While it is reported that COVID-19 patients are more prone to secondary bacterial infections, which are strongly linked to the severity of complications of the disease, bacterial coinfections associated with COVID-19 are not widely studied. This work aimed to investigate the prevalence of bacterial coinfections and associated antibiotic resistance profiles among hospitalised COVID-19 patients. Age, gender, weight, bacterial identities, and antibiotic sensitivity profiles were collected retrospectively for 108 patients admitted to the intensive care unit (ICU) and non-ICU ward of a single center in Saudi Arabia. ICU patients (60%) showed a significantly higher percentage of bacterial coinfections in sputum (74%) and blood (38%) samples, compared to non-ICU. Acinetobacter baumannii (56%) and Klebsiella pneumoniae (56%) were the most prevalent bacterial species from ICU patients, presenting with full resistance to all tested antibiotics except colistin. By contrast, samples of non-ICU patients exhibited infections with Escherichia coli (31%) and Pseudomonas aeruginosa (15%) predominantly, with elevated resistance of E. coli to piperacillin/tazobactam and trimethoprim/sulfamethoxazole. This alarming correlation between multi-drug resistant bacterial coinfection and admission to the ICU requires more attention and precaution with prescribed antibiotics to limit the spread of resistant bacteria and improve therapeutic management.

The devastating nosocomial resistance is an on-going global concern. Surveillance of resistance is crucial for efficient patient care. This study was aimed to conduct a surveillance in four major Ha’il Hospitals from September to December 2020. Using a multipoint program, records of 621 non-duplicate Gram-negative cultures were tested across 21 drugs belonging to different categories. Major species were Klebsiella pneumoniae (n = 187, 30%), E. coli (n = 151, 24.5%), Pseudomonas aeruginosa, (n = 84, 13.6%), Acinetobacter baumannii (n = 82, 13.3%), and Proteus mirabilis (n = 46, 7%). Based on recent resistance classifications, A. baumanni, P. aeruginosa, and enteric bacteria were defined as pan-resistant, extremely resistant, and multi-drug resistant, respectively. A. baumannii (35%) and K. pneumoniae (23%) dominated among coinfections in SARS-CoV2 patients. The “other Gram-negative bacteria” (n = 77, 12.5%) from diverse sources showed unique species-specific resistance patterns, while sharing a common Gram-negative resistance profile. Among these, Providencia stuartii was reported for the first time in Ha’il. In addition, specimen source, age, and gender differences played significant roles in susceptibility. Overall infection rates were 30% in ICU, 17.5% in medical wards, and 13.5% in COVID-19 zones, mostly in male (59%) senior (54%) patients. In ICU, infections were caused by P. mirabilis (52%), A. baumannii (49%), P. aeruginosa (41%), K. pneumoniae (24%), and E. coli (21%), and most of the respiratory infections were caused by carbapenem-resistant A. baumannii and K. pneumoniae and UTI by K. pneumoniae and E. coli. While impressive IC, hospital performances, and alternative treatment options still exist, the spread of resistant Gram-negative bacteria is concerning especially in geriatric patients. The high selective SARS-CoV2 coinfection by A. baumannii and K. pneumoniae, unlike the low global rates, warrants further vertical studies. Attributes of resistances are multifactorial in Saudi Arabia because of its global partnership as the largest economic and pilgrimage hub with close social and cultural ties in the region, especially during conflicts and political unrests. However, introduction of advanced inter-laboratory networks for genome-based surveillances is expected to reduce nosocomial resistances.

Bacterial co-infections may aggravate COVID-19 disease, and therefore being cognizant of other pathogens is imperative. We studied the types, frequency, antibiogram, case fatality rates (CFR), and clinical profiles of co-infecting-pathogens in 301 COVID-19 patients. Co-infection was 36% (n = 109), while CFR was 31.2% compared to 9.9% in non-co-infected patients (z-value = 3.1). Four bacterial species dominated, namely, multidrug-resistant Klebsiella pneumoniae (37%, n = 48), extremely drug-resistant Acinetobacter baumannii (26%, n = 34), multidrug-resistant Eschericia. coli (18.6%, n = 24), and extremely drug-resistant Pseudomonas aeruginosa (8.5%, n = 11), in addition to other bacterial species (9.3%, n = 12). Increased co-infection of K. pneumoniae and A. baumannii was associated with increased death rates of 29% (n = 14) and 32% (n = 11), respectively. Klebsiella pneumoniae was equally frequent in respiratory and urinary tract infections (UTI), while E. coli mostly caused UTI (67%), and A. baumannii and P. aeruginosa dominated respiratory infections (38% and 45%, respectively). Co-infections correlated with advance in age: seniors ≥ 50 years (71%), young adults 21–49 years (25.6%), and children 0–20 years (3%). These findings have significant clinical implications in the successful COVID-19 therapies, particularly in geriatric management. Future studies would reveal insights into the potential selective mechanism(s) of Gram-negative bacterial co-infection in COVID-19 patients.

No abstract

The devastating SARS-CoV2 pandemic is worsening with relapsing surges, emerging mutants, and increasing mortalities. Despite enormous efforts, it is not clear how SARS-CoV2 adapts and evolves in a clonal background. Laboratory research is hindered by high biosafety demands. However, the rapid sequence availability opened doors for bioinformatics. Using different bioinformatics programs, we investigated 6305 sequences for clonality, expressions strategies, and evolutionary dynamics. Results showed high nucleotide identity of 99.9% among SARS-CoV2 indicating clonal evolution and genome. High sequence identity and phylogenetic tree concordance were obtained with isolates from different regions. In any given tree topology, ~50% of isolates in a country formed country-specific sub-clusters. However, abundances of subtle overexpression strategies were found including transversions, signature-sequences and slippery-structures. Five different short tracks dominated with identical location patterns in all genomes where Slippery-4 AAGAA was the most abundant. Interestingly, transversion and transition substitutions mostly affected the same amino acid residues implying compensatory changes. To ensure these strategies were independent of sequence clonality, we simultaneously examined sequence homology indicators; tandem-repeats, restriction-site, and 3′UTR, 5′ UTR-caps and stem-loop locations in addition to stringent alignment parameters for 100% identity which all confirmed stability. Nevertheless, two rare events; a rearrangement in two SARS-CoV2 isolates against betacoronavirus ancestor and a polymorphism in S gene, were detected. Thus, we report on abundance of transversions, slippery sequences, and ON/OFF molecular structures, implying adaptive expressions had occurred, despite clonal evolution and genome stability. Furthermore, functional validation of the point mutations would provide insights into mechanisms of SARS-CoV2 virulence and adaptation.