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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2026

Evaluation of High Arctic terrestrial habitats as potential hotspots of nitrous oxide emissions (Hornsund region, South Spitsbergen)

Carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) are the main greenhouse gases (GHGs) contributing to the ongoing climate crisis. Among those N2O has the highest global warming potential and is mainly of microbiological origin. Tropical rainforests are considered the primary natural source, although in recent years fluxes of N2O from polar ecosystems have been reported at comparable levels. In this study we aimed to identify High Arctic terrestrial habitats with the highest potential to become sources of N2O emissions. A microbiological and geochemical analysis was performed on soil procured from the biologically and geomorphologically diverse South Spitsbergen region in search of biotic and abiotic determinants of a N2O emission hotspot. Terrestrial sites within this High Arctic area vastly differed in their potential to emit substantial N2O amounts. External organic matter inputs were pivotal in maintaining a pool of inorganic nitrogen compounds for microbially-mediated N2O-generating processes such as denitrification. The examined planktivorous seabird colony presented a unique, potential N2O emission hotspot as it featured persistent acidification of the surrounding soil, a steady ammonia release and nitrate presence even after breeding season closure. Soils of the majority of analyzed sites did not display detectable nitrate and/or ammonia levels, with some areas having the characteristics of a N2O-sink rather than an emitter, especially postglacial moraine deposits. The presented data encourage further, more targeted investigations of High Arctic N2O emission hot- and coldspots to progressively improve N2O emission estimates for permafrost affected regions worldwide.

microbiology↗
bioRxiv2026

MATRIX: Rapid Quantification of Total and Active Microbial Cells with Single Cell Phenotypes for Environmental Microbiomes

Quantifying the abundance and activity of bacteria within populations and communities is fundamental to systems microbiology and microbiome research. Yet direct microscopic cell counting remains low-throughput, labor-intensive, and prone to user variability, leading many researchers to rely on indirect proxies such as optical density or multicopy marker-gene quantification. These indirect approaches do not distinguish between active and inactive cells and can obscure ecological interpretation. Here, we introduce MATRIX (Microbial Activity and Total cell quantification via Rapid Imaging and eXtraction), an efficient workflow that integrates sample extraction, fluorescence staining, automated microscopy and image analysis, and Bayesian statistical inference to quantify total and redox-active cells and derive single-cell measurements for environmental microbial populations and communities. We demonstrate its reproducibility and versatility using both cultured isolates and high-diversity soil communities. The resulting quantitative, phenotypic datasets provide rapid, direct measurements of population of community size and activity, enabling well-powered analyses that strengthen mechanistic insight into microbial responses and improve the ecological grounding of microbiome studies. ImportanceMicrobiome studies commonly rely on relative abundance data, which cannot distinguish whether compositional shifts reflect true population growth, declines in total community size, or both. Without explicit measurements of population and community sizes, mechanistic interpretation of microbiome dynamics remains incomplete. Here we present a rapid, throughput workflow, MATRIX, that quantifies both total and redox-active bacterial cells from environmental samples. By integrating single-cell phenotypes with community-level metrics, this approach anchors microbiome datasets in direct ecological accounting rather than proxies. These measurements can clarify whether observed changes in community structure represent shifts in abundance, activity, or both, improving inference about microbial responses to stress or environmental change. MATRIX therefore offers an efficient way to incorporate quantitative ecology into systems-microbiology and microbiome studies and to strengthen the link between microbial cellular physiology, community dynamics, and eco-system function. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=125 SRC="FIGDIR/small/712149v1_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@1db1d8aorg.highwire.dtl.DTLVardef@708551org.highwire.dtl.DTLVardef@140a1aforg.highwire.dtl.DTLVardef@36fe58_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗
bioRxiv2026

Integrated omics analysis reveals reorganization of nitrogen and lipids metabolism in a toluene-degrading bacterium

