Species classifier choice is a key consideration when analysing low-complexity food microbiome data

peer-reviewedBackground The use of shotgun metagenomics to analyse low-complexity microbial communities in foods has the potential to be of considerable fundamental and applied value. However, there is currently no consensus with respect to choice of species classification tool, platform, or sequencing depth. Here, we benchmarked the performances of three high-throughput short-read sequencing platforms, the Illumina MiSeq, NextSeq 500, and Ion Proton, for shotgun metagenomics of food microbiota. Briefly, we sequenced six kefir DNA samples and a mock community DNA sample, the latter constructed by evenly mixing genomic DNA from 13 food-related bacterial species. A variety of bioinformatic tools were used to analyse the data generated, and the effects of sequencing depth on these analyses were tested by randomly subsampling reads. Results Compositional analysis results were consistent between the platforms at divergent sequencing depths. However, we observed pronounced differences in the predictions from species classification tools. Indeed, PERMANOVA indicated that there was no significant differences between the compositional results generated by the different sequencers (p = 0.693, R2 = 0.011), but there was a significant difference between the results predicted by the species classifiers (p = 0.01, R2 = 0.127). The relative abundances predicted by the classifiers, apart from MetaPhlAn2, were apparently biased by reference genome sizes. Additionally, we observed varying false-positive rates among the classifiers. MetaPhlAn2 had the lowest false-positive rate, whereas SLIMM had the greatest false-positive rate. Strain-level analysis results were also similar across platforms. Each platform correctly identified the strains present in the mock community, but accuracy was improved slightly with greater sequencing depth. Notably, PanPhlAn detected the dominant strains in each kefir sample above 500,000 reads per sample. Again, the outputs from functional profiling analysis using SUPER-FOCUS were generally accordant between the platforms at different sequencing depths. Finally, and expectedly, metagenome assembly completeness was significantly lower on the MiSeq than either on the NextSeq (p = 0.03) or the Proton (p = 0.011), and it improved with increased sequencing depth. Conclusions Our results demonstrate a remarkable similarity in the results generated by the three sequencing platforms at different sequencing depths, and, in fact, the choice of bioinformatics methodology had a more evident impact on results than the choice of sequencer did.This research was funded by Science Foundation Ireland in the form of a centre grant (APC Microbiome Institute grant number SFI/12/RC/2273). Research in the Cotter laboratory is also funded by Science Foundation Ireland through the PI award “Obesibiotics” (11/PI/1137). Orla O’Sullivan is funded by Science Foundation Ireland through a Starting Investigator Research Grant award (13/SIRG/2160)

The Sensitivity and Psychometric Properties of a Brief Computer-Based Cognitive Screening Battery in a Depression Clinic

At present, there is poor accuracy in assessing cognitive and vegetative symptoms in depression using clinician or self-rated measures, suggesting the need for development of standardized tasks to assess these functions. The current study assessed the psychometric properties and diagnostic specificity of a brief neuropsychological screening battery designed to assess core signs of depression; psychomotor retardation, attention and executive functioning difficulties, and impaired emotion perception within an outpatient psychiatry setting. Three hundred eighty-four patients with mood disorders and 77 healthy volunteers participated. A large percentage of patients met diagnostic criteria for Major Depressive Disorder alone (49%) or with another comorbid psychiatric disorder (24%). A brief, 25-min battery of computer-based tests was administered to control participants and patients measuring the constructs of inhibitory control, attention, visual perception, and both executive and visual processing speed. The patient groups performed significantly worse than the control group regardless of diagnosis on visual perception and attention accuracy and processing speed factors. Surprisingly, the anxiety disorder group performed better than several other psychiatric disorder groups in inhibitory control accuracy. Developing valid and reliable measures of cognitive signs in mood disorders creates excellent opportunities for tracking cognitive status prior to initiation of treatment, and allows for reliable retest following treatment

