Antibiotics are among the most successful drugs used for human therapy. However, since they can challenge microbial populations, they must be considered as important pollutants as well. Besides being used for human therapy, antibiotics are extensively used for animal farming and for agricultural purposes. Residues from human environments and from farms may contain antibiotics and antibiotic resistance genes that can contaminate natural environments. The clearest consequence of antibiotic release in natural environments is the selection of resistant bacteria. The same resistance genes found at clinical settings are currently disseminated among pristine ecosystems without any record of antibiotic contamination. Nevertheless, the effect of antibiotics on the biosphere is wider than this and can impact the structure and activity of environmental microbiota. Along the article, we review the impact that pollution by antibiotics or by antibiotic resistance genes may have for both human health and for the evolution of environmental microbial populations. The article reviews the current knowledge on the effects that pollution by antibiotics and antibiotic resistance genes may have for the microbiosphere.

The recurrence of reports citing water quality impairments in watersheds is evidence that tools are needed to identify pollution sources and facilitate restoration efforts such as implementing total maximum daily limits (TMDLs) or best management practices (BMPs). Fecal bacteria in surface waters are one of the most commonly cited impairments to water quality. This study evaluated microbial source tracking (MST), specifically multiple antibiotic resistance (MAR) analysis, as a management tool to differentiate nonpoint source pollution into source groups. A library containing Escherichia coli (E. coli, EC) and fecal streptococci (FS) isolates from poultry (EC n = 282, FS n = 650), human (EC n = 152, FS n = 240), wildlife (EC n = 17, FS n = 43), horse (EC n = 79, FS n = 82), dairy cattle (EC n = 38, FS n = 42), and beef cattle (EC n = 49, FS n = 46) sources was created. The MAR analysis was conducted on the isolates using a profile of seven antibiotics. The antibiotic signatures of unknown source isolates from Elkhorn and Hickman Creek watersheds were evaluated against the library to determine the contributions of potential fecal inputs from the respective sources. Correct classification was >60% when analyzed at the human and non-human-level of classification. On a watershed basis, both watersheds produced similar results; inputs from non-human sources were the greatest contributors to nonpoint source pollution. The results from the multiple antibiotic resistance (MAR) analysis revealed that the information produced, coupled with knowledge of the watershed and its associated land uses, would be helpful in allocating resources to remediate impaired water quality in such watersheds.