Gas-phase bioprocesses that immobilize microbial cells on solid carriers enable the efficient conversion of poorly water-soluble gaseous substrates, thereby offering significant potential to advance bioremediation and bioproduction. However, microorganisms in the gas phase are exposed to various environmental stresses, mainly due to the absence of bulk water. While survival strategies of microorganisms in gaseous environments have been studied in environmental microbiology, the metabolic adaptations that sustain bacterial cell activity remain poorly understood. In this study, we elucidated the comprehensive metabolic alterations of a highly adhesive bacterium Acinetobacter sp. Tol 5 degrading toluene under gas- and aqueous-phase conditions. An integrated approach combining metabolomics, lipidomics, and transcriptomics revealed significant differences in metabolic profiles between cells under these conditions. Under the gas-phase condition, the degradation of amino acids and nucleic acids was significantly promoted, and the intracellular glutamate pool was maintained at high levels. Notably, citrulline was found to accumulate specifically under the gas-phase condition, representing a stress response similar to that reported in Cucurbitaceae plants during drought. Furthermore, lipidomics revealed the lipid composition of Tol 5 and demonstrated a shift in response to environmental conditions. Specifically, the degradation of intracellular storage lipids was promoted under gas-phase conditions, suggesting a crucial link to bacterial survival in water-limited environments. These findings provide critical insights into the adaptation strategies of bacteria adapting to gaseous environments, offering fundamental information for the rational design of robust gas-phase bioprocesses and a deeper understanding of environmental microbiology.

microbiology↗
bioRxiv2026

Smartphone-Coupled Phase Contrast Microscopy Combined with Deep Transfer Learning for Candida Species Identification: A Proof-of-Concept Study

Species-level Candida identification can inform antifungal management, but reliable identification platforms remain inaccessible in many clinical microbiology laboratories, whereas phase contrast microscopy -- a common feature of routine laboratory microscopes -- is widely available. We asked whether this ubiquitous optical tool, combined with a consumer smartphone and deep transfer learning, could provide a feasible low-cost approach for preliminary Candida species discrimination. Fifteen clinical isolates of four species (C. albicans, C. glabrata, C. tropicalis, C. krusei) were collected from a single clinical microbiology laboratory and imaged using a consumer-grade smartphone coupled to a standard phase contrast microscope. Suspensions in human serum were imaged immediately after preparation (T0) and after 2-hour incubation at 37{degrees}C (T2). Pretrained vision backbone architectures were evaluated as fixed feature extractors under strict Leave-One-Strain-Out cross-validation. The best-performing model -- EfficientNet-B0 embeddings with a Linear Support Vector Machine applied to T2 images -- achieved an apparent internally cross-validated strain-level balanced accuracy of 0.833 and an overall strain accuracy of 86.7% (13/15 strains correctly classified). C. albicans, C. glabrata, and C. tropicalis were each identified with 100% recall. Both misclassified strains belonged to C. krusei -- the species with the smallest panel representation (n=3 strains) -- with misclassification attributable to limited strain diversity and suboptimal image quality. These findings demonstrate promising feasibility for preliminary image-based Candida species discrimination from smartphone-acquired phase contrast microscopy images, and support further evaluation in larger, externally validated strain collections.

microbiology↗
bioRxiv2026

Contact-dependent communication shapes the mesoscale spatial organization of bacterial swarms

