Nesprin-1 LINC complexes recruit microtubule cytoskeleton proteins and drive pathology in Lmna-mutant striated muscle.

Mutations in LMNA, the gene encoding A-type lamins, cause laminopathies-diseases of striated muscle and other tissues. The aetiology of laminopathies has been attributed to perturbation of chromatin organization or structural weakening of the nuclear envelope (NE) such that the nucleus becomes more prone to mechanical damage. The latter model requires a conduit for force transmission to the nucleus. NE-associated Linker of Nucleoskeleton and Cytoskeleton (LINC) complexes are one such pathway. Using clustered regularly interspaced short palindromic repeats to disrupt the Nesprin-1 KASH (Klarsicht, ANC-1, Syne Homology) domain, we identified this LINC complex protein as the predominant NE anchor for microtubule cytoskeleton components, including nucleation activities and motor complexes, in mouse cardiomyocytes. Loss of Nesprin-1 LINC complexes resulted in loss of microtubule cytoskeleton proteins at the nucleus and changes in nuclear morphology and positioning in striated muscle cells, but with no overt physiological defects. Disrupting the KASH domain of Nesprin-1 suppresses Lmna-linked cardiac pathology, likely by reducing microtubule cytoskeleton activities at the nucleus. Nesprin-1 LINC complexes thus represent a potential therapeutic target for striated muscle laminopathies.

Associations between forensic loci and expression levels of neighboring genes may compromise medical privacy.

A set of 20 short tandem repeats (STRs) is used by the US criminal justice system to identify suspects and to maintain a database of genetic profiles for individuals who have been previously convicted or arrested. Some of these STRs were identified in the 1990s, with a preference for markers in putative gene deserts to avoid forensic profiles revealing protected medical information. We revisit that assumption, investigating whether forensic genetic profiles reveal information about gene-expression variation or potential medical information. We find six significant correlations (false discovery rate = 0.23) between the forensic STRs and the expression levels of neighboring genes in lymphoblastoid cell lines. We explore possible mechanisms for these associations, showing evidence compatible with forensic STRs causing expression variation or being in linkage disequilibrium with a causal locus in three cases and weaker or potentially spurious associations in the other three cases. Together, these results suggest that forensic genetic loci may reveal expression levels and, perhaps, medical information.

Social Media Activism and Convergence in Tweet Topics After the Initial #MeToo Movement for Two Distinct Groups of Twitter Users.

Online social media movements are now common and support cultural discussions on difficult health and social topics. The #MeToo movement, focusing on the pervasiveness of sexual assault and harassment, has been one of the largest and most influential online movements. Our study examines topics of conversation on Twitter by supporters of the #MeToo movement and by Twitter users who were uninvolved in the movement to explore the extent to which tweet topics for these two groups converge over time. We identify and collect one year's worth of tweets for supporters of the #MeToo movement (N = 168 users; N = 105,538 tweets) and users not involved in the movement (N = 147 users; N = 112,301 tweets referred to as the Neutral Sample). We conduct topic frequency analysis and implement an unsupervised machine learning topic modeling algorithm, latent Dirichlet allocation, to explore topics of discussion on Twitter for these two groups of users before and after the initial #MeToo movement. Our results suggest that supporters of #MeToo discussed different topics compared to the Neutral Sample of Twitter users before #MeToo with some overlap on politics. The supporters were already discussing sexual assault and harassment issues six months before #MeToo, and discussion on this topic increased 13.7-fold in the six months after. For the Neutral Sample, sexual assault and harassment was not a key topic of discussion on Twitter before #MeToo, but there was some limited increase afterward. Results of bigram frequency analysis and topic modeling showed a clear increase in topic related to gender for the supporters of #MeToo but gave mixed results for the Neutral Sample comparison group. Our results suggest limited shifts in the conversation on Twitter for the Neutral Sample. Our methods and results have implications for measuring the extent to which online social media movements, like #MeToo, reach a broad audience.