Arijit Bhattacharya, Author at Adamas University

(Student Contributor: Prapam Nandi, UGIV-Biochemistry, Adamas University).

Despite various assumptions indicating deviations on the number of microbial cells in a healthy human body, it seems a fact that the number of bacteria easily outnumber the total number of human cells (Sender et al., 2016). Better known as the microbiome, trillions of microbes representing thousands of species including bacteria, viruses, fungi, and archaea inhabit the human body. If we consider the gene pool, its more than 20 million genes among them compared to a mere 20,000 genes that human genome harbors. The advent of high throughput and deep sequencing empowered metagenomic analysis enabled us to explore the compositions, organization, and multitudinous role of microbiome linked to health. Dysbiosis (loss of a specific member of normal microflora) has been associated with a cluster of chronic diseases (Wilkins et al., 2019). Gut microbiome, the major hub of microbes we host in our system, is regulating various response cascades including immune function, energy metabolism, and aging, to mention a few. Exploration of such regulation rendered us to perceive maneuvering microbiome as a therapeutic strategy. Microbiome grafting including fecal microbiota transplantation (FMT) has become a considerably successful bacterio-therapy for recurrent Clostridium difficile infection (Kachlíková et al., 2020). Revealing the factors influencing the composition of the microbiota and studying the dynamics are blossoming areas of microbiology research.

The gut microbiome, (~ 2 kilograms in mass, weighs more than most of the human organs) has been linked to several physiological responses. Whether it can affect behavior, cognition and mental health remained elusive until a decade ago, albeit various observations hinted toward such association for long. For example, persons with irritable bowel syndrome tend to be depressed, and on the flip side people on the autism and Parkinsons’s spectrum tend to have digestive problems. The gut-brain axis has been explored quite recently with Evans et al. reporting a direct correlation between the severity of the bipolar disorder and gut microbiome composition, particularly the lower abundance of Faecalibacterium in affected individuals (Evans et al., 2016). Earlier, a strong variance of gut microbiome composition between individuals with a major depressive disorder to those of healthy controls was reported with emulation of depression-like features by microbiome grafting in mice (Li et al., 2018). The same group linked the glucocorticoid receptor pathway and glycerophospholipid metabolism with behavior in nonhuman primate models (Luo et al., 2018). Lowry et al., reported that immunization with Mycobacterium vaccae reduced inflammatory response in the brain and curbed anxiety. In a similar study, they also observed that immunization after fear conditioning enhances fear extinction and ameliorate postoperative cognitive dysfunction (POCD, a common problem for older adults after surgery) (Hassel et al., 2019). With their consistent effort to characterize microbiome induced metabolic modulation, Cryan et al., have strengthened the relationship between gut microbes and brain. Murine models with fecal transplants from individuals with Parkinson’s, schizophrenia, autism, or depression recorded to develop rodent equivalents of such ailments (Golubeva et al., 2017). Microbe mediated tryptophan metabolism leads to the generation of serotonin (neurotransmitter) or kynurenine (pro-toxic product). The impact of dysbiosis on social brain function as evidenced by an association study that showed that autism-like condition had lower levels of Bifidobacterium and Blautia gut bacteria, their guts made less tryptophan and bile acid(needed for producing serotonin). Moreover in children with autism altered levels of Veillonellaceae, Coprococcus, and Prevotella gut bacteria were enumerated compared to individuals without such conditions (Golubeva et al., 2017).

Mechanistic understanding of the gut-brain axis remained obscure until very recently when Valles-Colomer et al. reported the outcome of a robust sequencing project comprising a fecal microbiome analysis of more than 1000 individuals (microbiome population cohort)which suggested the robust correlation with gut microbial activity and depression. Interestingly, with a higher quality of life, butyrate-producing bacteria Faecalibacterium and Coprococcuscould be associated while in depression condition, depletion of bacteria like Dialister, Coprococcus spp. were detected. Implementing a module-based framework for analysis, they assembled neuro-active potential of gut prokaryotes, which indicated microbial synthesis potential of the 3,4-dihydroxyphenylacetic acid (a metabolite for the neurotransmitter dopamine) with mental health and the potential role of microbial γ-aminobutyric acid production (GABA, an inhibitory neurotransmitter) in depression (Valles-Colomer et al., 2019).