Scientists use microscopy images to visualize and describe organisms and their interactions. Microscale visual inspections of individual cells and colonies, paired with genetic approaches, have revealed key developmental steps within microbial communities including, for example, swarms, biofilms, and fruiting bodies. However, there is a lack of formal, quantitative descriptions of these structures. This gap limits understanding of the structure and relevance of cell clusters, especially regarding development and interactions. Here, we develop biologically informed mathematical methods to formally assess cell clusters that assemble and dissolve over time in bacterial colonies. Our approaches to this analysis of the developmental stages of swarming, a type of collective migration, focus on cell length (as a proxy for developmental stage), cell-to-cell contacts, and groupings. We apply these to data from Proteus mirabilis strains with genetic disruptions in different aspects of its communication mechanisms to explore how identity signaling and cell-to-cell contact affect population structure at different scales. We found that kin recognition and contact-dependent, cell-cell communication govern population architecture during collective migration, such as swarming. This integration of microbiology with applied mathematics and computer science is a frontier for analyzing experimental data, leading to testable biological insights. SignificanceCollective behaviors are often exhibited by social organisms. Open questions regarding these behaviors include: how do communication and familial identity influence local interactions, and how do local interactions combine to create collective behaviors? Qualitative descriptions from visual examination of microbes can be useful in approaching these questions, but formal quantification of these structures will be essential if we are to understand the mechanisms that underlie organismal behavior and organization. This manuscript bridges microbiology and applied mathematics, interleaving the mathematics and the experiments in an iterative fashion. We offer critical advances to better understand, analyze, and characterize the multiscale structures that form and dissolve as a colony engages in collective migration. Our findings suggest that cell-cell communication plays an important role in population architecture, specifically that communication may "prime" cells for collective migration and prevent stagnation during life stage transitions.

microbiology↗
bioRxiv2026

Optical flow reveals motility signatures for inferring pathogenic bacterial mixture compositions via temporal convolutional networks

With the rapid expansion of global food demand, aquaculture has become a critical pillar for future food security. However, aquaculture systems remain highly vulnerable to pathogenic bacteria, and rapid identification of antagonistic microbes is essential for sustainable disease control. Conventional evaluation approaches rely on fluorescence labeling or post-culture assays, limiting the ability to quantify dynamic interactions in mixed microbial populations in a real-time and label-free manner. Here, we propose a computational framework for classifying the mixing ratio of Vibrio harveyi and environmental bacteria using time-series motion features extracted from microscopy videos. We defined 24 interpretable motility descriptors and employed a Temporal Convolutional Network (TCN) to learn their temporal structure. The proposed method achieved a classification accuracy of 93.3%, outperforming conventional static statistical approaches and alternative machine learning models. These findings indicate that mixture discrimination in microbial communities is governed not by absolute motility magnitude, but by collective alignment and its temporal stability. Our study establishes a time-resolved computational framework for quantifying dynamic collective order in mixed microbial populations and highlights its potential for label-free automated screening and robotic microbiological applications. Author summaryBacterial infections pose a major threat to aquaculture, and rapid identification of antagonistic microbes is essential for sustainable disease management. Existing screening approaches often require fluorescent labeling or post-culture analysis, making real-time evaluation of mixed bacterial populations difficult. In this study, we show that mixture ratios of Vibrio harveyi and environmental bacteria can be accurately classified from time-series motion features extracted from microscopy videos. By applying TCN to 24 interpretable motility descriptors, we achieved high classification accuracy without relying on fluorescent markers. Our analysis demonstrates that collective directional alignment and its temporal stability, rather than absolute swimming speed, are the key determinants of mixture discrimination. This work introduces a computational strategy for quantifying dynamic collective order in microbial communities and supports the development of label-free, automated screening platforms for microbiological applications.

microbiology↗
bioRxiv2026

Microscale assay to evaluate the minimum inhibitory concentration of purified compounds with limited sample volume

The minimum inhibitory concentration (MIC) is a standard measure for describing the lowest effective dose concentration of an antimicrobial compound in clinical practice; yet, conventional assays often require a substantial amount of antimicrobial compound, limiting their use with scarce, purified agents. Here, we describe a simple and reproducible technique to evaluate the MIC for purified compounds with a limited sample size. The protocol describes the MIC steps against a bacterial strain while minimizing the use of reagents and materials. It is helpful for screening purified natural products as antimicrobial agents and in early-stage drug discovery. The protocol adapts standard microplate-based assays for two-fold dilution of the compound, ensuring their applicability in microbiological studies. The MIC value of the standard antibiotic kanamycin against Staphylococcus aureus, Vibrio fischeri, Klebsiella pneumoniae, and Escherichia coli was determined using our method, and was found to be consistent with the conventional broth microdilution method, validating its reliability. Therefore, this method offers a practical and viable solution for antimicrobial drug discovery, addressing the disparity between limited compound availability and comprehensive microbiological assessment of MIC.

