Flury, T. | Search bioRxiv

bioRxiv2024

Standardising a microbiome pipeline for body fluid identification from complex crime scene stains

BackgroundRecent advances in next-generation sequencing have opened up new possibilities for utilizing the human microbiome in various fields, including forensics. Researchers have capitalized on the site-specific microbial communities found in different parts of the body to identify body fluids from biological evidence. Despite promising results, microbiome-based methods have not yet been fully integrated into forensic practice due to the lack of standardized protocols and systematic testing of methods on forensically relevant samples. Our study addresses critical decisions in establishing these protocols, focusing on bioinformatics choices and the use of machine learning to present microbiome results in court for forensically relevant and challenging samples. ResultsWe propose using Operational Taxonomic Units (OTUs) for read data processing and creating heterogeneous training datasets for training a random forest classifier. Our classifier incorporates six forensically relevant classes: saliva, semen, hand skin, penile skin, urine, and vaginal/menstrual fluid. Across these classes, our classifier achieved a high weighted average F1 score of 0.89. Systematic testing on mixed-source samples and underwear revealed reliable detection of at least one component of the mixture and the identification of vaginal fluid from underwear substrates. Additionally, when investigating the sexually shared microbiome (sexome) of heterosexual couples, our classifier shows promising results for the inference of sexual activity. ConclusionIn our study, we recommend the use of a novel random forest classifier trained on a heterogenous dataset for obtaining predictions from samples mimicking forensic evidence. We also highlight the potential of the sexome for assessing the nature of sexual activities in forensic investigations, while delineating areas that warrant further research. Furthermore, we underscore key considerations when presenting machine learning results for classifying mixed-source samples.

bioinformatics↗

bioRxiv2024

Engineering a novel probiotic toolkit in Escherichia coli Nissle1917 for sensing and mitigating gut inflammatory diseases

Inflammatory Bowel Disease (IBD) is characterized by chronic intestinal inflammation with no cure and limited treatment options that often have systemic side effects. In this study, we developed a target-specific system to potentially treat IBD by engineering the probiotic bacterium Escherichia coli Nissle 1917 (EcN). Our modular system comprises three components: a transcription factor-based sensor (NorR) capable of detecting the inflammation biomarker nitric oxide, a type 1 hemolysin secretion system, and a therapeutic cargo consisting of a library of humanized anti-TNF nanobodies. Despite a reduction in sensitivity, our system demonstrated a concentration-dependent response to nitric oxide, successfully secreting functional nanobodies with binding affinities comparable to the commonly used drug Adalimumab, as confirmed by ELISA and in vitro assays. This newly validated nanobody library expands EcN therapeutic capabilities. The adopted secretion system, also characterized for the first time in EcN, can be further adapted as a platform for screening and purifying proteins of interest. Additionally, we provided a mathematical framework to assess critical parameters in engineering probiotic systems, including the production and diffusion of relevant molecules, bacterial colonization rates, and particle interactions. This integrated approach expands the synthetic biology toolbox for EcN-based therapies, providing novel parts, circuits, and a model for tunable responses at inflammatory hotspots. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=166 HEIGHT=200 SRC="FIGDIR/small/599326v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@173d556org.highwire.dtl.DTLVardef@1bb2ae1org.highwire.dtl.DTLVardef@fc1fcborg.highwire.dtl.DTLVardef@8b5f7f_HPS_FORMAT_FIGEXP M_FIG C_FIG Graphical Table of Contents. The engineered probiotic system: Inflamed intestinal cells release the inflammatory regulator TNF (depicted as red squares), which promotes inflammation through a positive feedback loop. Concurrently, these cells produce large amounts of nitric oxide (NO, represented by yellow circles) during inflammation. Our custom-engineered EcN biosensor can detect NO using a NorR-based sensor (in purple) and subsequently trigger the production of nanobodies (in turquoise). These nanobodies are then released into the extracellular environment via a specially engineered secretion system in the bacterial host (shown in dark blue). Once outside the cell, the nanobodies attach to TNF, effectively sequestering them and reducing inflammation. The graph at the bottom of this panel illustrates the general behavior of our system: nanobody production starts upon reaching a certain NO concentration threshold and continues in an NO-dependent fashion. As nanobodies are produced, they capture TNF, leading to a reduction in inflammation and a decrease in NO production. This decrease in NO then halts the nanobody production. SignificanceProbiotics can be engineered to detect and act upon extracellular disease indicators, optimizing therapeutic outcomes. Particularly, self-regulating sense-and-respond genetic circuits have the potential to enhance the accuracy, efficacy, and adaptability of treatment interventions. In this study, we developed and characterized a new integrated and modular toolkit that detects a gut inflammation biomarker, specifically nitric oxide, and responds to it in an inducible manner by secreting humanized nanobodies targeting the pro-inflammatory molecule TNF. We also develop a coarse-grained mathematical framework for modelling engineered probiotic activity in the gut. This novel system contributes to current efforts to develop new engineered probiotic systems and holds promise for inspiring new treatments for gut inflammation associated with various autoimmune diseases.

synthetic biology↗