Upregulation of cholesterol synthesis by lysosomal defects requires a functional mitochondrial respiratory chain.

Mitochondria and lysosomes are two organelles that carry out both signaling and metabolic roles in cells. Recent evidence has shown that mitochondria and lysosomes are dependent on one another, as primary defects in one cause secondary defects in the other. Although there are functional impairments in both cases, the signaling consequences of primary mitochondrial dysfunction and lysosomal defects are dissimilar. Here, we used RNA sequencing to obtain transcriptomes from cells with primary mitochondrial or lysosomal defects to identify the global cellular consequences associated with mitochondrial or lysosomal dysfunction. We used these data to determine the pathways affected by defects in both organelles, which revealed a prominent role for the cholesterol synthesis pathway. We observed a transcriptional upregulation of this pathway in cellular and murine models of lysosomal defects, while it is transcriptionally downregulated in cellular and murine models of mitochondrial defects. We identified a role for the posttranscriptional regulation of transcription factor SREBF1, a master regulator of cholesterol and lipid biosynthesis, in models of mitochondrial respiratory chain deficiency. Furthermore, we found that retention of Ca2+ in lysosomes of cells with mitochondrial respiratory chain defects contributes to the differential regulation of the cholesterol synthesis pathway in the mitochondrial and lysosomal defects tested. Finally, we verified in vivo, using a model of mitochondria-associated disease in Caenorhabditis elegans that normalization of lysosomal Ca2+ levels results in partial rescue of the developmental delay induced by the respiratory chain deficiency.

Up-regulation of cholesterol synthesis by lysosomal defects requires a functional mitochondrial respiratory chain.

Mitochondria and lysosomes are two organelles that carry out both signaling and metabolic roles in the cells. Recent evidence has shown that mitochondria and lysosomes are dependent on one another, as primary defects in one cause secondary defects in the other. Nevertheless, the signaling consequences of primary mitochondrial malfunction and of primary lysosomal defects are not similar, despite in both cases there are impairments of mitochondria and of lysosomes. Here, we used RNA sequencing to obtain transcriptomes from cells with primary mitochondrial or lysosomal defects, to identify what are the global cellular consequences that are associated with malfunction of mitochondria or lysosomes. We used these data to determine what are the pathways that are affected by defects in both organelles, which revealed a prominent role for the cholesterol synthesis pathway. This pathway is transcriptionally up-regulated in cellular and mouse models of lysosomal defects and is transcriptionally down-regulated in cellular and mouse models of mitochondrial defects. We identified a role for post-transcriptional regulation of the transcription factor SREBF1, a master regulator of cholesterol and lipid biosynthesis, in models of mitochondrial respiratory chain deficiency. Furthermore, the retention of Ca 2+ in the lysosomes of cells with mitochondrial respiratory chain defects contributes to the differential regulation of the cholesterol synthesis pathway in the mitochondrial and lysosomal defects tested. Finally, we verified in vivo , using models of mitochondria-associated diseases in C. elegans , that normalization of lysosomal Ca 2+ levels results in partial rescue of the developmental arrest induced by the respiratory chain deficiency.

Machine Learning-Based Exploratory Clinical Decision Support for Newly Diagnosed Patients With Acute Myeloid Leukemia Treated With 7 + 3 Type Chemotherapy or Venetoclax/Azacitidine.

PURPOSE: There are currently limited objective criteria to help assist physicians in determining whether an individual patient with acute myeloid leukemia (AML) is likely to do better with induction with either standard 7 + 3 chemotherapy or targeted therapy with venetoclax plus azacitidine. The study goal was to address this need by developing exploratory clinical decision support methods. PATIENTS AND METHODS: Univariable and multivariable analysis as well as comparison of a range of machine learning (ML) predictors were performed using cohorts of 120 newly diagnosed 7 + 3-treated AML patients compared with 101 venetoclax plus azacitidine-treated patients. RESULTS: A variety of features in the two patient cohorts were identified that may potentially correlate with short- and long-term outcomes, toxicities, and other considerations. A subset of these diagnostic features was then used to develop ML-based predictors with relatively high areas under the curve of short- and long-term outcomes, hospital stays, transfusion requirements, and toxicities for individual patients treated with either venetoclax/azacitidine or 7 + 3. CONCLUSION: Potential ML-based approaches to clinical decision support to help guide individual patients with newly diagnosed AML to either 7 + 3 or venetoclax plus azacitidine induction therapy were identified. Larger cohorts with separate test and validation studies are necessary to confirm these initial findings.

NLP-PIER: A Scalable Natural Language Processing, Indexing, and Searching Architecture for Clinical Notes.

Many design considerations must be addressed in order to provide researchers with full text and semantic search of unstructured healthcare data such as clinical notes and reports. Institutions looking at providing this functionality must also address the big data aspects of their unstructured corpora. Because these systems are complex and demand a non-trivial investment, there is an incentive to make the system capable of servicing future needs as well, further complicating the design. We present architectural best practices as lessons learned in the design and implementation NLP-PIER (Patient Information Extraction for Research), a scalable, extensible, and secure system for processing, indexing, and searching clinical notes at the University of Minnesota.

Application of HL7/LOINC Document Ontology to a University-Affiliated Integrated Health System Research Clinical Data Repository.

Fairview Health Services is an affiliated integrated health system partnering with the University of Minnesota to establish a secure research-oriented clinical data repository that includes large numbers of clinical documents. Standardization of clinical document names and associated attributes is essential for their exchange and secondary use. The HL7/LOINC Document Ontology (DO) was developed to provide a standard representation of clinical document attributes with a multi-axis structure. In this study, we evaluated the adequacy of DO to represent documents in the clinical data repository from legacy and current EHR systems across community and academic practice sites. The results indicate that a large portion of repository data items can be mapped to the current DO ontology but that document attributes do not always link consistently with DO axes and additional values for certain axes, particularly "Setting" and "Role" are needed for better coverage. To achieve a more comprehensive representation of clinical documents, more effort on algorithms, DO value sets, and data governance over clinical document attributes is needed.