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The immune suppressive microenvironment affects efficacy of radio-immunotherapy in brain metastasis
(2021)
The tumor microenvironment in brain metastases is characterized by high myeloid cell content associated with immune suppressive and cancer-permissive functions. Moreover, brain metastases induce the recruitment of lymphocytes. Despite their presence, T-cell-directed therapies fail to elicit effective anti-tumor immune responses. Here, we seek to evaluate the applicability of radio- immunotherapy to modulate tumor immunity and overcome inhibitory effects that diminish anti-cancer activity. Radiotherapy- induced immune modulation resulted in an increase in cytotoxic T-cell numbers and prevented the induction of lymphocyte-mediated immune suppression. Radio-immunotherapy led to significantly improved tumor control with prolonged median survival in experi- mental breast-to-brain metastasis. However, long-term efficacy was not observed. Recurrent brain metastases showed accumula- tion of blood-borne PD-L1+ myeloid cells after radio-immunother- apy indicating the establishment of an immune suppressive environment to counteract re-activated T-cell responses. This finding was further supported by transcriptional analyses indicat- ing a crucial role for monocyte-derived macrophages in mediating immune suppression and regulating T-cell function. Therefore, selective targeting of immune suppressive functions of myeloid cells is expected to be critical for improved therapeutic efficacy of radio-immunotherapy in brain metastases.
Nisin-producing Lactococcus lactis strains show a high degree of resistance to the action of nisin, which is based upon expression of the self-protection (immunity) genes nisI, nisF, nisE, and nisG. Different combinations of nisin immunity genes were integrated into the chromosome of a nisin-sensitive Bacillus subtilis host strain under the control of an inducible promoter. For the recipient strain, the highest level of acquired nisin tolerance was achieved after coordinated expression of all four nisin immunity genes. But either the lipoprotein NisI or the ABC transporter-homologous system NisFEG, respectively, were also able to protect the Bacillus host cells. The acquired immunity was specific to nisin and provided no tolerance to subtilin, a closely related lantibiotic. Quantitative in vivo peptide release assays demonstrated that NisFEG diminished the quantity of cell-associated nisin, providing evidence that one role of NisFEG is to transport nisin from the membrane into the extracellular space. NisI solubilized from B. subtilis membrane vesicles and recombinant hexahistidine-tagged NisI from Escherichia coli interacted specifically with nisin and not with subtilin. This suggests a function of NisI as a nisin-intercepting protein.
Good quality data on precipitation are a prerequisite for applications like short-term weather forecasts, medium-term humanitarian assistance, and long-term climate modelling. In Sub-Saharan Africa, however, the meteorological station networks are frequently insufficient, as in the Cuvelai-Basin in Namibia and Angola. This paper analyses six rainfall products (ARC2.0, CHIRPS2.0, CRU-TS3.23, GPCCv7, PERSIANN-CDR, and TAMSAT) with respect to their performance in a crop model (APSIM) to obtain nutritional scores of a household’s requirements for dietary energy and further macronutrients. All products were calibrated to an observed time series using Quantile Mapping. The crop model output was compared against official yield data. The results show that the products (i) reproduce well the Basin’s spatial patterns, and (ii) temporally agree to station records (r = 0.84). However, differences exist in absolute annual rainfall (range: 154 mm), rainfall intensities, dry spell duration, rainy day counts, and the rainy season onset. Though calibration aligns key characteristics, the remaining differences lead to varying crop model results. While the model well reproduces official yield data using the observed rainfall time series (r = 0.52), the products’ results are heterogeneous (e.g., CHIRPS: r = 0.18). Overall, 97% of a household’s dietary energy demand is met. The study emphasizes the importance of considering the differences among multiple rainfall products when ground measurements are scarce.