Biology subjects

Ratti, C.

Publications and source records attributed to Ratti, C..

7 recordsLinked to original sources

bioRxiv2026

Endophytic colonization pathways of Pseudomonas chlororaphis M71 and Trichoderma atroviride SC1 in grapevine following stem injection

Endophytic biocontrol agents may contribute to grapevine health, but their ability to establish, persist, and move within woody tissues remains poorly understood. In this study, a stem injection method was developed to introduce Pseudomonas chlororaphis M71 and Trichoderma atroviride SC1 into rooted and grafted grapevine plants, and their spatial and temporal colonization patterns were compared with the movement of a dye tracer. The dye tracer moved rapidly through xylem tissues, whereas both microorganisms showed more restricted early distribution. Over time, M71 and SC1 displayed distinct colonization patterns. M71 persisted after injection, but remained localized near the inoculation site, with limited movement toward roots or distal aerial tissues. In grafted plants, M71 recovery depended on the injection site and declined more markedly after rootstock injection than after scion injection. In contrast, SC1 showed broader and more persistent colonization. In rooted cuttings, SC1 was recovered from stem and root tissues up to 56 days post-injection, and in grafted plants it was recovered across the graft union, particularly after scion injection. Microscopy supported internal localization of both microorganisms. GFP-labelled M71 and SC1 hyphae were observed mainly within xylem vessels, and viable microorganisms were recovered from corresponding wood tissues. No contamination was observed in control plants. These results show that beneficial microorganisms can be introduced into grapevine tissues by stem injection and that bacterial and fungal biocontrol agents differ markedly in their internal movement and persistence. IMPORTANCEIntroducing beneficial microorganisms directly into plant tissues could help in establishing protective endophytic populations, but little is known about how such microorganisms move and persist inside grapevine. This study shows that stem injection can deliver Pseudomonas chlororaphis M71 and Trichoderma atroviride SC1 into grapevine tissues without visible phytotoxicity. The two microorganisms followed different colonization patterns. M71 remained mostly localized near the injection site, whereas SC1 spread more broadly and persisted in both rooted and grafted plants. These findings provide a basis for developing targeted endophytic biocontrol strategies in grapevine propagation and early plant establishment. This approach may be particularly relevant for grapevine trunk diseases and other vascular disorders, in which pathogens colonize internal woody tissues.

microbiology↗

bioRxiv2026

A tomato fruit blotch viral replicon defines minimal requirements for cell autonomous replication and identifies functional RNA4-encoded movement and silencing suppression activities

Tomato fruit blotch virus (ToFBV) is an emerging multipartite positive-sense RNA virus associated with blotchy symptoms on tomato fruits and classified within the genus Blunervirus (family Kitaviridae). Despite its increasing agricultural relevance, the study of ToFBV has been hindered by the lack of mechanical transmissibility and the difficulty in reproducing infections under controlled conditions. In this work, we report a preliminary step toward the development of the first infectious agroclone system for ToFBV, based on full-length cDNA copies of its four genomic RNAs. We demonstrate that the cloned viral genome is capable of initiating cell autonomous replication in Nicotiana benthamiana, as indicated by the accumulation of negative-sense RNA intermediates in infiltrated tissues. To further validate the system, RNA3 was engineered to express GFP, enabling visualization of infection foci and confirming active viral replication in both N. benthamiana and tomato. Functional assays of RNA4-encoded proteins demonstrated that it encodes a movement protein capable of complementing movement-deficient viral vectors and a putative suppressor of post-transcriptional gene silencing (PTGS). Together, these results establish a versatile reverse genetics platform for ToFBV, providing new insights into the replication and functional organization of blunerviruses and enabling future studies on virus-host interactions, pathogenicity, and control strategies

microbiology↗

bioRxiv2020

Microbial Species Abundance Distributions Guide Human Population Size Estimation from Sewage Metagenomes

The metagenome embedded in urban sewage is an attractive new data source to understand urban ecology and assess human health status at scales beyond a single host. Analyzing the viral fraction of wastewater in the ongoing COVID-19 pandemic has shown the potential of wastewater as aggregated samples for early detection, prevalence monitoring, and variant identification of human diseases in large populations. However, using census-based population size instead of real-time population estimates can mislead the interpretation of data acquired from sewage, hindering assessment of representativeness, inference of prevalence, or comparisons of taxa across sites. Here, we show that taxon abundance and sub-species diversisty in gut-associated microbiomes are new feature space to utilize for human population estimation. Using a population-scale human gut microbiome sample of over 1,100 people, we found that taxon-abundance distributions of gut-associated multi-person microbiomes exhibited generalizable relationships with respect to human population size. Here and throughout this paper, the human population size is essentially the sample size from the wastewater sample. We present a new algorithm, MicrobiomeCensus, for estimating human population size from sewage samples. MicrobiomeCensus harnesses the inter-individual variability in human gut microbiomes and performs maximum likelihood estimation based on simultaneous deviation of multiple taxas relative abundances from their population means. MicrobiomeCensus outperformed generic algorithms in data-driven simulation benchmarks and detected population size differences in field data. New theorems are provided to justify our approach. This research provides a mathematical framework for inferring population sizes in real time from sewage samples, paving the way for more accurate ecological and public health studies utilizing the sewage metagenome. Author summaryWastewater-based epidemiology (WBE) is an emerging field that employs sewage as aggregated samples of human populations. This approach is particularly promising for tracking diseases that can spread asymptomatically in large populations, such as the COVID-19. As a new type of biological data, sewage has its own unique challenges to utilize. While wastewater samples are usually assumed to represent large populations, it is not guaranteed, because of stochasticity in toilet flushes; unlike epidemiological experiments collecting data from individuals, sample size, i.e., the human population size represented by a wastewater sample, is a fundamental yet difficult-to-characterize parameter for sewage samples. Researchers would need to aggregate data from large areas and week-long collection to stabilize data, during which, important spikes in small areas or short time scales may be lost. It also remains challenging to turn viral titers into case prevalences, evaluating representativeness, or comparing measurements across sites/studies. This study provides a framework to estimate human population size from sewage utilizing human gut-associated microorganisms. Through analysis, we demonstrate that variance of taxon abundances and single-nucleotide polymorphism as two variables that change with population size. We provide a new tool MicrobiomeCensus that performs population size estimation from microbial taxon abundances. MicrobiomeCensus outperforms generic algorithms in terms of computational efficiency while at comparable or better accuracy. Using MicrobiomeCensus, we detected population size differences in sewage samples taken in Cambridge, MA, under two sampling approaches, i.e., "grab" or "composite" sampling. This study provides a framework to utilize individual-level microbiomes to learn from sewage, paving the way to prevalence estimation and improved spatio-temporal resolutions in WBE..

