A standardized quantitative analysis strategy for stable isotope probing metagenomics.

Stable isotope probing (SIP) facilitates culture-independent identification of active microbial populations within complex ecosystems through isotopic enrichment of nucleic acids. Many DNA-SIP studies rely on 16S rRNA gene sequences to identify active taxa, but connecting these sequences to specific bacterial genomes is often challenging. Here, we describe a standardized laboratory and analysis framework to quantify isotopic enrichment on a per-genome basis using shotgun metagenomics instead of 16S rRNA gene sequencing. To develop this framework, we explored various sample processing and analysis approaches using a designed microbiome where the identity of labeled genomes and their level of isotopic enrichment were experimentally controlled. With this ground truth dataset, we empirically assessed the accuracy of different analytical models for identifying active taxa and examined how sequencing depth impacts the detection of isotopically labeled genomes. We also demonstrate that using synthetic DNA internal standards to measure absolute genome abundances in SIP density fractions improves estimates of isotopic enrichment. In addition, our study illustrates the utility of internal standards to reveal anomalies in sample handling that could negatively impact SIP metagenomic analyses if left undetected. Finally, we present SIPmg, an R package to facilitate the estimation of absolute abundances and perform statistical analyses for identifying labeled genomes within SIP metagenomic data. This experimentally validated analysis framework strengthens the foundation of DNA-SIP metagenomics as a tool for accurately measuring the in situ activity of environmental microbial populations and assessing their genomic potential. IMPORTANCE Answering the questions, "who is eating what?" and "who is active?" within complex microbial communities is paramount for our ability to model, predict, and modulate microbiomes for improved human and planetary health. These questions can be pursued using stable isotope probing to track the incorporation of labeled compounds into cellular DNA during microbial growth. However, with traditional stable isotope methods, it is challenging to establish links between an active microorganism's taxonomic identity and genome composition while providing quantitative estimates of the microorganism's isotope incorporation rate. Here, we report an experimental and analytical workflow that lays the foundation for improved detection of metabolically active microorganisms and better quantitative estimates of genome-resolved isotope incorporation, which can be used to further refine ecosystem-scale models for carbon and nutrient fluxes within microbiomes.

HT-SIP: a semi-automated stable isotope probing pipeline identifies cross-kingdom interactions in the hyphosphere of arbuscular mycorrhizal fungi.

BACKGROUND: Linking the identity of wild microbes with their ecophysiological traits and environmental functions is a key ambition for microbial ecologists. Of many techniques that strive for this goal, Stable-isotope probing-SIP-remains among the most comprehensive for studying whole microbial communities in situ. In DNA-SIP, actively growing microorganisms that take up an isotopically heavy substrate build heavier DNA, which can be partitioned by density into multiple fractions and sequenced. However, SIP is relatively low throughput and requires significant hands-on labor. We designed and tested a semi-automated, high-throughput SIP (HT-SIP) pipeline to support well-replicated, temporally resolved amplicon and metagenomics experiments. We applied this pipeline to a soil microhabitat with significant ecological importance-the hyphosphere zone surrounding arbuscular mycorrhizal fungal (AMF) hyphae. AMF form symbiotic relationships with most plant species and play key roles in terrestrial nutrient and carbon cycling. RESULTS: Our HT-SIP pipeline for fractionation, cleanup, and nucleic acid quantification of density gradients requires one-sixth of the hands-on labor compared to manual SIP and allows 16 samples to be processed simultaneously. Automated density fractionation increased the reproducibility of SIP gradients compared to manual fractionation, and we show adding a non-ionic detergent to the gradient buffer improved SIP DNA recovery. We applied HT-SIP to 13C-AMF hyphosphere DNA from a 13CO2 plant labeling study and created metagenome-assembled genomes (MAGs) using high-resolution SIP metagenomics (14 metagenomes per gradient). SIP confirmed the AMF Rhizophagus intraradices and associated MAGs were highly enriched (10-33 atom% 13C), even though the soils' overall enrichment was low (1.8 atom% 13C). We assembled 212 13C-hyphosphere MAGs; the hyphosphere taxa that assimilated the most AMF-derived 13C were from the phyla Myxococcota, Fibrobacterota, Verrucomicrobiota, and the ammonia-oxidizing archaeon genus Nitrososphaera. CONCLUSIONS: Our semi-automated HT-SIP approach decreases operator time and improves reproducibility by targeting the most labor-intensive steps of SIP-fraction collection and cleanup. We illustrate this approach in a unique and understudied soil microhabitat-generating MAGs of actively growing microbes living in the AMF hyphosphere (without plant roots). The MAGs' phylogenetic composition and gene content suggest predation, decomposition, and ammonia oxidation may be key processes in hyphosphere nutrient cycling. Video Abstract.

Transkingdom network reveals bacterial players associated with cervical cancer gene expression program.

