SUMMARYChanges in transcriptional regulatory networks can significantly contribute to species evolution and adaptation. However, identification of genome-scale regulatory networks is an open challenge, especially in non-model organisms. Here, we introduce multi-species regulatory network learning (MRTLE), a computational approach that uses phylogenetic structure, sequence-specific motifs, and transcriptomic data, to infer the regulatory networks in different species. Using simulated data from known networks and transcriptomic data from six divergent yeasts, we demonstrate that MRTLE predicts networks with greater accuracy than existing methods because it incorporates phylogenetic information. We used MRTLE to infer the structure of the transcriptional networks that control the osmotic stress responses of divergent, non-model yeast species and then validated our predictions experimentally. Interrogating these networks reveals that gene duplication promotes network divergence across evolution. Taken together, our approach facilitates study of regulatory network evolutionary dynamics across multiple poorly studied species.

Leptospirosis is an emerging zoonotic disease endemic in Kerala and close monitoring of emerging serovars is essential to adopt appropriate control strategies. Multi-Locus Sequence Typing (MLST) was reported to be less expensive compared to other cumbersome methods like whole genome sequencing. The present study was conducted to obtain isolates of Leptospira from infected animals and rats and for the identification of serovars using MLST. A total of 205 blood samples (dog, cat, cattle, goat), 43 urine samples (dog, cattle) and post-mortem kidney samples from various animals such as dog (n=12), cattle (n=2) and rat (n=25) were collected and subjected to polymerase chain reaction (PCR) using G1/G2 primers to identify the pathogenic Leptospira. Fifteen samples were found to be positive, these samples when inoculated in the Ellinghausen- McCullough-Johnson-Harris (EMJH) semi-solid medium to obtain ten isolates. These ten isolates were further subjected to secY, icdA and GyraseB PCR and sequenced. The obtained sequences were analysed using BLAST and were fed into specified MLST database of Leptospira scheme-3, the allelic profile and sequence type were generated. The MLST results obtained in the study indicated that the isolates S24 and S33 belonged to serovar Canicola, S40 and 47 were Sejroe and S19, S27, S55, S69 and S71 were Bataviae, Autumnalis, Pomona, Icterohaemorraghiae and Australis, respectively. It was concluded that MLST is a convenient method for identifying leptospiral serovars.

Coagulase negative staphylococci (CNS) are emerging as the most prevalent causative agent of bovine mastitis. They are resistant to many commonly used antibiotics due to the presence of antimicrobial resistance (AMR) genes. A study was conducted to evaluate the AMR profiling of CNS isolated from bovine subclinical mastitis.Coagulase negative staphylococci were isolated from 49 (44.95 per cent) of the subclinical mastitis samples. Disc diffusion assay revealed that highest resistance was shown against gentamicin (42.85 per cent) followed by methicillin (32.6 per cent), ceftriaxone – tazobactam (24.48 per cent), enrofloxacin (20.4 per cent), tetracycline (16.32 per cent) and least resistance to cotrimoxazole (4 per cent). Genotypic characterisation of AMR genes such as mecA, aacA-aphD and norA by PCR was done for determining resistance to methicillin, gentamicin and fluroquinolone resistance. The CNS carried aacA-aphD, norA and mecA in 44.89 per cent, 32.65 per cent and 14.28 per cent, respectively. Comparison of phenotypic and genotypic characterisation of AMR in CNS was carried out by McNemar test and it was found that there was significant difference between the presence of mecA gene and methicillin resistance. There was no significant difference noticed forcharacterisation of phenotypic and genotypic AMR of CNS for gentamicin and fluroquinolone resistance.