microbiology↗
bioRxiv2016

Speciation by Symbiosis: The Microbiome and Behavior

Species are fundamental units of comparison in biology. The newly discovered importance and ubiquity of host-associated microorganisms is now stimulating work on the roles that microbes can play in animal speciation. We previously synthesized the literature and advanced concepts of speciation by symbiosis with notable attention to hybrid sterility and lethality. Here, we review recent studies and relevant data on microbes as players in host behavior and behavioral isolation, emphasizing the patterns seen in these analyses and highlighting areas worthy of additional exploration. We conclude that the role of microbial symbionts in behavior and speciation is gaining exciting traction, and the holobiont and hologenome concepts afford an evolving intellectual framework to promote research and intellectual exchange between disciplines such as behavior, microbiology, genetics, symbiosis and speciation. Given the increasing centrality of microbiology in macroscopic life, microbial symbiosis is arguably the most neglected aspect of animal and plant speciation, and studying it should yield a better understanding of the origin of species.

Evolutionary Biology↗
bioRxiv2017

Metagenomic Next-Generation Sequencing Detects Pulmonary Pathogens in Hematopoietic Cellular Transplant Patients with Acute Respiratory Illnesses

RATIONALECurrent microbiologic diagnostics often fail to identify the etiology of lower respiratory tract infections (LRTI) in hematopoietic cellular transplant recipients (HCT), which precludes the implementation of targeted therapies.\n\nOBJECTIVESTo address the need for improved LRTI diagnostics, we evaluated the utility of metagenomic next generation sequencing (mNGS) of bronchoalveolar lavage (BAL) to detect microbial pathogens in HCT patients with acute respiratory illnesses.\n\nMETHODSWe enrolled 22 post-HCT adults ages 19-69 years with acute respiratory illnesses who underwent BAL at the University of Michigan between January 2012 and May 2013. mNGS was performed on BAL fluid to detect microbes and simultaneously assess the host transcriptional response. Results were compared against conventional microbiologic assays.\n\nMEASUREMENTS & MAIN RESULTSmNGS demonstrated 100% sensitivity for detecting respiratory microbes (human metapneumovirus, respiratory syncytial virus, Stenotrophomonas maltophilia, human herpesvirus 6 and cytomegalovirus) when compared to standard testing. Previously unrecognized LRTI pathogens were identified in six patients for whom standard testing was negative (human coronavirus 229E, human rhinovirus A, Corynebacterium propinquum and Streptococcus mitis); findings were confirmed by independent PCR and 16S rRNA sequencing. Relative to patients without infection, patients with infection had increased expression of immunity related genes (p=0.022) and significantly lower diversity of their respiratory microbiome (p=0.017).\n\nCONCLUSIONSCompared to conventional diagnostics, mNGS enhanced detection of pathogens in BAL fluid from HCT patients. Furthermore, our results suggest that combining unbiased microbial pathogen detection with assessment of host gene biomarkers of immune response may hold promise for enhancing the diagnosis of post-HCT respiratory infections.

genomics↗
bioRxiv2017

An outbreak of Pseudomonas aeruginosa infection linked to a Black Friday piercing event

