Biology subjects

Morowitz, M. J.

Publications and source records attributed to Morowitz, M. J..

9 recordsLinked to original sources

bioRxiv2026

Metagenomic strain-resolved DNA modification patterns link extrachromosomal genetic elements to host strains

DNA modification is central to microbial defense against extrachromosomal genetic elements (ECEs), consequently ECEs tend to adopt their hosts modification patterns. Shared ECE-host modification patterns enable linking ECEs to their hosts, but modification detection tools are designed for single genomes and are ineffective at metagenome scale. Here, we present MODIFI, software for detecting DNA modifications in metagenomes. MODIFI assumes that each k-mer in a metagenome is mostly unmodified and calculates background signal levels for that k-mer from PacBio HiFi reads, eliminating the need for matched control experiments. MODIFI ECE-host linkages were validated using >1,000 isolate and mock microbiome datasets. Illustrating the approach, we identified 315 strain-resolved, non-redundant ECE-host linkages in environmental and human metagenomes. In infant gut microbiomes, a chromosomal inversion in Enterococcus faecalis alters host and associated plasmid methylation motifs simultaneously. Overall, MODIFI solves a major bottleneck in DNA modification analysis and provides a foundational tool for understanding microbial epigenomics.

microbiology↗

bioRxiv2022

Using strain-resolved analysis to identify contamination in metagenomics data

Metagenomics analyses can be negatively impacted by DNA contamination. While external sources of contamination such as DNA extraction kits have been widely reported and investigated, contamination originating within the study itself remains underreported. Here we applied high-resolution strain-resolved analyses to identify contamination in two large-scale clinical metagenomics datasets. By mapping strain sharing to DNA extraction plates, we identified well-to-well contamination in both negative controls and biological samples in one dataset. Such contamination is more likely to occur among samples that are on the same or adjacent columns or rows of the extraction plate than samples that are far apart. Our strain-resolved workflow also reveals the presence of externally derived contamination, primarily in the other dataset. Overall in both datasets, contamination is more significant in samples with lower biomass. Our work demonstrates that genome-resolved strain tracking, with its essentially genome-wide nucleotide-level resolution, can be used to detect contamination in sequencing-based microbiome studies. Our results underscore the value of strain-specific methods to detect contamination and the critical importance of looking for contamination beyond negative and positive controls.

microbiology↗

bioRxiv2020

Genetic and behavioral adaptation of Candida parapsilosis to the microbiome of hospitalized infants revealed by in situ genomics, transcriptomics and proteomics

Candida parapsilosis is a common cause of invasive candidiasis, especially in newborn infants, and infections have been increasing over the past two decades. C. parapsilosis has been primarily studied in pure culture, leaving gaps in understanding of its function in microbiome context. Here, we reconstructed five unique C. parapsilosis genomes from premature infant fecal samples and analyzed their genome structure, population diversity and in situ activity relative to reference strains in pure culture. All five genomes contain hotspots of single nucleotide variants, some of which are shared by strains from multiple hospitals. A subset of environmental and hospital-derived genomes share variants within these hotspots suggesting derivation of that region from a common ancestor. Four of the newly reconstructed C. parapsilosis genomes have four to sixteen copies of the gene RTA3, which encodes a lipid translocase and is implicated in antifungal resistance, potentially indicating adaptation to hospital antifungal use. Time course metatranscriptomics and metaproteomics on fecal samples from a premature infant with a C. parapsilosis blood infection revealed highly variable in situ expression patterns that are distinct from those of similar strains in pure cultures. For example, biofilm formation genes were relatively less expressed in situ, whereas genes linked to oxygen utilization were more highly expressed, indicative of growth in a relatively aerobic environment. In gut microbiome samples, C. parapsilosis coexisted with Enterococcus faecalis that shifted in relative abundance over time, accompanied by changes in bacterial and fungal gene expression and proteome composition. The results reveal potentially medically relevant differences in Candida function in gut vs. laboratory environments, and constrain evolutionary processes that could contribute to hospital strain persistence and transfer into premature infant microbiomes.

microbiology↗

bioRxiv2019

Necrotizing enterocolitis is preceded by increased gut bacterial replication, Klebsiella, and fimbriae-encoding bacteria that may stimulate TLR4 receptors

