Incomplete sequencing of bacterial genomes complicates the determination of optimal bacterial species concentrations

The gap

Evidence profile

Sourced from the future work and limitations section of the source papers, classified as general, drawn from work published between 2024 and 2026, spanning 3 journals. Those papers have been cited 101 times in total.

Supporting evidence — 4 representative gaps

• Decoding Virulence and Revisiting the Evolution of Klebsiella pneumoniae (2026) · Cureus · doi

  Future research should focus on advancing rapid and accurate diagnostic technologies, particularly genome- based methods such as whole-genome sequencing, to facilitate early identification of hypervirulent strains. In addition, there is a need to develop targeted therapeutic strategies, including novel antimicrobial agents, combination therapies, and anti-virulence approaches, to effectively manage infections caused by emerging hypervirulent and multidrug-resistant strains. Further studies are also required to better understand the mechanisms underlying the convergence of virulence and antimicrobial resistance, which will be critical for informing clinical management and public health interventions.

  generalfuture workevidence 5/5
  Keywords: genome hypervirulent strains antimicrobial virulence future focus advancing rapid accurate diagnostic technologies particularly based whole
• Role of Mobilome in Carbapenem Resistance (2026) · Antibiotics · doi

  Despite significant advancements and ongoing dedicated efforts in understanding the vital role of mobile genetic elements in the evolution and dissemination of carbapenem resistance, the dynamic interplay between bacterial hosts, MGEs and diverse environments of clinical settings as well non-clinical habitats continue to pose a continues challenge. Keeping in view the seriousness of the issue, future research must shift from retrospec- tive characterization to a prospective, predictive, and therapeutic framework, alongside applying advanced analytical approaches [177,178]. Whole genome sequencing (WGS) has become a cornerstone in infectious disease research and surveillance. The field is now poised to extend its focus from descriptive genomics to predictive modelling. Multi-omics approaches integrated with the power of computation and bioinformatics may enable prediction of the likelihood of transmission of particular MGEs to high-risk clones or other members of bacterial species based on the sequence features. In addition to this, investigation of MGE transfer within complex communities (e.g., human gut) using sequence-based techniques, combined with artificial intelligence/machine learning (AI/ML), may identify the hotspots of gene transfer and predict how antibiotic use may shape up the mobilome of bacteria in a given habitat or a person and accelerate the rate of emergence of drug resistance [178,179]. Since most of the research studies on drug resistance are carried out under in vitro set- tings, which fail to recapitulate the complexity of the in vivo conditions, in vivo gut models may be employed to systematically understand the transmission dynamics of MGEs during clinical treatment, co-infection with bacteriophages or in the presence of immune response. Furthermore, applying advanced technologies such as long-read sequencing (Nanopore sequencing) of the bacterial strains directly from the patient’s sample would better help us understand the mechanism of gene transfer, without the biases of laboratory culturing. The One Health approach is becoming a cornerstone of infectious disease surveillance research. The mobilization and spread of carbapenem resistance genes occur across hu- man, animal and environmental sectors. It has now become inevitable to monitor drug resistance by phenotypic methods or detection of ARGs by molecular methods such as polymerase chain reaction (PCR). Surveillance and intervention research related to car- bapenem resistance must involve the One Health framework. There is a need of unified, global surveillance networks with standard operating procedures and willingness to ded- icate efforts to combat antibiotic resistance. This all requires standardized protocols to carry out metagenomic analyses of water, food, agricultural runoff, and wildlife to identify environmental reservoirs and transmission corridors that link to clinical outbreaks [180].

  generalfuture workevidence 5/5
  Keywords: resistance clinical surveillance bacterial mges sequencing transmission transfer drug efforts carbapenem must predictive framework applying
• Evaluation of Antibiotic Resistance Mechanisms in Gram-Positive Bacteria (2024) · Antibiotics · cited 75× · doi

  Antimicrobial resistance (AMR) continues to be a serious global health concern, partic- ularly with Gram-positive bacteria such as Staphylococcus aureus, Streptococcus pneumoniae, and Enterococcus faecium. These pathogens are highlighted in the 2024 World Health Or- ganization (WHO) high-priority list for their ability to cause severe infections and their growing resistance to standard antibiotics [14]. The extensive application of antibiotics in medical treatment has led to drug resistance and heightened the risk of the emergence of super-resistant bacteria [148]. Antimicrobial resistance in bacteria through horizontal gene transfer (HGT) is an ongoing process driven by genetic mutations and the spread of drug resistance genes between bacteria. Human activities significantly contribute to this problem, impacting people globally. To address this urgent issue, coordinated international efforts are essential. Adopting thorough strategies is crucial to effectively combat bacterial resistance [137]. Developing new antibiotics targeting the pathogens listed in the WHO Bacterial Priority Pathogens List (BPPL) presents significant scientific and commercial challenges. Although 42 antibiotics are under clinical development, only 11 of these have the potential to treat pathogens on the WHO’s critical threat list. A significant scientific challenge in antibiotic development is the complexity of discovering molecules that are selectively harmful to bacteria without causing damage to human cells. These antibiotics must not only efficiently inhibit bacterial growth or kill bacteria but also avoid mechanisms that promote resistance. Antibiotics are prescribed for short durations and priced lower than chronic disease treatments, making them less profitable. New antibiotics are reserved as a last resort to prevent resistance, limiting sales and market growth even after regulatory approval, discouraging private investment, and creating funding gaps in R&D [149]. The development and availability of new antibiotics have not kept up with the rapid progression of antimicrobial resistance [137]. Identifying antibiotic-resistant genes (ARGs) presents significant challenges due to the need for precise accuracy in practical treatments and the robustness to rapidly identify issues. Current laboratory characterization and diagnostic methods often fall short, yielding inconsistent results influenced by varying environmental and laboratory conditions. Consequently, the use of artificial intelligence (AI) techniques, particularly machine learning (ML) and deep learning (DL), has become essential [150]. Applying emerging therapies for early identification of infectious diseases and distin- guishing between infectious and non-infectious conditions is critical in addressing antibiotic resistance. In 2019, Yelin et al. developed a model for detecting urinary tract infections (UTIs) that leveraged personal clinical histories through a machine learning-based AMR prediction appr

  generalfuture workevidence 5/5
  Keywords: resistance antibiotics bacteria pathogens antimicrobial list bacterial significant development antibiotic learning infectious health priority infections
• Gut Microbiota Modulation in IBD: From the Old Paradigm to Revolutionary Tools (2025) · International Journal of Molecular Sciences · cited 26× · doi

  incomplete sequencing of bacterial genomes complicates the determination of optimal bacterial species concentrations and hinders the ability to predict potential side effects - clinical translation of these approaches has been limited, as the human microbiota differs substantially from that of animal models - the long-term impact on host tissues and the microbial ecosystem remains poorly understood - potential antibiotic resistance mechanisms and the risk of exacerbating infections - manufacturing complexity and costs may limit accessibility to these therapies

  generallimitations sectionevidence 5/5
  Keywords: incomplete sequencing bacterial genomes complicates determination optimal species

Questions about this gap