Our findings provide evidence that integrin functions as an engulfment receptor at the whole-organism level and reveal a nonconventional signaling pathway in which SRC provides a FAK-independent linkage between integrin alpha and the common motility-promoting signaling module CED-2/CrkII-CED-5/Dock180-CED-12/ELMO to promote the internalization of apoptotic cells.

Mutations in the human progranulin gene resulting in protein haploinsufficiency cause frontotemporal lobar degeneration with TDP-43 inclusions. Although progress has been made in understanding the normal functions of progranulin and TDP-43, the molecular interactions between these proteins remain unclear. Progranulin is proteolytically processed into granulins, but the role of granulins in the pathogenesis of neurodegenerative disease is unknown. We used a Caenorhabditis elegans model of neuronal TDP-43 proteinopathy to specifically interrogate the contribution of granulins to the neurodegenerative process. Complete loss of the progranulin gene did not worsen TDP-43 toxicity, whereas progranulin heterozygosity did. Interestingly, expression of individual granulins alone had little effect on behavior. In contrast, when granulins were coexpressed with TDP-43, they exacerbated its toxicity in a variety of behaviors including motor coordination. These same granulins increased TDP-43 levels via a post-translational mechanism. We further found that in human neurodegenerative disease subjects, granulin fragments accumulated specifically in diseased regions of brain. To our knowledge, this is the first demonstration of a toxic role for granulin fragments in a neurodegenerative disease model. These studies suggest that presence of cleaved granulins, rather than or in addition to loss of full-length progranulin, may contribute to disease in TDP-43 proteinopathies.

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Clearance of apoptotic cells by engulfment plays an important role in the homeostasis and development of multicellular organisms. Despite the fact that the recognition of apoptotic cells by engulfment receptors is critical in inducing the engulfment process, the molecular mechanisms are still poorly understood. Here, we characterize a novel cell corpse engulfment pathway mediated by the integrin α subunit PAT-2 in Caenorhabditis elegans and show that it specifically functions in muscle-mediated engulfment during embryogenesis. Inactivation of pat-2 results in a defect in apoptotic cell internalization. The PAT-2 extracellular region binds to the surface of apoptotic cells in vivo, and the intracellular region may mediate signaling for engulfment. We identify essential roles of small GTPase CDC-42 and its activator UIG-1, a guanine-nucleotide exchange factor, in PAT-2–mediated cell corpse removal. PAT-2 and CDC-42 both function in muscle cells for apoptotic cell removal and are co-localized in growing muscle pseudopods around apoptotic cells. Our data suggest that PAT-2 functions through UIG-1 for CDC-42 activation, which in turn leads to cytoskeletal rearrangement and apoptotic cell internalization by muscle cells. Moreover, in contrast to PAT-2, the other integrin α subunit INA-1 and the engulfment receptor CED-1, which signal through the conserved signaling molecules CED-5 (DOCK180)/CED-12 (ELMO) or CED-6 (GULP) respectively, preferentially act in epithelial cells to mediate cell corpse removal during mid-embryogenesis. Our results show that different engulfing cells utilize distinct repertoires of receptors for engulfment at the whole organism level.

Because it is not feasible to collect training data for every language, there is a growing interest in cross-lingual transfer learning. In this paper, we systematically explore zeroshot cross-lingual transfer learning on reading comprehension tasks with a language representation model pre-trained on multi-lingual corpus. The experimental results show that with pre-trained language representation zeroshot learning is feasible, and translating the source data into the target language is not necessary and even degrades the performance. We further explore what does the model learn in zero-shot setting 0 .