associated with greater dysregulation of NK cell subsets in bnAb subjects. NK cells from bnAb individuals had a more adaptive/dysfunctional phenotype and exhibited impaired degranulation and cytokine production that correlated with RAB11FIP5 transcript levels. Moreover, RAB11FIP5 overexpression modulated the function of NK cells. These data suggest that NK cells and Rab11 recycling endosomal transport are involved in regulation of HIV-1 bnAb development.

Transfer learning with false negative control improves polygenic risk prediction.

Polygenic risk score (PRS) is a quantity that aggregates the effects of variants across the genome and estimates an individual's genetic predisposition for a given trait. PRS analysis typically contains two input data sets: base data for effect size estimation and target data for individual-level prediction. Given the availability of large-scale base data, it becomes more common that the ancestral background of base and target data do not perfectly match. In this paper, we treat the GWAS summary information obtained in the base data as knowledge learned from a pre-trained model, and adopt a transfer learning framework to effectively leverage the knowledge learned from the base data that may or may not have similar ancestral background as the target samples to build prediction models for target individuals. Our proposed transfer learning framework consists of two main steps: (1) conducting false negative control (FNC) marginal screening to extract useful knowledge from the base data; and (2) performing joint model training to integrate the knowledge extracted from base data with the target training data for accurate trans-data prediction. This new approach can significantly enhance the computational and statistical efficiency of joint-model training, alleviate over-fitting, and facilitate more accurate trans-data prediction when heterogeneity level between target and base data sets is small or high.

Lateral preoptic area neurons signal cocaine self-administration behaviors.

The lateral preoptic area is implicated in numerous aspects of substance use disorder. In particular, the lateral preoptic area is highly sensitive to the pharmacological properties of psychomotor stimulants, and its activity promotes drug-seeking in the face of punishment and reinstatement during abstinence. Despite the lateral preoptic area's complicity in substance use disorder, how precisely lateral preoptic area neurons signal the individual components of drug self-administration has not been ascertained. To bridge this gap, we examined how the firing of single lateral preoptic area neurons correlates with three discrete elements of cocaine self-administration: (1) drug-seeking (pre-response), (2) drug-taking (response) and (3) receipt of the cocaine infusion. A significant subset of lateral preoptic area neurons responded to each component with a mix of increases and decreases in firing-rate. A majority of these neurons signal the operant response with increases in spiking, though responses during the drug-seeking, taking and reciept windows were highly correlated.