Mice Lacking Alkbh1 Display Sex-Ratio Distortion and Unilateral Eye Defects

Escherichia coli AlkB is a 2-oxoglutarate- and iron-dependent dioxygenase that reverses alkylated DNA damage by oxidative demethylation. Mouse AlkB homolog 1 (Alkbh1) is one of eight members of the newly discovered family of mammalian dioxygenases.In the present study we show non-Mendelian inheritance of the Alkbh1 targeted allele in mice. Both Alkbh1(-/-) and heterozygous Alkbh1(+/-) offspring are born at a greatly reduced frequency. Additionally, the sex-ratio is considerably skewed against female offspring, with one female born for every three to four males. Most mechanisms that cause segregation distortion, act in the male gametes and affect male fertility. The skewing of the sexes appears to be of paternal origin, and might be set in the pachythene stage of meiosis during spermatogenesis, in which Alkbh1 is upregulated more than 10-fold. In testes, apoptotic spermatids were revealed in 5-10% of the tubules in Alkbh1(-/-) adults. The deficiency of Alkbh1 also causes misexpression of Bmp2, 4 and 7 at E11.5 during embryonic development. This is consistent with the incompletely penetrant phenotypes observed, particularly recurrent unilateral eye defects and craniofacial malformations.Genetic and phenotypic assessment suggests that Alkbh1 mediates gene regulation in spermatogenesis, and that Alkbh1 is essential for normal sex-ratio distribution and embryonic development in mice

The impact of mobility network properties on predicted epidemic dynamics in Dhaka and Bangkok

Properties of city-level commuting networks are expected to influence epidemic potential of cities and modify the speed and spatial trajectory of epidemics when they occur. In this study, we use aggregated mobile phone user data to reconstruct commuter mobility networks for Bangkok (Thailand) and Dhaka (Bangladesh), two megacities in Asia with populations of 16 and 21 million people, respectively. We model the dynamics of directly-transmitted infections (such as SARS-CoV-2) propagating on these commuting networks, and find that differences in network structure between the two cities drive divergent predicted epidemic trajectories: the commuting network in Bangkok is composed of geographically-contiguous modular communities and epidemic dispersal is correlated with geographic distance between locations, whereas the network in Dhaka has less distinct geographic structure and epidemic dispersal is less constrained by geographic distance. We also find that the predicted dynamics of epidemics vary depending on the local topology of the network around the origin of the outbreak. Measuring commuter mobility, and understanding how commuting networks shape epidemic dynamics at the city level, can support surveillance and preparedness efforts in large cities at risk for emerging or imported epidemics

Low parasite connectivity among three malaria hotspots in Thailand

Identifying sources and sinks of malaria transmission is critical for designing effective intervention strategies particularly as countries approach elimination. The number of malaria cases in Thailand decreased 90% between 2012 and 2020, yet elimination has remained a major public health challenge with persistent transmission foci and ongoing importation. There are three main hotspots of malaria transmission in Thailand: Ubon Ratchathani and Sisaket in the Northeast; Tak in the West; and Yala in the South. However, the degree to which these hotspots are connected via travel and importation has not been well characterized. Here, we develop a metapopulation model parameterized by mobile phone call detail record data to estimate parasite flow among these regions. We show that parasite connectivity among these regions was limited, and that each of these provinces independently drove the malaria transmission in nearby provinces. Overall, our results suggest that due to the low probability of domestic importation between the transmission hotspots, control and elimination strategies can be considered separately for each region

Incorporating human mobility data improves forecasts of Dengue fever in Thailand

Over 390 million people worldwide are infected with dengue fever each year. In the absence of an effective vaccine for general use, national control programs must rely on hospital readiness and targeted vector control to prepare for epidemics, so accurate forecasting remains an important goal. Many dengue forecasting approaches have used environmental data linked to mosquito ecology to predict when epidemics will occur, but these have had mixed results. Conversely, human mobility, an important driver in the spatial spread of infection, is often ignored. Here we compare time-series forecasts of dengue fever in Thailand, integrating epidemiological data with mobility models generated from mobile phone data. We show that geographically-distant provinces strongly connected by human travel have more highly correlated dengue incidence than weakly connected provinces of the same distance, and that incorporating mobility data improves traditional time-series forecasting approaches. Notably, no single model or class of model always outperformed others. We propose an adaptive, mosaic forecasting approach for early warning systems