Necrotizing enterocolitis (NEC) is a devastating intestinal disease that occurs primarily in premature infants. We performed genome-resolved metagenomic analysis of 1,163 fecal samples from premature infants to identify microbial features predictive of NEC. Features considered include genes, bacterial strain types, eukaryotes, bacteriophages, plasmids and growth rates. A machine learning classifier found that samples collected prior to NEC diagnosis harbored significantly more Klebsiella, bacteria encoding fimbriae, and bacteria encoding secondary metabolite gene clusters related to quorum sensing and bacteriocin production. Notably, replication rates of all bacteria, especially Enterobacteriaceae, were significantly higher two days before NEC diagnosis. The findings uncover biomarkers that could lead to early detection of NEC and targets for microbiome-based therapeutics.

microbiology↗

bioRxiv2019

Functional potential of bacterial strains in the premature infant gut microbiome is associated with gestational age

The gut microbiota of premature and full-term infants have many known differences, but the extent to which the degree of prematurity influences the structure and functional potential of the microbiome has not been deeply explored. Here, we used genome-resolved metagenomics to address how gestational age impacts the premature infant gut microbiome. We found that gestational age is associated with species richness, with more premature infants having lower species richness; this effect lasts until the fourth week of life. Novel Clostridium species and strains related to Streptococcus salivarius and Enterococcus faecalis colonize infants of different gestational ages, and the metabolic potential of these organisms can be distinguished. Thus, we conclude that the extent of prematurity, or directly linked factors, can be an important influence on the microbiome and its functions.

microbiology↗

bioRxiv2018

Strain-level overlap between infant and hospital fungal microbiomes revealed through de novo assembly of eukaryotic genomes from metagenomes

Eukaryotes are a leading cause of nosocomial infections in neonates, but their diversity and population heterogeneity are rarely investigated. This has led to an incomplete understanding of eukaryotic strains that colonize infants and of the neonatal intensive care unit (NICU) as a possible source of these strains. Analysis of 1,174 time-series metagenomes from 161 premature infants revealed fungal colonization of 13 infants, primarily in the first two weeks of life. Nearly all 24 NICU samples contained eukaryotes, and the most diverse communities were in NICU sinks. Five of fourteen newly-assembled eukaryotic genomes derived from genomically undescribed species. Purpureocillium lilacinum genomes from infant and NICU samples shared 99.999% average nucleotide identity, highlighting the potential of hospital-associated fungi to colonize hospitalized infants. We quantified zygosity and within-population variation associated with the diploid eukaryotes, and thus defined the genetic reservoirs of eukaryotes in room environments and infants.

microbiology↗

bioRxiv2018

The developing premature infant gut microbiome is a major factor shaping the microbiome of neonatal intensive care unit rooms

1.1BackgroundThe neonatal intensive care unit (NICU) contains a unique cohort of patients with underdeveloped immune systems and nascent microbiome communities. Patients often spend several months in the same room and it has been previously shown that the gut microbiomes of these infants often resemble the microbes found in the NICU. Little is known, however, about the identity, persistence and absolute abundance of NICU room-associated bacteria over long stretches of time. Here we couple droplet digital PCR (ddPCR), 16S rRNA gene surveys, and recently published metagenomics data from infant gut samples to infer the extent to which the NICU microbiome is shaped by its room occupants.\n\nResultsOver 2,832 swabs, wipes, and air samples were collected from sixteen private-style NICU rooms housing very low birthweight (<1,500 g), premature (<31 weeks gestation) infants. For each infant, room samples were collected daily, Monday through Friday, for one month. The first samples from the first infant and last samples from the last infant were collected 383 days apart. Twenty-two NICU locations spanning room surfaces, hands, electronics, sink basins, and air were collected. Results point to an incredibly simple room community where 5-10 taxa, mostly skin associated, account for over 50% of 16S reads. Biomass estimates reveal 4-5 orders of magnitude difference between the least to the most dense microbial communities, air and sink basins, respectively. Biomass trends from bioaerosol samples and petri dish dust collectors suggest occupancy to be a main driver of suspended biological particles within the NICU. Using a machine learning algorithm to classify the origin of room samples, we show that each room has a unique microbial fingerprint. Several important taxa driving this model were dominant gut colonizers of infants housed within each room.\n\nConclusionsDespite regular cleaning of hospital surfaces, bacterial biomass was detectable at varying densities. A room specific microbiome signature was detected, suggesting microbes seeding NICU surfaces are sourced from reservoirs within the room and that these reservoirs contain actively dividing cells. Collectively, the data suggests that hospitalized infants, in combination with their caregivers, shape the microbiome of NICU rooms.

