Application of Singular Spectrum Analysis (SSA), Independent Component Analysis (ICA) and Empirical Mode Decomposition (EMD) for automated solvent suppression and automated baseline and phase correction from multi-dimensional NMR spectra

A common problem on protein structure determination by NMR spectroscopy is due to the solvent artifact. Typically, a deuterated solvent is used instead of normal water. However, several experimental methods have been developed to suppress the solvent signal in the case that one has to use a protonated solvent or if the signals of the remaining protons even in a highly deuterated sample are still too strong. For a protein dissolved in 90% H2O / 10% D2O, the concentration of solvent protons is about five orders of magnitude greater than the concentration of the protons of interest in the solute. Therefore, the evaluation of multi-dimensional NMR spectra may be incomplete since certain resonances of interest (e.g. Hα proton resonances) are hidden by the solvent signal and since signal parts of the solvent may be misinterpreted as cross peaks originating from the protein. The experimental solvent suppression procedures typically are not able to recover these significant protein signals. Many post-processing methods have been designed in order to overcome this problem. In this work, several algorithms for the suppression of the water signal have been developed and compared. In particular, it has been shown that the Singular Spectrum Analysis (SSA) can be applied advantageously to remove the solvent artifact from NMR spectra of any dimensionality both digitally and analogically acquired. In particular, the investigated time domain signals (FIDs) are decomposed into water and protein related components by means of an initial embedding of the data in the space of time-delayed coordinates. Eigenvalue decomposition is applied on these data and the component with the highest variance (typically represented by the dominant solvent signal) is neglected before reverting the embedding. Pre-processing (group delay management and signal normalization) and post-processing (inverse normalization, Fourier transformation and phase and baseline corrections) of the NMR data is mandatory in order to obtain a better performance of the suppression. The optimal embedding dimension has been empirically determined in accordance to a specific qualitative and quantitative analysis of the extracted components applied on a back-calculated two-dimensional spectrum of HPr protein from Staphylococcus aureus. Moreover, the investigation of experimental data (three-dimensional 1H13C HCCH-TOCSY spectrum of Trx protein from Plasmodium falciparum and two-dimensional NOESY and TOCSY spectra of HPr protein from Staphylococcus aureus) has revealed the ability of the algorithm to recover resonances hidden underneath the water signal. Pathological diseases and the effects of drugs and lifestyle can be detected from NMR spectroscopy applied on samples containing biofluids (e.g. urine, blood, saliva). The detection of signals of interest in such spectra can be hampered by the solvent as well. The SSA has also been successfully applied to one-dimensional urine, blood and cell spectra. The algorithm for automated solvent suppression has been introduced in the AUREMOL software package (AUREMOL_SSA). It is optionally followed by an automated baseline correction in the frequency domain (AUREMOL_ALS) that can be also used out the former algorithm. The automated recognition of baseline points is differently performed in dependence on the dimensionality of the data. In order to investigate the limitations of the SSA, it has been applied to spectra whose dominant signal is not the solvent (as in case of watergate solvent suppression and in case of back-calculated data not including any experimental water signal) determining the optimal solvent-to-solute ratio. The Independent Component Analysis (ICA) represents a valid alternative for water suppression when the solvent signal is not the dominant one in the spectra (when it is smaller than the half of the strongest solute resonance). In particular, two components are obtained: the solvent and the solute. The ICA needs as input at least as many different spectra (mixtures) as the number of components (source signals), thus the definition of a suitable protocol for generating a dataset of one-dimensional ICA-tailored inputs is straightforward. The ICA has revealed to overcome the SSA limitations and to be able to recover resonances of interest that cannot be detected applying the SSA. The ICA avoids all the pre- and post-processing steps, since it is directly applied in the frequency domain. On the other hand, the selection of the component to be removed is automatically detected in the SSA case (having the highest variance). In the ICA, a visual inspection of the extracted components is still required considering that the output is permutable and scale and sign ambiguities may occur. The Empirical Mode Decomposition (EMD) has revealed to be more suitable for automated phase correction than for solvent suppression purposes. It decomposes the FID into several intrinsic mode functions (IMFs) whose frequency of oscillation decreases from the first to the last ones (that identifies the solvent signal). The automatically identified non-baseline regions in the Fourier transform of the sum of the first IMFs are separately evaluated and genetic algorithms are applied in order to determine the zero- and first-order terms suitable for an optimal phase correction. The SSA and the ALS algorithms have been applied before assigning the two-dimensional NOESY spectrum (with the program KNOWNOE) of the PSCD4-domain of the pleuralin protein in order to increase the number of already existing distance restraints. A new routine to derive 3JHNHα couplings from torsion angles (Karplus relation) and vice versa, has been introduced in the AUREMOL software. Using the newly developed tools a refined three-dimensional structure of the PSCD4-domain could be obtained