BackgroundOutbreaks linked to cosmetic piercing are rare, but can cause significant illness. We report the investigation and management of a point-source outbreak that occurred during a \"Black Friday\" event in North West England.\n\nMethodsOutbreak investigation was led by Public Health England, and included active case finding among individuals pierced at a piercing premises between 25/11/2016 (\"Black Friday\") and 7/12/2016. Detailed epidemiological, environmental (including inspection and sampling), and microbiological investigation was undertaken.\n\nResultsDuring the \"Black Friday\" event (25/11/2016), 45 people were pierced (13 by a newly-appointed practitioner). Eleven cases were identified (7 microbiologically-confirmed, 2 probable, and 2 possible). All cases had clinical signs of infection around piercing sites, and five required surgical intervention, with varying degrees of post-operative disfigurement. All confirmed and probable cases had a \"scaffold piercing\" placed with a guide bar by the newly-appointed practitioner. Pseudomonas aeruginosa, indistinguishable at nine-locus variable-number tandem repeat loci, was isolated from four of the confirmed cases, and from pre- and post-flush samples from five separate water taps (three sinks) in the premises. Water samples taken after remedial plumbing work confirmed elimination of Pseudomonas contamination.\n\nConclusionsAlthough high levels of Pseudomonas water contamination and some poor infection control procedures were identified, infection appeared to require additional exposure to an inexperienced practitioner, and the more invasive scaffold piercing. A proactive collaborative approach between piercers and health and environmental officials is required to reduce outbreak risk, particularly when unusually large events are planned.

epidemiology↗
bioRxiv2020

Virtual-freezing fluorescence imaging flow cytometry

By virtue of the combined merits of flow cytometry and fluorescence microscopy, imaging flow cytometry (IFC) has become an established tool for cell analysis in diverse biomedical fields such as cancer biology, microbiology, immunology, hematology, and stem cell biology. However, the performance and utility of IFC are severely limited by the fundamental trade-off between throughput, sensitivity, and spatial resolution. For example, at high flow speed (i.e., high throughput), the integration time of the image sensor becomes short, resulting in reduced sensitivity or pixel resolution. Here we present an optomechanical imaging method that overcomes the trade-off by virtually "freezing" the motion of flowing cells on the image sensor to effectively achieve 1,000 times longer exposure time for microscopy-grade fluorescence image acquisition. Consequently, it enables high-throughput IFC of single cells at >10,000 cells/s without sacrificing sensitivity and spatial resolution. The availability of numerous information-rich fluorescence cell images allows high-dimensional statistical analysis and accurate classification with deep learning, as evidenced by our demonstration of unique applications in hematology and microbiology.

cell biology↗
bioRxiv2020

Equivolumetric protocol generates library sizes proportional to total microbial load in next-generation sequencing

Next-generation sequencing (NGS) has been extensively employed to perform microbiome characterization worldwide. As a culture-independent methodology, it has allowed high-level profiling of sample microbial composition. However, most studies are limited to information regarding relative bacterial abundances, ignoring scenarios in which sample microbe biomass can vary widely. Here, we develop an equivolumetric protocol for amplicon library preparation capable of generating NGS data responsive to input DNA, recovering proportionality between observed read counts and absolute bacterial abundances. Under specified conditions, we argue that the estimation of colony-forming units (CFU), the most common unit of bacterial abundance in classical microbiology, is challenged mostly by resolution and taxon-to-taxon variation. We propose Bayesian cumulative probability models to address such issues. Our results indicate that predictive errors vary consistently below one order of magnitude for observed bacteria. We also demonstrate our approach has the potential to generalize to previously unseen bacteria, but predictive performance is hampered by specific taxa of uncommon profile. Finally, it remains clear that NGS data are not inherently restricted to relative information only, and microbiome science can indeed meet the working scales of traditional microbiology.

molecular biology↗
bioRxiv2020

Identification of the keystone species in non-alcoholic fatty liver disease by causal inference and dynamic intervention modeling