microbiology↗

bioRxiv2017

Hospitalized premature infants are colonized by related bacterial strains with distinct proteomic profiles

During the first weeks of life, microbial colonization of the gut impacts human immune system maturation and other developmental processes. In premature infants, aberrant colonization has been implicated in the onset of necrotizing enterocolitis (NEC), a life-threatening intestinal disease. To study the premature infant gut colonization process, genome-resolved metagenomics was conducted on 343 fecal samples collected during the first three months of life from 35 premature infants housed in a neonatal intensive care unit, 14 of which developed NEC, and metaproteomic measurements were made on 87 samples. Microbial community composition and proteomic profiles remained relatively stable on the time scale of a week, but the proteome was more variable. Although genetically similar organisms colonized many infants, most infants were colonized by distinct strains with metabolic profiles that could be distinguished using metaproteomics. Microbiome composition correlated with infant, antibiotics administration, and NEC diagnosis. Communities were found to cluster into seven primary types, and community type switched within infants, sometimes multiple times. Interestingly, some communities sampled from the same infant at subsequent time points clustered with those of other infants. In some cases, switches preceded onset of NEC; however, no species or community type could account for NEC across the majority of infants. In addition to a correlation of protein abundances with organism replication rates, we found that organism proteomes correlated with overall community composition. Thus, this genome-resolved proteomics study demonstrates that the contributions of individual organisms to microbiome development depend on microbial community context.\n\nImportance\n\nHumans are colonized by microbes at birth, a process that is important to health and development. However, much remains to be known about the fine-scale microbial dynamics that occur during the colonization period. We conducted a genome-resolved study of microbial community composition, replication rates, and proteomes during the first three months of life of both healthy and sick premature infants. Infants were found to be colonized by similar microbes, but each underwent a distinct colonization trajectory.\n\nInterestingly, related microbes colonizing different infants were found to have distinct proteomes, indicating that microbiome function is not only driven by which organisms are present, but also largely depends on microbial responses to the unique set of physiological conditions in the infant gut.

microbiology↗

bioRxiv2017

Machine learning leveraging genomes from metagenomes identifies influential antibiotic resistance genes in the infant gut microbiome

Antibiotic resistance in pathogens is extensively studied, yet little is known about how antibiotic resistance genes of typical gut bacteria influence microbiome dynamics. Here, we leverage genomes from metagenomes to investigate how genes of the premature infant gut resistome correspond to the ability of bacteria to survive under certain environmental and clinical conditions. We find that formula feeding impacts the resistome. Random forest models corroborated by statistical tests revealed that the gut resistome of formula-fed infants is enriched in class D beta-lactamase genes. Interestingly, Clostridium difficile strains harboring this gene are at higher abundance in formula-fed infants compared to C. difficile lacking this gene. Organisms with genes for major facilitator superfamily drug efflux pumps have faster replication rates under all conditions, even in the absence of antibiotic therapy. Using a machine learning approach, we identified genes that are predictive of an organisms direction of change in relative abundance after administration of vancomycin and cephalosporin antibiotics. The most accurate results were obtained by reducing annotated genomic data into five principal components classified by boosted decision trees. Among the genes involved in predicting if an organism increased in relative abundance after treatment are those that encode for subclass B2 beta-lactamases and transcriptional regulators of vancomycin resistance. This demonstrates that machine learning applied to genome-resolved metagenomics data can identify key genes for survival after antibiotics and predict how organisms in the gut microbiome will respond to antibiotic administration.\n\nImportanceThe process of reconstructing genomes from environmental sequence data (genome-resolved metagenomics) allows for unique insight into microbial systems. We apply this technique to investigate how the antibiotic resistance genes of bacteria affect their ability to flourish in the gut under various conditions. Our analysis reveals that strain-level selection in formula-fed infants drives enrichment of beta-lactamase genes in the gut resistome. Using genomes from metagenomes, we built a machine learning model to predict how organisms in the gut microbial community respond to perturbation by antibiotics. This may eventually have clinical and industrial applications.

microbiology↗