Microbial Succession and Flavor Production in the Fermented Dairy Beverage Kefir

peer-reviewedKefir is a putatively health-promoting dairy beverage that is produced when a kefir grain, consisting of a consortium of microorganisms, is added to milk to initiate a natural fermentation. Here, a detailed analysis was carried out to determine how the microbial population, gene content, and flavor of three kefirs from distinct geographic locations change over the course of 24-h fermentations. Metagenomic sequencing revealed that Lactobacillus kefiranofaciens was the dominant bacterial species in kefir during early stages of fermentations but that Leuconostoc mesenteroides became more prevalent in later stages. This pattern is consistent with an observation that genes involved in aromatic amino acid biosynthesis were absent from L. kefiranofaciens but were present in L. mesenteroides. Additionally, these shifts in the microbial community structure, and associated pathways, corresponded to changes in the levels of volatile compounds. Specifically, Acetobacter spp. correlated with acetic acid; Lactobacillus spp. correlated with carboxylic acids, esters and ketones; Leuconostoc spp. correlated with acetic acid and 2,3-butanedione; and Saccharomyces spp. correlated with esters. The correlation data suggest a causal relationship between microbial taxa and flavor that is supported by observations that addition of L. kefiranofaciens NCFB 2797 increased the levels of esters and ketones whereas addition of L. mesenteroides 213M0 increased the levels of acetic acid and 2,3-butanedione. Finally, we detected genes associated with probiotic functionalities in the kefir microbiome. Our results illustrate the dynamic nature of kefir fermentations and microbial succession patterns therein and can be applied to optimize the fermentation processes, flavors, and health-related attributes of this and other fermented foods. IMPORTANCE Traditional fermented foods represent relatively low-complexity microbial environments that can be used as model microbial communities to understand how microbes interact in natural environments. Our results illustrate the dynamic nature of kefir fermentations and microbial succession patterns therein. In the process, the link between individual species, and associated pathways, with flavor compounds is revealed and several genes that could be responsible for the purported gut health-associated benefits of consuming kefir are identified. Ultimately, in addition to providing an important fundamental insight into microbial interactions, this information can be applied to optimize the fermentation processes, flavors, and health-related attributes of this and other fermented foods

CD39 activity correlates with stage and inhibits platelet reactivity in chronic lymphocytic leukemia

<p>Abstract</p> <p>Background</p> <p>Chronic lymphocytic leukemia (CLL) is characterized by accumulation of mature appearing lymphocytes and is rarely complicated by thrombosis. One possible explanation for the paucity of thrombotic events in these patients may be the presence of the ecto-nucleotidase CD39/NTDPase-1 on the surface of the malignant cells in CLL. CD39 is the major promoter of platelet inhibition <it>in vivo </it>via its metabolism of ADP to AMP. We hypothesize that if CD39 is observed on CLL cells, then patients with CLL may be relatively protected against platelet aggregation and recruitment and that CD39 may have other effects on CLL, including modulation of the disease, via its metabolism of ATP.</p> <p>Methods</p> <p>Normal and malignant lymphocytes were isolated from whole blood from patients with CLL and healthy volunteers. Enzyme activity was measured via radio-TLC assay and expression via FACS. Semi-quantititative RT-PCR for CD39 splice variants and platelet function tests were performed on several samples.</p> <p>Results</p> <p>Functional assays demonstrated that ADPase and ATPase activities were much higher in CLL cells than in total lymphocytes from the normal population on a per cell basis (p-value < 0.00001). CD39 activity was elevated in stage 0–2 CLL compared to stage 3–4 (p < 0.01). FACS of lymphocytes demonstrated CD39 expression on > 90% of normal and malignant B-lymphocytes and ~8% of normal T-lymphocytes. RT-PCR showed increased full length CD39 and splice variant 1.5, but decreased variant 1.3 in CLL cells. Platelet function tests showed inhibition of platelet activation and recruitment to ADP by CLL cells.</p> <p>Conclusion</p> <p>CD39 is expressed and active on CLL cells. Enzyme activity is higher in earlier stages of CLL and decreased enzyme activity may be associated with worsening disease. These results suggest that CD39 may play a role in the pathogenesis of malignancy and protect CLL patients from thrombotic events.</p

Evaluation of Lateglacial temperatures in the Southern Alps of New Zealand based on glacier modelling at Irishman Stream, Ben Ohau Range

