Testing a theoretical framework for the environment-species abundance paradigm: a new approach to the Abundant Centre Hypothesis

The linkage between environment, a species' fitness and its abundance is central to the theory of evolution. So far, all studies of this linkage have been heuristic and empirical due to an inability to determine fitness either experimentally (independent of abundance) or theoretically (from species-environment interaction). One category of such studies involves the Abundant Centre Hypothesis which posits that a species' abundance rises to a maximum at the centre of its range. We argue that the confusing mix of results from ACH studies arises from ignoring the central premise that the abundance distribution cannot be independent of the environment. First, we employed a theoretical framework to identify an environmental context (an elevational transect; 200-2800 m in the eastern Himalayas) likely to favour ACH. We then improved upon some previously identified conceptual and methodological shortcomings of ACH studies. Using systematically collected bird data (245 species; 15867 records) from that transect we found that the community average abundance profile is symmetric, as expected by ACH. Notwithstanding which, the abundance profiles of individual species showed a small degree of asymmetry which was correlated with elevation. This elevational dependence may be due to the hard elevational limits at the lower and upper ends of the mountain, as expected from theoretical considerations. We also showed that the average abundance profile shape is close to gaussian, while ruling out uniform and inverted-quadratic shapes. This work demonstrates that selecting a particular category of environmental contexts can help in integrating theoretical tools into a field dominated by empirical studies. Such a union should spur the development of more detailed and testable theoretical models for better insights in an important field.

Surface proteomics and label-free quantification of Leptospira interrogans serovar Pomona

Leptospirosis is a re-emerging zoonosis with a global distribution. Surface-exposed outer membrane proteins (SE-OMPs) are crucial for bacterial–host interactions. SE-OMPs locate and expose their epitope on cell surface where is easily accessed by host molecules. This study aimed to screen for surface-exposed proteins and their abundance profile of pathogenic Leptospira interrogans serovar Pomona. Two complementary approaches, surface biotinylation and surface proteolytic shaving, followed by liquid chromatography tandem-mass spectrometry (LC-MS/MS) were employed to identify SE-OMPs of intact leptospires. For quantitative comparison, in-depth label-free analysis of SE-OMPs obtained from each method was performed using MaxQuant. The total number of proteins identified was 1,001 and 238 for surface biotinylation and proteinase K shaving, respectively. Among these, 39 were previously known SE-OMPs and 68 were predicted to be localized on the leptospiral surface. Based on MaxQuant analysis for relative quantification, six known SE-OMPs including EF- Tu, LipL21, LipL41, LipL46, Loa22, and OmpL36, and one predicted SE-OMPs, LipL71 were found in the 20 most abundant proteins, in which LipL41 was the highest abundant SE-OMP. Moreover, uncharacterized LIC14011 protein (LIP3228 ortholog in serovar Pomona) was identified as a novel predicted surface βb-OMP. High-abundance leptospiral SE-OMPs identified in this study may play roles in virulence and infection and are potential targets for development of vaccine or diagnostic tests for leptospirosis.

NanoCLUST: a species-level analysis of 16S rRNA nanopore sequencing data

Summary NanoCLUST is an analysis pipeline for the classification of amplicon-based full-length 16S rRNA nanopore reads. It is characterized by an unsupervised read clustering step, based on Uniform Manifold Approximation and Projection (UMAP), followed by the construction of a polished read and subsequent Blast classification. Here, we demonstrate that NanoCLUST performs better than other state-of-the-art software in the characterization of two commercial mock communities, enabling accurate bacterial identification and abundance profile estimation at species-level resolution. Availability and implementation Source code, test data and documentation of NanoCLUST are freely available at https://github.com/genomicsITER/NanoCLUST under MIT License. Supplementary information Supplementary data are available at Bioinformatics online.

