Optimized Extraction Method To Remove Humic Acid Interferences from Soil Samples Prior to Microbial Proteome Measurements

The microbial composition and their activities in soil environments play a critical role in organic matter transformation and nutrient cycling. Liquid chromatography coupled to high-performance mass spectrometry provides a powerful approach to characterize soil microbiomes; however, the limited microbial biomass and the presence of abundant interferences in soil samples present major challenges to proteome extraction and subsequent MS measurement. To this end, we have designed an experimental method to improve microbial proteome measurement by removing the soil-borne humic substances coextraction from soils. Our approach employs an <i>in situ</i> detergent-based microbial lysis/TCA precipitation coupled to an additional cleanup step involving acidified precipitation and filtering at the peptide level to remove most of the humic acid interferences prior to proteolytic peptide measurement. The novelty of this approach is an integration to exploit two different characteristics of humic acids: (1) Humic acids are insoluble in acidic solution but should not be removed at the protein level, as undesirable protein removal may also occur. Rather it is better to leave the humics acids in the samples until the peptide level, at which point the significant differential solubility of humic acids versus peptides at low pH can be exploited very efficiently. (2) Most of the humic acids have larger molecule weights than the peptides. Therefore, filtering a pH 2 to 3 peptide solution with a 10 kDa filter will remove most of the humic acids. This method is easily interfaced with normal proteolytic processing approaches and provides a reliable and straightforward protein extraction method that efficiently removes soil-borne humic substances without inducing proteome sample loss or biasing protein identification in mass spectrometry. In general, this humic acid removal step is universal and can be adopted by any workflow to effectively remove humic acids to avoid them negatively competing with peptides for binding with reversed-phase resin or ionization in the electrospray

Quantifying the Structure of Water and Hydrated Monovalent Ions by Density Functional Theory-Based Molecular Dynamics

The accurate description of the structures of water and hydrated ions is important in electrochemical desalination, ion separation, and supercapacitors. In this work, we present an ab initio atomistic simulation-based study to explore the structure of water and hydrated monovalent ions (Li+, Na+, K+, Rb+, F–, and Cl–) at ambient conditions using generalized gradient approximation (GGA)-based methods with and without van der Waals correction (PBE, PBE + D3, and revPBE + D3) and recently developed strongly constrained and appropriately normed (SCAN) meta-GGA. We find that both revPBE + D3 and SCAN can well capture the structure of bulk water with +30 K artificial high temperature in contrast to overstructuring water using PBE and PBE + D3. However, being the same as PBE + D3, revPBE + D3 overestimates the structure of the hydration shell, especially for monovalent cations. Surprisingly, SCAN can well match the experimental results of hydrated monovalent ions. Detailed structure analyzes of entropy reveal that the hydration shell under the level of PBE + D3 and revPBE + D3 is more disordered and looser than SCAN. The successful prediction of the flexible SCAN functional could facilitate the exploration of complex ionic processes in the aqueous phase, the interactions of hydrated ions with surfaces, and solvation states in nanopores at an accurate, efficient, predictive, and ab initio level

I/O throughput of Bodytrack with an HDD.

<p><b>(a)</b> Fixed numbers of threads. <b>(b)</b> IOPA.</p

I/O throughput of Bzip2 with an HDD.

<p><b>(a)</b> Fixed numbers of threads. <b>(b)</b> IOPA.</p

IOPA system architecture.

<p>IOPA system architecture.</p

Execution time of benchmark applications with HDD.

<p>Execution time of benchmark applications with HDD.</p

Performances with an SSD and an HDD while varying the numbers of threads.

<p>(a) SSD (solid state drive).(b) HDD (hard disk drive).</p

I/O throughput of microbenchmarks with an SSD.

<p><b>(a)</b> File size: 10 KB files. <b>(b)</b> File size: 50 KB. (c) File size: 100 KB.</p

Example program and function call relationships of IOPA.

<p>Example program and function call relationships of IOPA.</p

I/O throughput of Bodytrack with an SSD.

<p><b>(a)</b> Fixed numbers of threads. <b>(b)</b> IOPA.</p