ObjectiveKeystone species are required for the integrity and stability of an ecological community, and therefore, are potential intervention targets for microbiome related diseases. DesignHere we describe an algorithm for the identification of keystone species from cross-sectional microbiome data of non-alcoholic fatty liver disease (NAFLD) based on causal inference theories and dynamic intervention modeling (DIM). ResultsEight keystone species in the gut of NAFLD, represented by P. loveana, A. indistinctus and D. pneumosintes, were identified by our algorithm, which could efficiently restore the microbial composition of the NAFLD toward a normal gut microbiome with 92.3% recovery. These keystone species regulate intestinal amino acids metabolism and acid-base environment to promote the growth of the butyrate-producing Lachnospiraceae and Ruminococcaceae species. ConclusionOur method may benefit microbiome studies in the broad fields of medicine, environmental science and microbiology. SUMMARYWhat is already known about this subject? O_LINon-alcoholic fatty liver disease (NAFLD) is a complex multifactorial disease whose pathogenesis remains unclear. C_LIO_LIDysbiosis in the gut microbiota affects the initiation and development of NAFLD, but the mechanisms is yet to be established. C_LIO_LIKeystone species represent excellent candidate targets for gut microbiome-based interventions, as they are defined as the species required for the integrity and stability of the ecological system. C_LI What are the new findings? O_LINAFLD showed significant dysbiosis in butyrate-producing Lachnospiraceae and Ruminococcaceae species. C_LIO_LIMicrobial interaction networks were constructed by the novel algorithm with causal inference. C_LIO_LIKeystone species were identified form microbial interaction networks through dynamic intervention modeling based on generalized Lotka-Volterra model. C_LIO_LIEight keystone species of NAFLD with the highest potential for restoring the microbial composition were identified. C_LI How might it impact on clinical practice in the foreseeable future? O_LIAn algorithm for the identification of keystone species from cross-sectional microbiome data based on causal inference theories and dynamic intervention modeling. C_LIO_LIEight keystone species in the gut of NAFLD, represented by P. loveana, A. indistinctus and D. pneumosintes, which could efficiently restore the microbial composition of the NAFLD toward a normal gut microbiome. C_LIO_LIOur method may benefit microbiome studies in the broad fields of medicine, environmental science and microbiology. C_LI

bioinformatics↗
bioRxiv2020

Understanding the compositional changes in LB Lennox medium during growth of Escherichia coli DH5α in a 1L bioreactor

Compositional changes in growth medium represents dynamic interplay between cell growth, biomass formation, and energy maintenance, with concomitant decrease in nutrients and increase in secreted metabolites and metabolic byproducts. Such information is important for quantifying microbial physiological response both at the population and cellular level, with respect to understanding subtle differences in microbial growth response, as well as supporting model building efforts in metabolic engineering. With the desire to understand molecular weight changes in components of LB Lennox medium as well as broth fractions present during cultivation of Escherichia coli DH5 (ATCC 53868) at 37 {degrees}C, 400 rpm stirring and 1 VVM aeration in a 1 L bioreactor, this study used a combination of gel filtration chromatography (GFC) and reversed phase high performance liquid chromatography (RP-HPLC) for determining changes to molecular weight of different fractions of the growth medium. Experiment results revealed the difficulty of fractionating the culture broth with RP-HPLC, where no distinct peaks of narrow retention time width were obtained. More importantly, the column used for GFC was unable to differentiate small molecular weight changes on the order of a few tens to few hundred Da through a refractive index detector. Together, GFC and RP-HPLC highlighted the difficulty of fractionating LB Lennox culture broth into different distinct fractions. Finally, the study validated the use of 194 nm as detection wavelength for visualizing the chromatogram of LB Lennox medium eluted from a C-18 reversed phase column during liquid chromatography. Collectively, GFC and RP-HPLC could not fractionate LB Lennox broth of E. coli DH5 into distinct fractions for further analysis by identification techniques such as mass spectrometry. Given the inherent complexity of complex medium such as LB Lennox, clean separation of the medium into every component with high purity may be impossible. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=47 SRC="FIGDIR/small/311373v1_ufig1.gif" ALT="Figure 1"> View larger version (7K): org.highwire.dtl.DTLVardef@2d0861org.highwire.dtl.DTLVardef@814ebcorg.highwire.dtl.DTLVardef@158328borg.highwire.dtl.DTLVardef@15e6844_HPS_FORMAT_FIGEXP M_FIG C_FIG Short descriptionBroad elution profile and lack of distinct peaks in chromatogram of LB Lennox broth at 6 hours post inoculation with Escherichia coli DH5 (ATCC 53868), after attempted fractionation by C-18 reversed phase high performance liquid chromatography (RP-HPLC), revealed that separation could not be achieved with a complex mixture such as a microbiology broth. Specifically, while the objective of understanding compositional changes to culture medium during growth presents tremendous opportunities for determining the dynamic conversion of nutrients into metabolites and byproducts, difficulty of profiling all chemical constituents in a complex broth mixture meant that current model building efforts for metabolic engineering remains primitive with respect to the consortia of metabolic reactions occurring in situ at the cellular level. Subject areasbiotechnology, biochemistry, cell biology, microbiology, analytical chemistry

