In this task, you are given an abstract of article. Your task is to generate title for this article. Preferred titles are under thirty words.

Example Input: Osteogenesis during bone modeling and remodeling is coupled with angiogenesis. A recent study showed that a specific vessel subtype, strongly positive for CD31 and endomucin (CD31hiEmcnhi), couples angiogenesis and osteogenesis. Here, we found that platelet-derived growth factor-BB (PDGF-BB) secreted by preosteoclasts induces CD31hiEmcnhi vessel formation during bone modeling and remodeling. Mice with depletion of PDGF-BB in the tartrate-resistant acid phosphatasepositive cell lineage show significantly lower trabecular and cortical bone mass, serum and bone marrow PDGF-BB concentrations, and fewer CD31hiEmcnhi vessels compared to wild-type mice. In the ovariectomy (OVX)-induced osteoporotic mouse model, serum and bone marrow levels of PDGF-BB and numbers of CD31hiEmcnhi vessels are significantly lower compared to sham-operated controls. Treatment with exogenous PDGF-BB or inhibition of cathepsin K to increase the number of preosteoclasts, and thus the endogenous levels of PDGF-BB, increases CD31hiEmcnhi vessel number and stimulates bone formation in OVX mice. Thus, pharmacotherapies that increase PDGF-BB secretion from preosteoclasts offer a new therapeutic target for treating osteoporosis by promoting angiogenesis and thus bone formation.
Example Output: PDGF-BB secreted by preosteoclasts induces angiogenesis during coupling with osteogenesis

Example Input: Ataxia telangiectasia is a neurodegenerative disease caused by mutation of the Atm gene. Here we report that ataxia telangiectasia mutated (ATM) deficiency causes nuclear accumulation of histone deacetylase 4 (HDAC4) in neurons and promotes neurodegeneration. Nuclear HDAC4 binds to chromatin, as well as to myocyte enhancer factor 2A (MEF2A) and cAMP-responsive element binding protein (CREB), leading to histone deacetylation and altered neuronal gene expression. Blocking either HDAC4 activity or its nuclear accumulation blunts these neurodegenerative changes and rescues several behavioral abnormalities of ATM-deficient mice. Full rescue of the neurodegeneration, however, also requires the presence of HDAC4 in the cytoplasm, suggesting that the ataxia telangiectasia phenotype results both from a loss of cytoplasmic HDAC4 as well as its nuclear accumulation. To remain cytoplasmic, HDAC4 must be phosphorylated. The activity of the HDAC4 phosphatase, protein phosphatase 2A (PP2A), is downregulated by ATM-mediated phosphorylation. In ATM deficiency, enhanced PP2A activity leads to HDAC4 dephosphorylation and the nuclear accumulation of HDAC4. Our results define a crucial role of the cellular localization of HDAC4 in the events leading to ataxia telangiectasia neurodegeneration.
Example Output: Nuclear accumulation of HDAC4 in ATM deficiency promotes neurodegeneration in ataxia-telangiectasia

Example Input: The reconstruction of gene regulatory networks underlying cell differentiation from high-throughput gene expression and chromatin data remains a challenge. Here, we derive dynamic gene regulatory networks for human myeloid differentiation using a 5-day time series of RNA-seq and ATAC-seq data. We profile HL-60 promyelocytes differentiating into macrophages, neutrophils, monocytes, and monocyte-derived macrophages. We find a rapid response in the expression of key transcription factors and lineage markers that only regulate a subset of their targets at a given time, which is followed by chromatin accessibility changes that occur later along with further gene expression changes. We observe differences between promyelocyte- and monocyte-derived macrophages at both the transcriptional and chromatin landscape level, despite using the same differentiation stimulus, which suggest that the path taken by cells in the differentiation landscape defines their end cell state. More generally, our approach of combining neighboring time points and replicates to achieve greater sequencing depth can efficiently infer footprint-based regulatory networks from long series data.
Example Output:
Dynamic Gene Regulatory Networks of Human Myeloid Differentiation.