Climate proxy records from the middle to high latitude Southern Hemisphere indicate that a Lateglacial (15,000-11,500 years ago) climate reversal, approximately coeval with the Antarctic Cold Reversal (ACR), interrupted a warming trend during deglaciation. In New Zealand, some palaeoclimate proxy records indicate a cool episode during the ACR (ca 14,500-12,500 years ago), while others do not express a significant change in climate. Recently published moraine maps and ages present an opportunity to improve the palaeoclimate interpretation through numerical modelling of glaciers. We use a coupled energy-balance and ice-flow model to quantify palaeoclimate from past glacier extent constrained by mapped and dated moraines in the headwaters of Irishman Stream, a high-elevation catchment in the Southern Alps. First, a suite of steady-state model runs is used to identify the temperature and precipitation forcing required to fit the modelled glacier to well-dated Lateglacial moraine crests. Second, time dependent glacier simulations forced by a nearby proxy temperature record derived from chironomids are used to assess the fit with the glacial geomorphic record. Steady-state experiments using an optimal parameter set demonstrate that the conditions under which the 13,000 year old moraine formed were 2.3-3.2 *C colder than present with the range in temperature corresponding to a +/- 20% variance in precipitation relative to the present-day. This reconstructed climate change relative to the present-day corresponds to an equilibrium-line altitude of ca 2000 +/- 40 m above sea level (asl), which is ca 400 m lower than present. Time-dependent simulations of glacier length produce ice advance to within 100 m of the 13,000 year old terminal moraine, indicating that the chironomid-based temperature forcing and moraine record provide consistent information about past climate. Our results, together with other climate proxy reconstructions from pollen records and marine sediment cores, support the notion that temperatures during the ACR in New Zealand were ~2-3 *C cooler than today

Kefir ameliorates specific microbiota-gut-brain axis impairments in a mouse model relevant to autism spectrum disorder.

Abstract Autism spectrum disorder (ASD) is one of the most severe developmental disorders, affecting on average 1 in 150 children worldwide. There is a great need for more effective strategies to improve quality of life in ASD subjects. The gut microbiome has emerged as a potential therapeutic target in ASD. A novel modulator of the gut microbiome, the traditionally fermented milk drink kefir, has recently been shown to modulate the microbiota and decrease repetitive behaviour, one of the hallmarks of ASD, in mice. As such, we hypothesized that kefir could ameliorate behavioural deficits in a mouse model relevant to ASD; the BTBR T+ Itpr3tf/J mouse strain. To this end, adult mice were administered either kefir (UK4) or a milk control for three weeks as treatment lead-in, after which they were assessed for their behavioural phenotype using a battery of tests. In addition, we assessed systemic immunity by flow cytometry and the gut microbiome using shotgun metagenomic sequencing. We found that indeed kefir decreased repetitive behaviour in this mouse model. Furthermore, kefir prolonged stress-induced increases in corticosterone 60 min post-stress, which was accompanied by an ameliorated innate immune response as measured by LY6Chi monocyte levels. In addition, kefir increased the levels of anti-inflammatory Treg cells in mesenteric lymph nodes (MLNs). Kefir also increased the relative abundance of Lachnospiraceae bacterium A2, which correlated with reduced repetitive behaviour and increased Treg cells in MLNs. Functionally, kefir modulated various predicted gut microbial pathways, including the gut-brain module S-Adenosylmethionine (SAM) synthesis, as well as L-valine biosynthesis and pyruvate fermentation to isobutanol, which all correlated with repetitive behaviour. Taken together our data show that kefir modulates peripheral immunoregulation, can ameliorate specific ASD behavioural dysfunctions and modulates selective aspects of the composition and function of the gut microbiome, indicating that kefir supplementation might prove a viable strategy in improving quality of life in ASD subjects

Biogenic carbonate mercury and marine temperature records reveal global influence of Late Cretaceous Deccan Traps

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155784/1/Meyer_et_al_2019_Biogenic_carbonate_mercury.pd

Deep machine learning provides state-of-the art performance in image-based plant phenotyping

Deep learning is an emerging field that promises unparalleled results on many data analysis problems. We show the success offered by such techniques when applied to the challenging problem of image-based plant phenotyping, and demonstrate state-of-the-art results for root and shoot feature identification and localisation. We predict a paradigm shift in image-based phenotyping thanks to deep learning approaches