In the era when large whole genome bacterial datasets are generated routinely, rapid and accurate molecular systematics is becoming increasingly important. However, 16S ribosomal RNA sequencing does not always offer sufficient resolution to discriminate between closely related genera. The SsgA-like proteins are developmental regulatory proteins in sporulating actinomycetes, whereby SsgB actively recruits FtsZ during sporulation-specific cell division. Here, we present a novel method to classify actinomycetes, based on the extraordinary way the SsgA and SsgB proteins are conserved. The almost complete conservation of the SsgB amino acid (aa) sequence between members of the same genus and its high divergence between even closely related genera provides high-quality data for the classification of morphologically complex actinomycetes. Our analysis validates Kitasatospora as a sister genus to Streptomyces in the family Streptomycetaceae and suggests that Micromonospora, Salinispora and Verrucosispora may represent different clades of the same genus. It is also apparent that the aa sequence of SsgA is an accurate determinant for the ability of streptomycetes to produce submerged spores, dividing the phylogenetic tree of streptomycetes into liquid-culture sporulation and no liquid-culture sporulation branches. A new phylogenetic tree of industrially relevant actinomycetes is presented and compared with that based on 16S rRNA sequences.

A B S T R A C TWith mass spectrometry imaging (MSI) on tissue microarrays (TMAs) a large number of biomolecules can be studied for many patients at the same time, making it an attractive tool for biomarker discovery. Here we investigate whether lymph node metastasis can be predicted from MALDI-MSI data. Measurements are performed on TMAs and then filtered based on spectral intensity and the percentage of tumor cells, after which the resulting data for 122 patients is further preprocessed. We assume differences between patients with and without metastasis are expressed in a limited number of features. Two univariate feature selection methods are applied to reduce the dimensionality of the MALDI-MSI data. The selected features are then used in combination with three classifiers. The best classification scores are obtained with a decision tree classifier, which classifies about 72% of patients correctly. Almost all the predictive power comes from a single peak (m/z 718.4). The sensitivity of our classification approach, which can be generically used to search for biomarkers, is investigated using artificially modified data.

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Although tumor hypoxia is associated with tumor aggressiveness and resistance to cancer treatment, many details of hypoxia-induced changes in tumors remain to be elucidated. Mass spectrometry imaging (MSI) is a technique that is well suited to study the biomolecular composition of specific tissue regions, such as hypoxic tumor regions. Here, we investigate the use of pimonidazole as an exogenous hypoxia marker for matrix-assisted laser desorption/ionization (MALDI) MSI. In hypoxic cells, pimonidazole is reduced and forms reactive products that bind to thiol groups in proteins, peptides, and amino acids. We show that a reductively activated pimonidazole metabolite can be imaged by MALDI-MSI in a breast tumor xenograft model. Immunohistochemical detection of pimonidazole adducts on adjacent tissue sections confirmed that this metabolite is localized to hypoxic tissue regions. We used this metabolite to image hypoxic tissue regions and their associated lipid and small molecule distributions with MALDI-MSI. We identified a heterogeneous distribution of 1-methylnicotinamide and acetylcarnitine, which mostly colocalized with hypoxic tumor regions. As pimonidazole is a widely used immunohistochemical marker of tissue hypoxia, it is likely that the presented direct MALDI-MSI approach is also applicable to other tissues from pimonidazole-injected animals or humans.

The increasing analytical speed of mass-spectrometry imaging (MSI) has led to growing interest in the medical field. Acute kidney injury is a severe disease with high morbidity and mortality. No reliable cut-offs are known to estimate the severity of acute kidney injury. Thus, there is a need for new tools to rapidly and accurately assess acute ischemia, which is of clinical importance in intensive care and in kidney transplantation. We investigated the value of MSI to assess acute ischemic kidney tissue in a porcine model. A perfusion model was developed where paired kidneys received warm (severe) or cold (minor) ischemia (n = 8 per group). First, ischemic tissue damage was systematically assessed by two blinded pathologists. Second, MALDI-MSI of kidney tissues was performed to study the spatial distributions and compositions of lipids in the tissues. Histopathological examination revealed no significant difference between kidneys, whereas MALDI-MSI was capable of a detailed discrimination of severe and mild ischemia by differential expression of characteristic lipid-degradation products throughout the tissue within 2 h. In particular, lysolipids, including lysocardiolipins, lysophosphatidylcholines, and lysophosphatidylinositol, were dramatically elevated after severe ischemia. This study demonstrates the significant potential of MSI to differentiate and identify molecular patterns of early ischemic injury in a clinically acceptable time frame. The observed changes highlight the underlying biochemical processes of acute ischemic kidney injury and provide a molecular classification tool that can be deployed in assessment of acute ischemic kidney injury.