In one of their recent works, Simpson et al. established the link between the presence of specific microbes with neurodevelopment and neurological disorders. Using α-syncluin overexpressing mice (mimicking synucleinopathies exemplified by Parkinson’s disease) they established the gut bacteria-neuro-degeneration link elegantly. The study indicated that alteration of the human microbiome represents an elevated risk of neurodegeneration (Sampson et al., 2020).

Based on such concepts, entrepreneurship endeavors are already underway. Phil Strandwitz, CEO of Holobiome, and his team are actively engaged in identifying microbes with psychobiont potential. Primarily they have identified Bacteroides, Parabacteroides, and Escherichia species as bacteria with GABA producing pathways through transcriptome analysis of human stool samples (Strandwitz et al., 2019). At Holobiome they have identified and ranked 30 promising GABA-producing bacteria and also discovered that the bacterially produce GABA in the gut increases its levels in the brain. The group is trying to develop a consortium of bacteria that would comprise a broader range of species and will be able to target various psychological conditions like depression (https://www.sciencemag.org/news/2020/05/meet-psychobiome-gut-bacteria-may-alter-how-you-think-feel-and-act#). Barring such consortia approach, prebiotic trials are also being attempted with the gross aim to enrich a good psychobiome. In one such trial Grimaldi et al. report Bimuno^® galactooligosaccharide (B-GOS^®) prebiotic intervention driving, enrichment of Lachnospiraceae family, and improvements in anti-social behavior (Grimaldi et al., 2018).

Despite its tremendous potential, optimizing microbiome therapy is not at all a smooth sail. Microbe-metal health interface research is still at a sprouting stage, primarily deciphering microbial metabolism linked to neurotransmitter generation/ stability. There are dimensions yet to be explored to have a systemic understanding. It would require a robust association study comprising substantial population and individual data. Individual microbiomes frequently expel the bacteria in standard probiotics, preventing the probiotic species from becoming established in gut microbiome (Suez et al., 2018). In this context, adopting parameters similar to precision medicine development while grafting microbiomes might accentuate the successful development of psychobiome therapy.

References:

Evans SJ, Bassis CM, Hein R, et al. The gut microbiome composition associates with bipolar disorder and illness severity. J Psychiatr Res. 2017;87:23‐29. doi:10.1016/j.jpsychires.2016.12.007

Golubeva AV, Joyce SA, Moloney G, et al. Microbiota-related Changes in Bile Acid & Tryptophan Metabolism are Associated with Gastrointestinal Dysfunction in a Mouse Model of Autism. EBioMedicine. 2017;24:166‐178. doi:10.1016/j.ebiom.2017.09.020

Grimaldi R, Gibson GR, Vulevic J, et al. A prebiotic intervention study in children with autism spectrum disorders (ASDs). Microbiome. 2018;6(1):133. Published 2018 Aug 2. doi:10.1186/s40168-018-0523-3

Hassell JE Jr, Fox JH, Arnold MR, et al. Treatment with a heat-killed preparation of Mycobacterium vaccae after fear conditioning enhances fear extinction in the fear-potentiated startle paradigm. Brain Behav Immun. 2019;81:151‐160. doi:10.1016/j.bbi.2019.06.008

https://www.sciencemag.org/news/2020/05/meet-psychobiome-gut-bacteria-may-alter-how-you-think-feel-and-act#

Kachlíková M, Sabaka P, Koščálová A, Bendžala M, Dovalová Z, Stankovič I. Comorbid status and the faecal microbial transplantation failure in treatment of recurrent Clostridioides difficile infection – pilot prospective observational cohort study. BMC Infect Dis. 2020;20(1):52. Published 2020 Jan 16. doi:10.1186/s12879-020-4773-x

