While MALDI-TOF mass spectrometry (MS) is widely considered as the reference method for the rapid and inexpensive identification of microorganisms in routine laboratories, less attention has been addressed to its ability for detection of antimicrobial resistance (AMR). Recently, some studies assessed its potential application together with machine learning for the detection of AMR in clinical pathogens. The scope of this study was to investigate MALDI-TOF MS protein mass spectra combined with a prediction approach as an AMR screening tool for relevant foodborne pathogens, such as Campylobacter coli and Campylobacter jejuni. A One-Health panel of 224 C. jejuni and 116 C. coli strains was phenotypically tested for seven antimicrobial resistances, i.e., ciprofloxacin, erythromycin, tetracycline, gentamycin, kanamycin, streptomycin, and ampicillin, independently, and were submitted, after an on- and off-plate protein extraction, to MALDI Biotyper analysis, which yielded one average spectra per isolate and type of extraction. Overall, high performance was observed for classifiers detecting susceptible as well as ciprofloxacin- and tetracycline-resistant isolates. A maximum sensitivity and a precision of 92.3 and 81.2%, respectively, were reached. No significant prediction performance differences were observed between on- and off-plate types of protein extractions. Finally, three putative AMR biomarkers for fluoroquinolones, tetracyclines, and aminoglycosides were identified during the current study. Combination of MALDI-TOF MS and machine learning could be an efficient and inexpensive tool to swiftly screen certain AMR in foodborne pathogens, which may enable a rapid initiation of a precise, targeted antibiotic treatment.

Matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) is today the reference method for direct identification of microorganisms in diagnostic laboratories, as it is notably time- and cost-efficient. In the context of increasing cases of enteric diseases with emerging multi-drug resistance patterns, there is an urgent need to adopt an efficient workflow to characterize antimicrobial resistance (AMR). Current approaches, such as antibiograms, are time-consuming and directly impact the “patient-physician” workflow. Through this mini-review, we summarize how the detection of specific patterns by MALDI-TOF MS, as well as bioinformatics, become more and more essential in research, and how these approaches will help diagnostics in the future. Along the same lines, the idea to export more precise biomarker identification steps by MALDI-TOF(/TOF) MS data towards AMR identification pipelines is discussed. The study also critically points out that there is currently still a lack of research data and knowledge on different foodborne pathogens as well as several antibiotics families such as macrolides and quinolones, and many questions are still remaining. Finally, the innovative combination of whole-genome sequencing and MALDI-TOF MS could be soon the future for diagnosis of antimicrobial resistance in foodborne pathogens.

Monitoring SARS-CoV-2 in wastewater has shown to be an effective tool for epidemiological surveillance. More specifically, RNA levels determined with RT-qPCR have been shown to track with the infection dynamics within the population. However, the surveillance of individual lineages circulating in the population based on genomic sequencing of wastewater samples is challenging, as the genetic material constitutes a mixture of different viral haplotypes. Here, we identify specific signature mutations from individual SARS-CoV-2 lineages in wastewater samples to estimate lineages circulating in Luxembourg. We compare circulating lineages and mutations to those detected in clinical samples amongst infected individuals. We show that especially for dominant lineages, the allele frequencies of signature mutations correspond to the occurrence of particular lineages in the population. In addition, we provide evidence that regional clusters can also be discerned. We focused on the time period between November 2020 and March 2021 in which several variants of concern emerged and specifically traced the lineage B.1.1.7, which became dominant in Luxembourg during that time. During the subsequent time points, we were able to reconstruct short haplotypes, highlighting the co-occurrence of several signature mutations. Our results highlight the potential of genomic surveillance in wastewater samples based on amplicon short-read data. By extension, our work provides the basis for the early detection of novel SARS-CoV-2 variants.