bioinformatics↗

bioRxiv2019

Longitudinal wastewater sampling in buildings reveals temporal dynamics of metabolites

Direct sampling of building wastewater has the potential to enable "precision public health" observations and interventions. Temporal sampling offers additional dynamic information that can be used to increase the informational content of individual metabolic "features", but few studies have focused on high-resolution sampling. Here, we sampled three spatially close buildings, revealing individual metabolomics features, retention time (rt) and mass-to-charge ratio (mz) pairs, that often possess similar stationary statistical properties, as expected from aggregate sampling. However, the temporal profiles of features--providing orthogonal information to physicochemical properties--illustrate that many possess significantly different feature temporal dynamics (fTDs) across buildings, with rapid and unpredictable deviations from the mean. Internal to a building, numerous and seemingly unrelated features, with mz and rt differences up to hundreds of Daltons and seconds, display highly correlated fTDs, suggesting non-obvious feature relationships. Data-driven building classification achieves high sensitivity and specificity, and extracts building-identifying features with unique dynamics. Analysis of fTDs from many short-duration samples allows for tailored community monitoring with applicability in public health studies.

bioinformatics↗

bioRxiv2019

24-hour multi-omics analysis of residential sewage reflects human activity and informs public health

High-throughput molecular analysis of sewage is a promising tool for precision public health. Here, we combine sewer network and demographic data to identify a residential catchment for sampling, and explore the potential of applying untargeted genomics and metabolomics to sewage to collect actionable public health data. We find that wastewater sampled upstream in a residential catchment is representative of the human microbiome and metabolome, and we are able to identify glucuronidated compounds indicative of direct human excretion, which are typically degraded too quickly to be detected at treatment plants. We show that diurnal variations during 24-hour sampling can be leveraged to discriminate between biomarkers in sewage that are associated with human activity from those related to the environmental background. Finally, we putatively annotate a suite of human-associated metabolites, including pharmaceuticals, food metabolites, and biomarkers of human health and activity, suggesting that mining untargeted data derived from residential sewage can expand currently-used biomarkers with direct public health or policy relevance.

genomics↗

bioRxiv2019

Population histories of the United States revealed through fine-scale migration and haplotype analysis

The population of the United States is shaped by centuries of migration, isolation, growth, and admixture between ancestors of global origins. Here, we assemble a comprehensive view of recent population history by studying the ancestry and population structure of over 32,000 individuals in the US using genetic, ancestral birth origin, and geographic data from the National Geographic Genographic Project. We identify migration routes and barriers that reflect historical demographic events. We also uncover the spatial patterns of relatedness in subpopulations through the combination of haplotype clustering, ancestral birth origin analysis, and local ancestry inference. Examples of these patterns include substantial substructure and heterogeneity in Hispanics/Latinos, isolation-by-distance in African Americans, elevated levels of relatedness and homozygosity in Asian immigrants, and fine-scale structure in European descents. Taken together, our results provide detailed insights into the genetic structure and demographic history of the diverse US population.

genetics↗

bioRxiv2019

Multi-site sampling and risk prioritization reveals the public health relevance of antibiotic resistance genes found in wastewater environments

The spread of bacterial antibiotic resistance across human and environmental habitats is a global public health challenge. Wastewater has been implicated as a major source of antibiotic resistance in the environment, as it carries resistant bacteria and resistance genes from humans into natural ecosystems. However, different wastewater environments and antibiotic resistance genes in wastewater do not all present the same level of risk to human health. In this study, we investigate the public health relevance of antibiotic resistance found in wastewater by combining metagenomic sequencing with risk prioritization of resistance genes, analyzing samples across urban sewage system environments in multiple countries. We find that many of the resistance genes commonly found in wastewater are not readily present in humans. Ranking antibiotic resistance genes based on their potential pathogenicity and mobility reveals that most of the resistance genes in wastewater are not clinically relevant. Additionally, we show that residential wastewater resistomes pose greater risk to human health than those in wastewater treatment plant samples, and that residential wastewater can be as risky as hospital effluent. Across countries, differences in antibiotic resistance in residential wastewater can, in some cases, reflect differences in antibiotic drug consumption. Finally, we find that the flow of antibiotic resistance genes is influenced by geographical distance and environmental selection. Taken together, we demonstrate how different analytical approaches can provide greater insights into the public health relevance of antibiotic resistance in wastewater.

microbiology↗