Li B, Guo K, Zeng L, et al. Metabolite identification in fecal microbiota transplantation mouse livers and combined proteomics with chronic unpredictive mild stress mouse livers. Transl Psychiatry. 2018;8(1):34. Published 2018 Jan 31. doi:10.1038/s41398-017-0078-2

Luo Y, Zeng B, Zeng L, et al. Gut microbiota regulates mouse behaviors through glucocorticoid receptor pathway genes in the hippocampus. Transl Psychiatry. 2018;8(1):187. Published 2018 Sep 7. doi:10.1038/s41398-018-0240-5

Sampson TR, Challis C, Jain N, et al. A gut bacterial amyloid promotes α-synuclein aggregation and motor impairment in mice. Elife. 2020;9:e53111. Published 2020 Feb 11. doi:10.7554/eLife.53111

Sender R, Fuchs S, Milo R. Revised Estimates for the Number of Human and Bacteria Cells in the Body. PLoS Biol. 2016;14(8):e1002533. Published 2016 Aug 19. doi:10.1371/journal.pbio.1002533

Strandwitz P, Kim KH, Terekhova D, et al. GABA-modulating bacteria of the human gut microbiota. Nat Microbiol. 2019;4(3):396‐403. doi:10.1038/s41564-018-0307-3

Suez J, Zmora N, Zilberman-Schapira G, et al. Post-Antibiotic Gut Mucosal Microbiome Reconstitution Is Impaired by Probiotics and Improved by Autologous FMT. Cell. 2018;174(6):1406‐1423.e16. doi:10.1016/j.cell.2018.08.047

Valles-Colomer M, Falony G, Darzi Y, et al. The neuroactive potential of the human gut microbiota in quality of life and depression. Nat Microbiol. 2019;4(4):623‐632. doi:10.1038/s41564-018-0337-x

Wilkins LJ, Monga M, Miller AW. Defining Dysbiosis for a Cluster of Chronic Diseases. Sci Rep. 2019;9(1):12918. Published 2019 Sep 9. doi:10.1038/s41598-019-49452-y

Student contributor: Akash Mitra, PG-IV Microbiology

The tussle between pathogens and antimicrobial therapy is essentially in the evolutionary arms race. The specific mutation for stabilizing the drug-tolerant property and horizontal gene transfer, offering selection advantage against antimicrobials, are the common modes for resistance acquisition as observed for antibiotic-resistant bacteria. Often the rate of mutation acquisition is enhanced from a transient hypermutation phenotype, with loss of maintenance of fidelity for genome duplication owing to dysfunctional mismatch repair or by error-prone translation repair mechanism. However, for obligate parasites including viruses, the evolutionary landscape is somewhat multidimensional in terms of dynamic intra-host drug concentrations resulting in differential selection pressure. Mutations induct considerable fitness cost for basic processes associated with parasitism. Antiviral resistance often accompanies less robust therapeutic regimens insufficient to diminish viral replication, thereby imposing selection pressure and eventuates rapid adaptation leading to resistance. With the high replication rate for large population size, the resistance-conferring polymorphisms emerge promptly in the viral genome (Irwin et al., 2016).

While strategizing drug designing, a key factor to consider is sustainability. Genetic barrier, i.e. number of genetic changes required for building resistance is the cornerstone in determining the period for optimum applicability of an antimicrobial. For some of the older drugs like the first generation reverse transcriptase inhibitors, the genetic barrier was indeed feeble and mere one or two variations impart resistance. The genetic barrier should not be perceived as a mere number (of mutation) but the type of genetic variation that would result in resistance also influences the magnitude of the barrier. Transition mutations are often common compared to transversions. Drugs requiring to transversions (for resistance) should impose greater impediments compared to a transition. Additionally, a higher genetic barrier requires building an integrated mutation network to attain a stable and substantial level of resistance. Such a situation imposes deleterious trade-offs like reduced replication rate and deregulation of gene expression which often surge beyond error threshold causing deleterious mutations (and lethal) to occur.Such situations cause depletion of faithful replication and eventual extinction. However often compensatory mutations emerge to decrease the fitness cost imposed by such trade-offs (Gotte 2012). Therefore, a clear understanding of resistance emergence from an evolutionary perspective is imperative for strategizing antiviral regiments including developing novel, repurposed, or combinatorial therapy.