Cervical cancer is the fourth most common cancer in women worldwide with human papillomavirus (HPV) being the main cause the disease. Chromosomal amplifications have been identified as a source of upregulation for cervical cancer driver genes but cannot fully explain increased expression of immune genes in invasive carcinoma. Insight into additional factors that may tip the balance from immune tolerance of HPV to the elimination of the virus may lead to better diagnosis markers. We investigated whether microbiota affect molecular pathways in cervical carcinogenesis by performing microbiome analysis via sequencing 16S rRNA in tumor biopsies from 121 patients. While we detected a large number of intra-tumor taxa (289 operational taxonomic units (OTUs)), we focused on the 38 most abundantly represented microbes. To search for microbes and host genes potentially involved in the interaction, we reconstructed a transkingdom network by integrating a previously discovered cervical cancer gene expression network with our bacterial co-abundance network and employed bipartite betweenness centrality. The top ranked microbes were represented by the families Bacillaceae, Halobacteriaceae, and Prevotellaceae. While we could not define the first two families to the species level, Prevotellaceae was assigned to Prevotella bivia. By co-culturing a cervical cancer cell line with P. bivia, we confirmed that three out of the ten top predicted genes in the transkingdom network (lysosomal associated membrane protein 3 (LAMP3), STAT1, TAP1), all regulators of immunological pathways, were upregulated by this microorganism. Therefore, we propose that intra-tumor microbiota may contribute to cervical carcinogenesis through the induction of immune response drivers, including the well-known cancer gene LAMP3.

CVID enteropathy is characterized by exceeding low mucosal IgA levels and interferon-driven inflammation possibly related to the presence of a pathobiont.

Common variable immunodeficiency (CVID), the most common symptomatic primary antibody deficiency, is accompanied in some patients by a duodenal inflammation and malabsorption syndrome known as CVID enteropathy (E-CVID).The goal of this study was to investigate the immunological abnormalities in CVID patients that lead to enteropathy as well as the contribution of intestinal microbiota to this process.We found that, in contrast to noE-CVID patients (without enteropathy), E-CVID patients have exceedingly low levels of IgA in duodenal tissues. In addition, using transkingdom network analysis of the duodenal microbiome, we identified Acinetobacter baumannii as a candidate pathobiont in E-CVID. Finally, we found that E-CVID patients exhibit a pronounced activation of immune genes and down-regulation of epithelial lipid metabolism genes. We conclude that in the virtual absence of mucosal IgA, pathobionts such as A. baumannii, may induce inflammation that re-directs intestinal molecular pathways from lipid metabolism to immune processes responsible for enteropathy.

Interplay between viruses and bacterial microbiota in cancer development.

During the last few decades we have become accustomed to the idea that viruses can cause tumors. It is much less considered and discussed, however, that most people infected with oncoviruses will never develop cancer. Therefore, the genetic and environmental factors that tip the scales from clearance of viral infection to development of cancer are currently an area of active investigation. Microbiota has recently emerged as a potentially critical factor that would affect this balance by increasing or decreasing the ability of viral infection to promote carcinogenesis. In this review, we provide a model of microbiome contribution to the development of oncogenic viral infections and viral associated cancers, give examples of this process in human tumors, and describe the challenges that prevent progress in the field as well as their potential solutions.

Differentially correlated genes in co-expression networks control phenotype transitions.

BACKGROUND: Co-expression networks are a tool widely used for analysis of "Big Data" in biology that can range from transcriptomes to proteomes, metabolomes and more recently even microbiomes. Several methods were proposed to answer biological questions interrogating these networks. Differential co-expression analysis is a recent approach that measures how gene interactions change when a biological system transitions from one state to another. Although the importance of differentially co-expressed genes to identify dysregulated pathways has been noted, their role in gene regulation is not well studied. Herein we investigated differentially co-expressed genes in a relatively simple mono-causal process (B lymphocyte deficiency) and in a complex multi-causal system (cervical cancer). METHODS: Co-expression networks of B cell deficiency (Control and BcKO) were reconstructed using Pearson correlation coefficient for two mus musculus datasets: B10.A strain (12 normal, 12 BcKO) and BALB/c strain (10 normal, 10 BcKO). Co-expression networks of cervical cancer (normal and cancer) were reconstructed using local partial correlation method for five datasets (total of 64 normal, 148 cancer). Differentially correlated pairs were identified along with the location of their genes in BcKO and in cancer networks. Minimum Shortest Path and Bi-partite Betweenness Centrality where statistically evaluated for differentially co-expressed genes in corresponding networks.    Results: We show that in B cell deficiency the differentially co-expressed genes are highly enriched with immunoglobulin genes (causal genes). In cancer we found that differentially co-expressed genes act as "bottlenecks" rather than causal drivers with most flows that come from the key driver genes to the peripheral genes passing through differentially co-expressed genes. Using in vitro knockdown experiments for two out of 14 differentially co-expressed genes found in cervical cancer (FGFR2 and CACYBP), we showed that they play regulatory roles in cancer cell growth. CONCLUSION: Identifying differentially co-expressed genes in co-expression networks is an important tool in detecting regulatory genes involved in alterations of phenotype.