Case Report: Role of Ketone Monitoring in Diabetic Ketoacidosis With Acute Kidney Injury: Better Safe Than Sorry

BACKGROUND: Type 1 Diabetes (T1D) is a well-known endocrinological disease in children and adolescents that is characterized by immune-mediated destruction of pancreatic β-cells, leading to partial or total insulin deficiency, with an onset that can be subtle (polydipsia, polyuria, weight loss) or abrupt (Diabetic Keto-Acidosis, hereafter DKA, or, although rarely, Hyperosmolar Hyperglycemic State, hereafter HHS). Severe DKA risk at the onset of T1D has recently significantly increased during the SARS-CoV-2 pandemic with life-threatening complications often due to its management. DKA is marked by low pH (7.3) and bicarbonates (>15 mmol/L) with no or very low ketone bodies. Despite this, ketone monitoring is not universally available, and DKA diagnosis is mainly based on pH and bicarbonates. A proper diagnosis of the right form with main elements (pH, bicarbonates, ketones) is essential to begin the right treatment and to identify organ damage (such as acute kidney injury). CASE PRESENTATIONS: In this series, we describe 3 case reports in which the onset of T1D was abrupt with severe acidosis (pH < 7.1) in the absence of both DKA and HHS. In a further evaluation, all 3 patients showed acute kidney injury, which caused low bicarbonates and severe acidosis without increasing ketone bodies. CONCLUSION: Even if it is not routinely recommended, a proper treatment that included bicarbonates was then started, with a good response in terms of clinical and laboratory values. With this case series, we would like to encourage emergency physicians to monitor ketones, which are diriment for a proper diagnosis and treatment of DKA

"Open Sesame" to the complexity of pattern recognition receptors of myeloid-derived suppressor cells in cancer

Pattern recognition receptors are primitive sensors that arouse a preconfigured immune response to broad stimuli, including nonself pathogen-associated and autologous damage-associated molecular pattern molecules. These receptors are mainly expressed by innate myeloid cells, including granulocytes, monocytes, macrophages, and dendritic cells. Recent investigations have revealed new insights into these receptors as key players not only in triggering inflammation processes against pathogen invasion but also in mediating immune suppression in specific pathological states, including cancer. Myeloid-derived suppressor cells are preferentially expanded in many pathological conditions. This heterogeneous cell population includes immunosuppressive myeloid cells that are thought to be associated with poor prognosis and impaired response to immune therapies in various cancers. Identification of pattern recognition receptors and their ligands increases the understanding of immune-activating and immune-suppressive myeloid cell functions and sheds light on myeloid-derived suppressor cell differences from cognate granulocytes and monocytes in healthy conditions. This review summarizes the different expression, ligand recognition, signaling pathways, and cancer relations and identifies Toll-like receptors as potential new targets on myeloid-derived suppressor cells in cancer, which might help us to decipher the instruction codes for reverting suppressive myeloid cells toward an antitumor phenotype

A Complex Metabolic Network Confers Immunosuppressive Functions to Myeloid-Derived Suppressor Cells (MDSCs) within the Tumour Microenvironment

