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Inflammasomes are cytosolic innate immune sensors of pathogen infection and cellular damage that induce caspase-1-mediated inflammation upon activation. Although inflammation is protective, uncontrolled excessive inflammation can cause inflammatory diseases and can be detrimental, such as in coronavirus disease (COVID-19). However, the underlying mechanisms that control inflammasome activation are incompletely understood. Here we report that the leucine-rich repeat (LRR) protein ribonuclease inhibitor (RNH1), which shares homology with LRRs of NLRP (nucleotide-binding oligomerization domain, leucine-rich repeat, and pyrin domain containing) proteins, attenuates inflammasome activation. Deletion of RNH1 in macrophages increases interleukin (IL)-1β production and caspase-1 activation in response to inflammasome stimulation. Mechanistically, RNH1 decreases pro-IL-1β expression and induces proteasome-mediated caspase-1 degradation. Corroborating this, mouse models of monosodium urate (MSU)-induced peritonitis and lipopolysaccharide (LPS)-induced endotoxemia, which are dependent on caspase-1, respectively, show increased neutrophil infiltration and lethality in Rnh1 -/- mice compared with wild-type mice. Furthermore, RNH1 protein levels were negatively related with disease severity and inflammation in hospitalized COVID-19 patients. We propose that RNH1 is a new inflammasome regulator with relevance to COVID-19 severity.

Inflammasomes are cytosolic innate immune sensors of pathogen infection and cellular damage that induce caspase-1 mediated inflammation upon activation. Although inflammation is protective, uncontrolled excessive inflammation can cause inflammatory diseases and can be detrimental, such as in COVID-19. However, the underlying mechanisms that control inflammasome activation are incompletely understood. Here we report that the leucine rich repeat (LRR) protein Ribonuclease inhibitor (RNH1), which shares homology with LRRs of NLRP proteins, attenuates inflammasome activation. Deletion of RNH1 in macrophages increases IL-1b production and caspase-1 activation for inflammasome stimuli. Mechanistically, RNH1 decreases pro-IL-1b expression and induces proteasome-mediated caspase-1 degradation. Corroborating this, mouse models of monosodium urate (MSU)-induced peritonitis and LPS-induced endotoxemia, which are dependent on caspase-1, respectively show increased neutrophil infiltration and lethality in Rnh1-/- mice compared to WT mice. Furthermore, RNH1 protein levels are negatively correlated with inflammation and disease severity in hospitalized COVID-19 patients. We propose that RNH1 is a new inflammasome regulator with relevance to COVID-19 severity

BACKGROUND: The etiology of male breast cancer (MBC) is poorly understood. In particular, the extent to which the genetic basis of MBC differs from female breast cancer (FBC) is unknown. A previous genome-wide association study of MBC identified 2 predisposition loci for the disease, both of which were also associated with risk of FBC. METHODS: We performed genome-wide single nucleotide polymorphism genotyping of European ancestry MBC case subjects and controls in 3 stages. Associations between directly genotyped and imputed single nucleotide polymorphisms with MBC were assessed using fixed-effects meta-analysis of 1380 cases and 3620 controls. Replication genotyping of 810 cases and 1026 controls was used to validate variants with P values less than 1 × 10-06. Genetic correlation with FBC was evaluated using linkage disequilibrium score regression, by comprehensively examining the associations of published FBC risk loci with risk of MBC and by assessing associations between a FBC polygenic risk score and MBC. All statistical tests were 2-sided. RESULTS: The genome-wide association study identified 3 novel MBC susceptibility loci that attained genome-wide statistical significance (P < 5 × 10-08). Genetic correlation analysis revealed a strong shared genetic basis with estrogen receptor-positive FBC. Men in the top quintile of genetic risk had a fourfold increased risk of breast cancer relative to those in the bottom quintile (odds ratio = 3.86, 95% confidence interval = 3.07 to 4.87, P = 2.08 × 10-30). CONCLUSIONS: These findings advance our understanding of the genetic basis of MBC, providing support for an overlapping genetic etiology with FBC and identifying a fourfold high-risk group of susceptible men.

With the rise of cellular communications, the broadcast channel has gained prominence since it is an effective model of the cellular downlink channel. Multiple-input multiple-output (MIMO) communications has also gained in popularity due to its theoretically promised performance benefits over traditional single antenna systems. Combining these two theoretical objects yields the the MIMO, or vector, broadcast channel. To achieve the highest possible performance it is known that channel state information (CSI) from each user in the broadcast channel must be known at the transmitter. This requires each user to feed back this information, which is an unwanted overhead. The first part of the dissertation discusses methods to reduce this feedback overhead by considering use of multiple receive antennas. Under the random beamforming transmit methodology, the feedback is reduced by considering only feeding back the largest SINR value observed at each user. Analysis of this reduced feedback scheme involves finding the distribution of the largest SINR over correlated random variables. Each user can also use the additional receive antennas to perform LMMSE reception. The distribution of the post-processed SINR is found and used to compute the system performance. Feedback can be reduced after LMMSE reception by feeding back only the largest post-processed SINR. Bounding techniques on the distribution function of the maximum post-processed SINR are used to evaluate the performance of this scheme. Fixed finite thresholds are shown to have no asymptotic effects on the system performance. The second part of the thesis considers the design of thresholds as a function of the number of users to reduce feedback. The asymptotic properties of any successful threshold are derived, namely any threshold T(n) in the class o(log n) asymptotically achieves the optimal sum-rate scaling rate while any threshold T(n) in the class [omega]( \log n) loses all multi-user diversity. Under three proposed system performance metrics, the optimal threshold is obtained. These metrics are : constraining the average number of users providing feedback, constraining the probability that no user feeds back, and constraining the rate lost due to thresholding. The final portion of the dissertation address the question of fairness in the random beamforming technique. To improve fairness the proportional fair sharing algorithm is proposed as a substitute for the greedy scheduling algorithm. Under the Rayleigh fading model, the asymptotic performance is found to be identical to the asymptotic performance of the greedy algorithm. The rate of convergence of the proportional fair sharing algorithm to the asymptotic performance limit is found. The distribution of the distance from the asymptotic limit is computed. Additional analysis is performed on the use of thresholds under proportional fair sharing