Towards precision quantification of contamination in metagenomic sequencing experiments.

Metagenomic next-generation sequencing (mNGS) experiments involving small amounts of nucleic acid input are highly susceptible to erroneous conclusions resulting from unintentional sequencing of occult contaminants, especially those derived from molecular biology reagents. Recent work suggests that, for any given microbe detected by mNGS, an inverse linear relationship between microbial sequencing reads and sample mass implicates that microbe as a contaminant. By associating sequencing read output with the mass of a spike-in control, we demonstrate that contaminant nucleic acid can be quantified in order to identify the mass contributions of each constituent. In an experiment using a high-resolution (n = 96) dilution series of HeLa RNA spanning 3-logs of RNA mass input, we identified a complex set of contaminants totaling 9.1 ± 2.0 attograms. Given the competition between contamination and the true microbiome in ultra-low biomass samples such as respiratory fluid, quantification of the contamination within a given batch of biological samples can be used to determine a minimum mass input below which sequencing results may be distorted. Rather than completely censoring contaminant taxa from downstream analyses, we propose here a statistical approach that allows separation of the true microbial components from the actual contribution due to contamination. We demonstrate this approach using a batch of n = 97 human serum samples and note that despite E. coli contamination throughout the dataset, we are able to identify a patient sample with significantly more E. coli than expected from contamination alone. Importantly, our method assumes no prior understanding of possible contaminants, does not rely on any prior collection of environmental or reagent-only sequencing samples, and does not censor potentially clinically relevant taxa, thus making it a generalized approach to any kind of metagenomic sequencing, for any purpose, clinical or otherwise.

JAK/STAT pathway inhibition overcomes IL7-induced glucocorticoid resistance in a subset of human T-cell acute lymphoblastic leukemias.

While outcomes for children with T-cell acute lymphoblastic leukemia (T-ALL) have improved dramatically, survival rates for patients with relapsed/refractory disease remain dismal. Prior studies indicate that glucocorticoid (GC) resistance is more common than resistance to other chemotherapies at relapse. In addition, failure to clear peripheral blasts during a prednisone prophase correlates with an elevated risk of relapse in newly diagnosed patients. Here we show that intrinsic GC resistance is present at diagnosis in early thymic precursor (ETP) T-ALLs as well as in a subset of non-ETP T-ALLs. GC-resistant non-ETP T-ALLs are characterized by strong induction of JAK/STAT signaling in response to interleukin-7 (IL7) stimulation. Removing IL7 or inhibiting JAK/STAT signaling sensitizes these T-ALLs, and a subset of ETP T-ALLs, to GCs. The combination of the GC dexamethasone and the JAK1/2 inhibitor ruxolitinib altered the balance between pro- and anti-apoptotic factors in samples with IL7-dependent GC resistance, but not in samples with IL7-independent GC resistance. Together, these data suggest that the addition of ruxolitinib or other inhibitors of IL7 receptor/JAK/STAT signaling may enhance the efficacy of GCs in a biologically defined subset of T-ALL.