Impact of varied combinatorial mixture of non-fishmeal ingredients on growth, metabolism, immunity and gut microbiota of Lates calcarifer (Bloch, 1790) fry

The search for suitable fish meal replacements in aqua-diets is a salient agenda in the constant effort of making aquaculture practices more sustainable. In this study, we tested four customised diets composed by systematic inclusion of pre-selected fish meal substitutes, lupin kernel meal, BSF meal, TH and PBM on growth, metabolism, cytokine profile, gut morphology and microbiota of juvenile Lates calcarifer. Five isoproteic and isoenergetic diets were prepared viz. FM100 as a control (without fish meal substitute), while FM75, FM50, FM25 and FM0 indicates replacement of fish meal (FM) at 25%, 50%, 75%, and 100%, respectively by a mixture of four different pre-selected non-fish meal (NFM) ingredients. Fish fed FM100, FM75, FM50, FM25 exhibited consistent growth and haematological response, while the fish fed no fishmeal (FM0) showed significant decline in final body weight (FBW) and specific growth rate (SGR). The poor growth performance was correlated with a decrease in villous width, microvilli height and goblet cells density. A significant shift in abundance profile of Psychrobacter in the gut microbial profile of fish fed FM50 was noticed compared to fish fed FM100. The results of qRT-PCR showed up-regulated expression of innate immune responsive genes in the FM50 group. The adverse impacts on growth performance and gut health of fish fed FM0 suggest that the complete substitution of fishmeal is not advisable and the inclusion range of these alternatives should be decided for a species only after examining their effect on maximal physiological performance.

Novel Approach for Microbiome Analysis Using Bacterial Replication Rates and Causal Inference with Applications

Motivation: Metagenomics sequencing data can be used to compute not just the relative abundance profile, but also the replication rates of every taxon in the microbiome sample. We investigate how the dynamics implied by the replication rates can be used to understand the antibiotic response in microbiomes, given the significant variation in the types of antibiotics and the types of response by different taxa. The analysis is further expanded by factoring in the resistome of the microbiomes, which can be readily profiled from the metagenomic sequence data. The fact that some antibiotics such as β -lactams target replicating cells makes it even more critical to use replication rates to analyze the antibiotic response. Results: We introduce a novel approach for metagenomic analysis that integrates microbial community profiling, replication rate calculation, and causal structural learning to analyze the antibiotic response. First, we developed PeTRi, which involves efficient cluster computation of bacterial replication rates from metagenomic sequence data. Second, we integrate the abundance profile, replication profile, resistome profile, and environmental variables to perform causality analysis. Finally, we applied the integrated analysis to the data from an infant gut microbiome study. Conclusions from our analysis are as follows: (i) Microbes tend to lower their replication rates in response to β -lactams; (ii) The presence of antibiotic resistance genes combined with the causality analysis strongly suggest that genes fosA5, oqxA, kpnF, arnA, and acrA provides resistance for the taxon K. pneumoniae, allowing it to replicate and dominate the microbiome after the drug ticarcillin-clavulanate was administered; and (iii) Human and donor milk strongly influence the resistome of the infant gut microbiome.

NanoCLUST: a species-level analysis of 16S rRNA nanopore sequencing data

SummaryNanoCLUST is an analysis pipeline for classification of amplicon-based full-length 16S rRNA nanopore reads. It is characterized by an unsupervised read clustering step, based on Uniform Manifold Approximation and Projection (UMAP), followed by the construction of a polished read and subsequent Blast classification. Here we demonstrate that NanoCLUST performs better than other state-of-the-art software in the characterization of two commercial mock communities, enabling accurate bacterial identification and abundance profile estimation at species level resolution.Availability and implementationSource code, test data and documentation of NanoCLUST is freely available at https://github.com/genomicsITER/NanoCLUST under MIT [email protected]

SHOGUN: a modular, accurate and scalable framework for microbiome quantification

Summary The software pipeline SHOGUN profiles known taxonomic and gene abundances of short-read shotgun metagenomics sequencing data. The pipeline is scalable, modular and flexible. Data analysis and transformation steps can be run individually or together in an automated workflow. Users can easily create new reference databases and can select one of three DNA alignment tools, ranging from ultra-fast low-RAM k-mer-based database search to fully exhaustive gapped DNA alignment, to best fit their analysis needs and computational resources. The pipeline includes an implementation of a published method for taxonomy assignment disambiguation with empirical Bayesian redistribution. The software is installable via the conda resource management framework, has plugins for the QIIME2 and QIITA packages and produces both taxonomy and gene abundance profile tables with a single command, thus promoting convenient and reproducible metagenomics research. Availability and implementation https://github.com/knights-lab/SHOGUN.