biochemistry↗
bioRxiv2021

Remote teaching versus blended learning: exploring discourse richness and learning effectiveness

The Internet has changed the way teachers and students access information and build knowledge. The recent COVID-19 pandemic has created challenges for both teachers and students and a demand for new methodologies of remote learning. In the life sciences, mixing online content with practical activities represents an even greater challenge. In microbiology, the implementation of an active teaching methodology, the #Adopt project, based on the social network Facebook(R), represents an excellent option for connecting remote education with classroom activities. In 2020, the version applied in high school, "Adopt a Microorganism", was adapted to meet the demands of emergency remote education owing to the suppression of face-to-face activities caused by the pandemic. In the present study, we assessed how the change in methodology impacted the discourse richness of students from high school integrated with technical education in the Business Administration program of the Federal Institute of Sao Paulo, Sorocaba Campus. Three questionnaires related to the groups of microorganisms (Archaea, Bacteria, Virus, Fungi, and Protozoan) were applied. The students responses in the 2019 and 2020 classes were compared concerning content richness and multiplicity of concepts through the application of the Shannon diversity index, an approach that is generally used to assess biodiversity in different environments. The observed results suggest that remote learning provided students with a conceptual basis and richness of content equivalent to that achieved by students subjected to the hybrid teaching model. In conclusion, this study suggests that the #Adopt project methodology increases students discourse richness in microbiology even without face-to-face traditional classes.

scientific communication and education↗
bioRxiv2021

Microbial community composition of translocated ancient woodland soil: a case study

Soil translocation is an ecological habitat restoration technique which consists of moving the entire topsoil from a donor site to a chosen receptor site. We investigated changes in soil chemistry and microbiology three years following the salvage of semi-ancient woodland soil and materials (0.94 ha) to a nearby receptor pasture due to road widening works (Kent, UK). We sampled i) intact woodland soils adjacent to the area of soils that was translocated to represent the lost donor site, ii) the soil three years after it had been translocated, and iii) grassland soils adjacent to the translocated soil to represent the original receptor site. The intention was to ascertain if shifts in soil chemistry and microbial community composition (Phospholipid Fatty-acid analysis: PLFA) occurred due to soil translocation. PLFA signature biomarkers demonstrated the overall microbial community profile of the translocated and woodland soils were similar; however, salvaged soils had a 40% increase in the Arbuscular Mycorrhizal Fungi (AMF) bioindicator fatty acid 16:1{omega}5, a 10% decrease in the Gram-positive bacterial fatty acids, and increased pH (5.01-5.77) compared to the original donor woodland soil. The AMF bioindicator and the first Principal Component (PC1) of the PCA of PLFA data positively correlated with soil pH (r2=0.94 and r2=0.88 respectively) across all three experimental groups. Considering that soil pH increases with depth in this location, it is likely that mixing of soil horizons during translocation increased the topsoil pH causing changes in the soil microbial communities. We concluded that after three years, the chemical and microbial properties of the salvaged soil were characteristic of a woodland soil but showed signs of disturbance. Implications for practiceO_LITranslocated semi-ancient woodland soils may still exhibit similar microbiological characteristics to the donor site three years after being moved to a pasture receptor site. C_LIO_LIHowever, disturbances expressed as significant changes in chemistry and microbial communities can affect soils following their translocation. C_LIO_LIWhere soil pH varies with depth, translocation by loose-tipping may redistribute acidity within the soil profile, with important cascading impacts on the soil microbial communities. C_LI

