Human Hsp70 Substrate-Binding Domains Recognize Distinct Client Proteins

The 13 Hsp70 proteins in humans act on unique sets of substrates with diversity often being attributed to J-domain-containing protein (Hsp40 or JDP) cofactors. We were therefore surprised to find drastically different binding affinities for Hsp70-peptide substrates, leading us to probe substrate specificity among the 8 canonical Hsp70s from humans. We used peptide arrays to characterize Hsp70 binding and then mined these data using machine learning to develop an algorithm for isoform-specific prediction of Hsp70 binding sequences. The results of this algorithm revealed recognition patterns not predicted based on local sequence alignments. We then showed that none of the human isoforms can complement heat-shocked DnaK knockout Escherichia coli cells. However, chimeric Hsp70s consisting of the human nucleotide-binding domain and the substrate-binding domain of DnaK complement during heat shock, providing further evidence in vivo of the divergent function of the Hsp70 substrate-binding domains. We also demonstrated that the differences in heat shock complementation among the chimeras are not due to loss of DnaJ binding. Although we do not exclude JDPs as additional specificity factors, our data demonstrate substrate specificity among the Hsp70s, which has important implications for inhibitor development in cancer and neurodegeneration

Human Hsp70 Substrate-Binding Domains Recognize Distinct Client Proteins

The 13 Hsp70 proteins in humans act on unique sets of substrates with diversity often being attributed to J-domain-containing protein (Hsp40 or JDP) cofactors. We were therefore surprised to find drastically different binding affinities for Hsp70-peptide substrates, leading us to probe substrate specificity among the 8 canonical Hsp70s from humans. We used peptide arrays to characterize Hsp70 binding and then mined these data using machine learning to develop an algorithm for isoform-specific prediction of Hsp70 binding sequences. The results of this algorithm revealed recognition patterns not predicted based on local sequence alignments. We then showed that none of the human isoforms can complement heat-shocked DnaK knockout Escherichia coli cells. However, chimeric Hsp70s consisting of the human nucleotide-binding domain and the substrate-binding domain of DnaK complement during heat shock, providing further evidence in vivo of the divergent function of the Hsp70 substrate-binding domains. We also demonstrated that the differences in heat shock complementation among the chimeras are not due to loss of DnaJ binding. Although we do not exclude JDPs as additional specificity factors, our data demonstrate substrate specificity among the Hsp70s, which has important implications for inhibitor development in cancer and neurodegeneration

Human Hsp70 Substrate-Binding Domains Recognize Distinct Client Proteins

The 13 Hsp70 proteins in humans act on unique sets of substrates with diversity often being attributed to J-domain-containing protein (Hsp40 or JDP) cofactors. We were therefore surprised to find drastically different binding affinities for Hsp70-peptide substrates, leading us to probe substrate specificity among the 8 canonical Hsp70s from humans. We used peptide arrays to characterize Hsp70 binding and then mined these data using machine learning to develop an algorithm for isoform-specific prediction of Hsp70 binding sequences. The results of this algorithm revealed recognition patterns not predicted based on local sequence alignments. We then showed that none of the human isoforms can complement heat-shocked DnaK knockout Escherichia coli cells. However, chimeric Hsp70s consisting of the human nucleotide-binding domain and the substrate-binding domain of DnaK complement during heat shock, providing further evidence in vivo of the divergent function of the Hsp70 substrate-binding domains. We also demonstrated that the differences in heat shock complementation among the chimeras are not due to loss of DnaJ binding. Although we do not exclude JDPs as additional specificity factors, our data demonstrate substrate specificity among the Hsp70s, which has important implications for inhibitor development in cancer and neurodegeneration

Data Streaming for Metabolomics: Accelerating Data Processing and Analysis from Days to Minutes

The speed and throughput of analytical platforms has been a driving force in recent years in the “omics” technologies and while great strides have been accomplished in both chromatography and mass spectrometry, data analysis times have not benefited at the same pace. Even though personal computers have become more powerful, data transfer times still represent a bottleneck in data processing because of the increasingly complex data files and studies with a greater number of samples. To meet the demand of analyzing hundreds to thousands of samples within a given experiment, we have developed a data streaming platform, XCMS Stream, which capitalizes on the acquisition time to compress and stream recently acquired data files to data processing servers, mimicking just-in-time production strategies from the manufacturing industry. The utility of this XCMS Online-based technology is demonstrated here in the analysis of T cell metabolism and other large-scale metabolomic studies. A large scale example on a 1000 sample data set demonstrated a 10 000-fold time savings, reducing data analysis time from days to minutes. Further, XCMS Stream has the capability to increase the efficiency of downstream biochemical dependent data acquisition (BDDA) analysis by initiating data conversion and data processing on subsets of data acquired, expanding its application beyond data transfer to smart preliminary data decision-making prior to full acquisition

Smartphone Analytics: Mobilizing the Lab into the Cloud for Omic-Scale Analyses

Active data screening is an integral part of many scientific activities, and mobile technologies have greatly facilitated this process by minimizing the reliance on large hardware instrumentation. In order to meet with the increasingly growing field of metabolomics and heavy workload of data processing, we designed the first remote metabolomic data screening platform for mobile devices. Two mobile applications (apps), XCMS Mobile and METLIN Mobile, facilitate access to XCMS and METLIN, which are the most important components in the computer-based XCMS Online platforms. These mobile apps allow for the visualization and analysis of metabolic data throughout the entire analytical process. Specifically, XCMS Mobile and METLIN Mobile provide the capabilities for remote monitoring of data processing, real time notifications for the data processing, visualization and interactive analysis of processed data (e.g., cloud plots, principle component analysis, box-plots, extracted ion chromatograms, and hierarchical cluster analysis), and database searching for metabolite identification. These apps, available on Apple iOS and Google Android operating systems, allow for the migration of metabolomic research onto mobile devices for better accessibility beyond direct instrument operation. The utility of XCMS Mobile and METLIN Mobile functionalities was developed and is demonstrated here through the metabolomic LC-MS analyses of stem cells, colon cancer, aging, and bacterial metabolism