Myeloid-derived suppressor cells (MDSCs) constitute a plastic and heterogeneous cell population among immune cells within the tumour microenvironment (TME) that support cancer progression and resistance to therapy. During tumour progression, cancer cells modify their metabolism to sustain an increased energy demand to cope with uncontrolled cell proliferation and differentiation. This metabolic reprogramming of cancer establishes competition for nutrients between tumour cells and leukocytes and most importantly, among tumour-infiltrating immune cells. Thus, MDSCs that have emerged as one of the most decisive immune regulators of TME exhibit an increase in glycolysis and fatty acid metabolism and also an upregulation of enzymes that catabolise essential metabolites. This complex metabolic network is not only crucial for MDSC survival and accumulation in the TME but also for enhancing immunosuppressive functions toward immune effectors. In this review, we discuss recent progress in the field of MDSC-associated metabolic pathways that could facilitate therapeutic targeting of these cells during cancer progression

The Engagement Between MDSCs and Metastases: Partners in Crime

Tumor metastases represent the major cause of cancer-related mortality, confirming the urgent need to identify key molecular pathways and cell-associated networks during the early phases of the metastatic process to develop new strategies to either prevent or control distal cancer spread. Several data revealed the ability of cancer cells to establish a favorable microenvironment, before their arrival in distant organs, by manipulating the cell composition and function of the new host tissue where cancer cells can survive and outgrow. This predetermined environment is termed \u201cpre-metastatic niche\u201d (pMN). pMN development requires that tumor-derived soluble factors, like cytokines, growth-factors and extracellular vesicles, genetically and epigenetically re-program not only resident cells (i.e., fibroblasts) but also non-resident cells such as bone marrow-derived cells. Indeed, by promoting an \u201cemergency\u201d myelopoiesis, cancer cells switch the steady state production of blood cells toward the generation of pro-tumor circulating myeloid cells defined as myeloid-derived suppressor cells (MDSCs) able to sustain tumor growth and dissemination. MDSCs are a heterogeneous subset of myeloid cells with immunosuppressive properties that sustain metastatic process. In this review, we discuss current understandings of how MDSCs shape and promote metastatic dissemination acting in each fundamental steps of cancer progression from primary tumor to metastatic disease

The Endless Saga of Monocyte Diversity

Cancer immunotherapy relies on either restoring or activating the function of adaptive immune cells, mainly CD8(+) T lymphocytes. Despite impressive clinical success, cancer immunotherapy remains ineffective in many patients due to the establishment of tumor resistance, largely dependent on the nature of tumor microenvironment. There are several cellular and molecular mechanisms at play, and the goal is to identify those that are clinically significant. Among the hematopoietic-derived cells, monocytes are endowed with high plasticity, responsible for their pro- and anti-tumoral function. Indeed, monocytes are involved in several cancer-associated processes such as immune-tolerance, metastatic spread, neoangiogenesis, and chemotherapy resistance; on the other hand, by presenting cancer-associated antigens, they can also promote and sustain anti-tumoral T cell response. Recently, by high throughput technologies, new findings have revealed previously underappreciated, profound transcriptional, epigenetic, and metabolic differences among monocyte subsets, which complement and expand our knowledge on the monocyte ontogeny, recruitment during steady state, and emergency hematopoiesis, as seen in cancer. The subdivision into discrete monocytes subsets, both in mice and humans, appears an oversimplification, whereas continuum subsets development is best for depicting the real condition. In this review, we examine the evidences sustaining the existence of a monocyte heterogeneity along with functional activities, at the primary tumor and at the metastatic niche. In particular, we describe how tumor-derived soluble factors and cell-cell contact reprogram monocyte function. Finally, we point out the role of monocytes in preparing and shaping the metastatic niche and describe relevant targetable molecules altering monocyte activities. We think that exploiting monocyte complexity can help identifying key pathways important for the treatment of cancer and several conditions where these cells are involved