RNA viruses replicate close to error threshold and are prone to ‘error catastrophe’. Although this might appear an inherently unstable evolutionary strategy, provided that viral population sizes are sufficiently large, life at the error threshold does allow RNA viruses to produce effective mutations within a few replication cycles. So a prospective rational for next-generation drug development can be aiming at exceeding an error threshold. Favipiravir is one such drug that targets RNA dependent RNA polymerase of RNA viruses including Coronaviridae and enhances wrong base incorporation.Although targeting RNA dependent RNA polymerases (RdRp) might be considered as a strategy to induce “error catastrophe”, mechanistic understanding of components of replication for the virus shed light on various other factors that can be targeted. Like many other RNA virus, coronavirus (CoV) RNA-dependent RNA polymerases (RdRp) lack co- and post-replicative proof-reading pathways as reflected by the incorporation of mutations at a considerably elevated rate. RdRps have been exploited as a favored node for intervention as being prone to incorporate nucleotide analogs like ribavirin into nascent viral RNA during genome amplification (Crotty et al., 2000). For CoV, resistance inducing mutation against3C-Like protease Inhibitor displayed considerable fitness cost indicative of low genetic barrier (Deng et al., 2014). Interestingly, in this group of the virus, nsp14 protein with 3′-5′ exoribonuclease (ExoN) in the amino (N) terminus (Eckerle et al., 2010) mediates mismatch correction. Inactivation of nsp14-ExoN of SARS-CoV results in ~20-fold more mutations in genomes than for wild-type (wt) viruses in infected cells (Eckerle et al., 2010). The ExoN domain (amino acids 1–287) with a DEEDh catalytic motif and the N7-MTase domain (amino acids 288–527) and a DxGxPxG/A SAM-binding/catalytic domain of nsp14 are distinct in structure among other members of the same enzyme family. The N7cap-MTase domain is also linked to RNA processing as it generates Cap0 of mRNA and this reaction is particularly crucial for recognition by the host ribosome (Chen et al., 2016). Inhibiting NSP14 mediated mismatch correction might be a viable strategy to induce ‘error catastrophe’ during replication. With the ongoing effort to screen specific inhibitor against NSP14 (Fig.1) and deciphering the evolutionary impact of such inhibition on mutational dynamics can accentuate the development of a sustainable drug against SARS-CoV-2.

References:

Chen Y, Guo D.Molecular mechanisms of coronavirus RNA capping and methylation.Virol Sin. 2016 Feb;31(1):3-11. doi: 10.1007/s12250-016-3726-4.

Crotty S, Maag D, Arnold JJ, Zhong W, Lau JY, Hong Z, Andino R, Cameron CE.

Eckerle LD, Becker MM, Halpin RA, Li K, Venter E, Lu X, Scherbakova S, Graham RL, Baric RS, Stockwell TB, Spiro DJ, Denison MR. Infidelity of SARS-CoV Nsp14-exonuclease mutant virus replication is revealed by complete genome sequencing.PLoSPathog. 2010 May 6;6(5):e1000896.

Götte M.The distinct contributions of fitness and genetic barrier to the development of antiviral drug resistance.CurrOpinVirol. 2012 Oct;2(5):644-50

Irwin KK, Renzette N, Kowalik TF, Jensen JD.Antiviral drug resistance as an adaptive process.Virus Evol. 2016 Jun 10;2(1):vew014.

The broad-spectrum antiviral ribonucleoside ribavirin is an RNA virus mutagen. Nat Med. 2000 Dec;6(12):1375-9

Fig1. Putative inhibitor for nsp14.Molecular docking analysis depicting potential interaction between the ExoN domain of SARS-CoV-2 nsp14 and a candidate molecule annotated as ExoI.5 (unpublished data).

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Author: Arijit Bhattacharya

Psychobiome and the microbe-mind interface

The evolutionary approach to drug development: a resistance perspective