ecology↗
bioRxiv2021

Comethyl: A network-based methylome approach to investigate the multivariate nature of health and disease

Health outcomes are frequently shaped by difficult to dissect inter-relationships between biological, behavioral, social, and environmental factors. DNA methylation patterns reflect such multi-variate intersections, providing a rich source of novel biomarkers and insight into disease etiologies. Recent advances in whole-genome bisulfite sequencing (WGBS) enable investigation of DNA methylation over all genomic CpGs, but existing bioinformatic approaches lack accessible system-level tools. Here, we develop the R package Comethyl, for weighted gene correlation network analysis (WGCNA) of user-defined genomic regions that generates modules of comethylated regions, which are then tested for correlations with sample traits. First, regions are defined by CpG genomic location or regulatory annotation and filtered based on CpG count, sequencing depth, and variability. Next, correlation networks are used to find modules of interconnected nodes using methylation values within the selected regions. Each module containing multiple comethylated regions is reduced in complexity to a single eigennode value, which is then tested for correlations with experimental metadata. Comethyl has the ability to cover the noncoding regulatory regions of the genome with high relevance to interpretation of genome-wide association studies and integration with other types of epigenomic data. We demonstrate the utility of Comethyl on a dataset of male cord blood samples from newborns later diagnosed with autism spectrum disorder (ASD) versus typical development. Comethyl successfully identified an ASD-associated module containing gene regions with brain glial functions. Comethyl is expected to be useful in uncovering the multi-variate nature of health disparities for a variety of common disorders. Comethyl is available at github.com/cemordaunt/comethyl. Description of the AuthorsCharles E. Mordaunt, Ph.D. developed Comethyl while a postdoctoral fellow in the department of Medical Microbiology and Immunology at UC Davis. He is currently a Computational Biologist at GSK. Julia S. Mouat is a doctoral student in the Integrative Genetics and Genomics graduate group at UC Davis with interests in health disparities and intergenerational epigenetic risk factors for autism spectrum disorders. Rebecca J. Schmidt, Ph.D. is an Associate Professor of Public Health Sciences at UC Davis, with expertise in the use of epigenetics in epidemiology and neurodevelopmental disorders. Janine M. LaSalle, Ph.D. is a Professor of Medical Microbiology and Immunology, Co-Director of the Perinatal Origins of Disparities Center, and Deputy Director of the Environmental Health Sciences Center at UC Davis, with expertise in epigenomics and neurodevelopmental disorders.

bioinformatics↗
bioRxiv2021

REDigest: a Python GUI for In-Silico Restriction Digestion Analysis of Genes or Complete Genome Sequences

Restriction fragment length polymorphism (RFLP) is a technology for the molecular characterization of DNA and widely used genome mapping, medical genetics, molecular microbiology and forensics etc. Terminal restriction fragment length polymorphism (T-RFLP), a variant of RFLP is extensively used in environmental microbiology for the microbial community profiling based on the restriction digestion profile of marker gene (16S rRNA, FTHFS etc.) amplicons. At present, there is a lack of a tool which can perform in-silico restriction digestion of a large number of sequences at a time, in an interactive way and as an output produce sequences of the restriction fragments and visualization plot. I have developed a graphical user interface based software "REDigest" for the in-silico restriction digestion analysis for gene or genome sequences. The REDigest software program with a graphical user interface is freely available at https://github.com/abhijeetsingh1704/REDigest.

bioinformatics↗