Mycobacterial metabolic model development for drug target identification.

Antibiotic resistance is increasing at an alarming rate, and three related mycobacteria are sources of widespread infections in humans. According to the World Health Organization, , which causes leprosy, is still endemic in tropical countries; is the second leading infectious killer worldwide after COVID-19; and , a group of non-tuberculous mycobacteria, causes lung infections and other healthcare-associated infections in humans. Due to the rise in resistance to common antibacterial drugs, it is critical that we develop alternatives to traditional treatment procedures.

CONTRABASS: exploiting flux constraints in genome-scale models for the detection of vulnerabilities.

Motivation: Despite the fact that antimicrobial resistance is an increasing health concern, the pace of production of new drugs is slow due to the high cost and uncertain success of the process. The development of high-throughput technologies has allowed the integration of biological data into detailed genome-scale models of multiple organisms. Such models can be exploited by means of computational methods to identify system vulnerabilities such as chokepoint reactions and essential reactions.

Mycobacterium abscessus pathogenesis identified by phenogenomic analyses.

The medical and scientific response to emerging and established pathogens is often severely hampered by ignorance of the genetic determinants of virulence, drug resistance and clinical outcomes that could be used to identify therapeutic drug targets and forecast patient trajectories. Taking the newly emergent multidrug-resistant bacteria Mycobacterium abscessus as an example, we show that combining high-dimensional phenotyping with whole-genome sequencing in a phenogenomic analysis can rapidly reveal actionable systems-level insights into bacterial pathobiology. Through phenotyping of 331 clinical isolates, we discovered three distinct clusters of isolates, each with different virulence traits and associated with a different clinical outcome.

Integrated human/SARS-CoV-2 metabolic models present novel treatment strategies against COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic caused by the new coronavirus (SARS-CoV-2) is currently responsible for more than 3 million deaths in 219 countries across the world and with more than 140 million cases. The absence of FDA-approved drugs against SARS-CoV-2 has highlighted an urgent need to design new drugs. We developed an integrated model of the human cell and SARS-CoV-2 to provide insight into the virus' pathogenic mechanism and support current therapeutic strategies.

Predicted structural mimicry of spike receptor-binding motifs from highly pathogenic human coronaviruses.

Viruses often encode proteins that mimic host proteins in order to facilitate infection. Little work has been done to understand the potential mimicry of the SARS-CoV-2, SARS-CoV, and MERS-CoV spike proteins, particularly the receptor-binding motifs, which could be important in determining tropism and druggability of the virus. Peptide and epitope motifs have been detected on coronavirus spike proteins using sequence homology approaches; however, comparing the three-dimensional shape of the protein has been shown as more informative in predicting mimicry than sequence-based comparisons.

Stepwise pathogenic evolution of .

Although almost all mycobacterial species are saprophytic environmental organisms, a few, such as , have evolved to cause transmissible human infection. By analyzing the recent emergence and spread of the environmental organism through the global cystic fibrosis population, we have defined key, generalizable steps involved in the pathogenic evolution of mycobacteria. We show that epigenetic modifiers, acquired through horizontal gene transfer, cause saltational increases in the pathogenic potential of specific environmental clones.

SARS-CoV-2 3D database: understanding the coronavirus proteome and evaluating possible drug targets.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a rapidly growing infectious disease, widely spread with high mortality rates. Since the release of the SARS-CoV-2 genome sequence in March 2020, there has been an international focus on developing target-based drug discovery, which also requires knowledge of the 3D structure of the proteome. Where there are no experimentally solved structures, our group has created 3D models with coverage of 97.

Mabellini: a genome-wide database for understanding the structural proteome and evaluating prospective antimicrobial targets of the emerging pathogen Mycobacterium abscessus.

Mycobacterium abscessus, a rapid growing, multidrug resistant, nontuberculous mycobacteria, can cause a wide range of opportunistic infections, particularly in immunocompromised individuals. M. abscessus has emerged as a growing threat to patients with cystic fibrosis, where it causes accelerated inflammatory lung damage, is difficult and sometimes impossible to treat and can prevent safe transplantation.

Mycobacterial genomics and structural bioinformatics: opportunities and challenges in drug discovery.

Of the more than 190 distinct species of Mycobacterium genus, many are economically and clinically important pathogens of humans or animals. Among those mycobacteria that infect humans, three species namely Mycobacterium tuberculosis (causative agent of tuberculosis), Mycobacterium leprae (causative agent of leprosy) and Mycobacterium abscessus (causative agent of chronic pulmonary infections) pose concern to global public health. Although antibiotics have been successfully developed to combat each of these, the emergence of drug-resistant strains is an increasing challenge for treatment and drug discovery.

Multispecies reconstructions uncover widespread conservation, and lineage-specific elaborations in eukaryotic mRNA metabolism.

The degree of conservation and evolution of cytoplasmic mRNA metabolism pathways across the eukaryotes remains incompletely resolved. In this study, we describe a comprehensive genome and transcriptome-wide analysis of proteins involved in mRNA maturation, translation, and mRNA decay across representative organisms from the six eukaryotic super-groups. We demonstrate that eukaryotes share common pathways for mRNA metabolism that were almost certainly present in the last eukaryotic common ancestor, and show for the first time a correlation between intron density and a selective absence of some Exon Junction Complex (EJC) components in eukaryotes.