During a study on the prevalence and diversity of members of the genus Campylobacte r in a shellfish-harvesting area and its catchment in Brittany, France, six urease-positive isolates of members of the genus Campylobacter were recovered from surface water samples, as well as three isolates from stools of humans displaying enteric infection in the same period. These strains were initially identified as members of the Campylobacter lari group by MALDI-TOF mass spectrometry and placed into a distinct group in the genus Campylobacter , following atpA gene sequence analysis based on whole-genome sequencing data. This taxonomic position was confirmed by phylogenetic analysis of the 16S rRNA, rpoB and hsp60 (groEL) loci, and an analysis of the core genome that provided an improved phylogenetic resolution. The average nucleotide identity between the representative strain CA656T (CCUG 73571T=CIP 111675T) and the type strain of the most closely related species Campylobacter ornithocola WBE38T was 88.5 %. The strains were found to be microaerobic and anaerobic, motile, non-spore-forming, Gram-stain-negative, spiral-shaped bacteria that exhibit catalase, oxidase and urease activities but not nitrate reduction. This study demonstrates clearly that the nine isolates represent a novel species within the C. lari group, for which the name Campylobacter armoricus is proposed. Here, we present phenotypic and morphological features of the nine strains and the description of their genome sequences. The proposed type strain CA656T has a 1.589 Mbp chromosome with a DNA G+C content of 28.5 mol% and encodes 1588 predicted coding sequences, 38 tRNAs, and 3 rRNA operons.

The assumption that cellulose degradation and assimilation can only be carried out by heterotrophic organisms was shattered in 2012 when it was discovered that the unicellular green alga, Chlamydomonas reinhardtii (Cr), can utilize cellulose for growth under CO₂-limiting conditions. Publications of genomes/transcriptomes of the colonial microalgae, Gonium pectorale (Gp) and Volvox carteri (Vc), between 2010–2016 prompted us to look for cellulase genes in these algae and to compare them to cellulases from bacteria, fungi, lower/higher plants, and invertebrate metazoans. Interestingly, algal catalytic domains (CDs), belonging to the family GH9, clustered separately and showed the highest (33–42%) and lowest (17–36%) sequence identity with respect to cellulases from invertebrate metazoans and bacteria, respectively, whereas the identity with cellulases from plants was only 27–33%. Based on comparative multiple alignments and homology models, the domain arrangement and active-site architecture of algal cellulases are described in detail. It was found that all algal cellulases are modular, consisting of putative novel cysteine-rich carbohydrate-binding modules (CBMs) and proline/serine-(PS) rich linkers. Two genes were found to encode a protein with a putative Ig-like domain and a cellulase with an unknown domain, respectively. A feature observed in one cellulase homolog from Gp and shared by a spinach cellulase is the existence of two CDs separated by linkers and with a C-terminal CBM. Dockerin and Fn-3-like domains, typically found in bacterial cellulases, are absent in algal enzymes. The targeted gene expression analysis shows that two Gp cellulases consisting, respectively, of a single and two CDs were upregulated upon filter paper addition to the medium.

The prokaryotic community of a groundwater aquifer exposed to high concentrations of tetrachloromethane (CCl₄) for more than three decades was followed by terminal restriction fragment length polymorphism (T-RFLP) during pump-and-treat remediation at the contamination source. Bacterial enrichments and isolates were obtained under selective anoxic conditions, and degraded 10 mg·L⁻¹ CCl₄, with less than 10% transient formation of chloroform. Dichloromethane and chloromethane were not detected. Several tetrachloromethane-degrading strains were isolated from these enrichments, including bacteria from the Klebsiella and Clostridium genera closely related to previously described CCl₄ degrading bacteria, and strain TM1, assigned to the genus Pelosinus, for which this property was not yet described. Pelosinus sp. TM1, an oxygen-tolerant, Gram-positive bacterium with strictly anaerobic metabolism, excreted a thermostable metabolite into the culture medium that allowed extracellular CCl₄ transformation. As estimated by T-RFLP, phylotypes of CCl₄-degrading enrichment cultures represented less than 7%, and archaeal and Pelosinus strains less than 0.5% of the total prokaryotic groundwater community.