The chemical evolution of galaxy clusters: Dissecting the iron mass budget of the intracluster medium

Aims. We study the chemical evolution of galaxy clusters by measuring the iron mass in the ICM after dissecting the abundance profiles into different components. Methods. We used Chandra archival observations of 186 morphologically regular clusters in the redshift range of [0.04, 1.07]. For each cluster, we computed the azimuthally averaged iron abundance and gas density profiles. In particular, our aim is to identify a central peak in the iron distribution, which is associated with the central galaxy, and an approximately constant plateau reaching the largest observed radii, which is possibly associated with early enrichment that occurred before or shortly after achieving virialization within the cluster. We were able to firmly identify two components in the iron distribution in a significant fraction of the sample simply by relying on the fit of the iron abundance profile. From the abundance and ICM density profiles, we computed the iron mass included in the iron peak and iron plateau, and the gas mass-weighted iron abundance of the ICM out to an extraction radius of 0.4r500 and to r500 by extending the abundance profile as a constant. Results. We find that the iron plateau shows no evolution with redshift. On the other hand, we find a marginal (< 2σ c.l.) decrease with redshift in the iron mass included in the iron peak rescaled by the gas mass. We measure that the fraction of iron peak mass is typically a few percent (∼1%) of the total iron mass within r500. Therefore, since the total iron mass budget is dominated by the plateau, we find consistently that the global gas mass-weighted iron abundance does not evolve significantly across our sample. We were also able to reproduce past claims of evolution in the global iron abundance, which turn out to be due to the use of cluster samples with different selection methods combined with the use of emission-weighted, instead of gas mass-weighted, abundance values. Finally, while the intrinsic scatter in the iron plateau mass is consistent with zero, the iron peak mass exhibits a large scatter, in line with the fact that the peak is produced after the virialization of the halo and depends on the formation history of the hosting cool core and the strength of the associated feedback processes. Conclusions. We conclude that only a spatially resolved approach can resolve the issue of iron abundance evolution in the ICM, reconciling the contradictory results obtained in the last ten years. Evolutionary effects below z ∼ 1 are marginally measurable with present-day data, while at z > 1 the constraints are severely limited by poor knowledge of the high-z cluster population. The path towards a full and comprehensive chemical history of the ICM requires the application of high angular resolution X-ray bolometers and a dramatic increase in the number of faint, extended X-ray sources.

Quantifying the contribution of microbial immigration in engineered water systems

Immigration is a process that can influence the assembly of microbial communities in natural and engineered environments. However, it remains challenging to quantitatively evaluate the contribution of this process to the microbial diversity and function in the receiving ecosystems. Currently used methods, i.e., counting shared microbial species, microbial source tracking, and neutral community model, rely on abundance profile to reveal the extent of overlapping between the upstream and downstream communities. Thus, they cannot suggest the quantitative contribution of immigrants to the downstream community function because activities of individual immigrants are not considered after entering the receiving environment. This limitation can be overcome by using an approach that couples a mass balance model with high-throughput DNA sequencing, i.e., ecogenomics-based mass balance. It calculates the net growth rate of individual microbial immigrants and partitions the entire community into active populations that contribute to the community function and inactive ones that carry minimal function. Linking activities of immigrants to their abundance further provides quantification of the contribution from an upstream environment to the downstream community. Considering only active populations can improve the accuracy of identifying key environmental parameters dictating process performance using methods such as machine learning.