Sanjay K. Shukla

Digital pathology (DP) entails converting physical histologic data into a digital format, ultimately enhancing data transmission, archival, and retrieval. Moreover, digitization serves as an essential prerequisite for utilizing artificial intelligence programs in histologic image analysis. Clinical use of DP has had limited uptake despite its transformative potential and numerous proposed benefits. This paper explores the commonly cited workflow, regulatory, and financial barriers currently hindering DP implementation based on the published experiences of early adopters. It provides an overview of topics including relevant regulatory bodies and guidelines, cost-to-benefit analysis, data management, and the role of artificial intelligence within DP.

Background: Phenome-WideAssociation study (PheWAS) is a powerful tool designed to systematically screen clinical observations derived from medical records (phenotypes) for association with a variable of interest. Despite their usefulness, no systematic screening of phenotypes associated withStaphylococcusaureusinfections(SAIs) has been done leaving potential novel risk factors or complications undiscovered.Method and cohorts: We tailored the PheWAS approach into a two-stage screening procedure to identify novel phenotypes correlating with SAIs. The first stage screened for co-occurrence of SAIs with other phenotypes within medical records. In the second stage, significant findings were examined for the correlations between their age of onset with that of SAIs. The PheWAS was implemented using the medical records of 754,401 patients from the Marshfield Clinic Health System. Any novel associations discovered were subsequently validated using datasets from TriNetX andAll of Us, encompassing 109,884,571 and 118,538 patients respectively.Results: Forty-one phenotypes met the significance criteria of a p-value < 3.64e-5 and odds ratios of > 5. Out of these, we classified 23 associations either as risk factors or as complications of SAIs. Three novel associations were discovered and classified either as a risk (long-term use of aspirin) or complications (iron deficiency anemia and anemia of chronic disease). All novel associations were replicated in the TriNetX cohort. In theAll of Uscohort, anemia of chronic disease was replicated according to our significance criteria.Conclusions: The PheWAS of SAIs expands our understanding of SAIs interacting phenotypes. Additionally, the novel two-stage PheWAS approach developed in this study can be applied to examine other disease-disease interactions of interest. Due to the possibility of bias inherent in observational data, the findings of this study require further investigation.

Globally, half a billion people are employed in animal agriculture and are directly exposed to the associated microorganisms. However, the extent to which such exposures affect resident human microbiomes is unclear. Here we conducted a longitudinal profiling of the nasal and faecal microbiomes of 66 dairy farmers and 166 dairy cows over a year-long period. We compare farmer microbiomes to those of 60 age-, sex- and ZIP code-matched people with no occupational exposures to farm animals (non-farmers). We show that farming is associated with microbiomes containing livestock-associated microbes; this is most apparent in the nasal bacterial community, with farmers harbouring a richer and more diverse nasal community than non-farmers. Similarly, in the gut microbial communities, we identify more shared microbial lineages between cows and farmers from the same farms. Additionally, we find that shared microbes are associated with antibiotic resistance genes. Overall, our study demonstrates the interconnectedness of human and animal microbiomes.

Multiple sclerosis (MS) is a complex autoimmune disease in which both the roles of genetic susceptibility and environmental/microbial factors have been investigated. More than 200 genetic susceptibility variants have been identified along with the dysbiosis of gut microbiota, both independently have been shown to be associated with MS. We hypothesize that MS patients harboring genetic susceptibility variants along with gut microbiome dysbiosis are at a greater risk of exhibiting the disease. We investigated the genetic risk score for MS in conjunction with gut microbiota in the same cohort of 117 relapsing remitting MS (RRMS) and 26 healthy controls. DNA samples were genotyped using Illumina’s Infinium Immuno array-24 v2 chip followed by calculating genetic risk score and the microbiota was determined by sequencing the V4 hypervariable region of the 16S rRNA gene. We identified two clusters of MS patients, Cluster A and B, both having a higher genetic risk score than the control group. However, the MS cases in cluster B not only had a higher genetic risk score but also showed a distinct gut microbiome than that of cluster A. Interestingly, cluster A which included both healthy control and MS cases had similar gut microbiome composition. This could be due to (i) the non-active state of the disease in that group of MS patients at the time of fecal sample collection and/or (ii) the restoration of the gut microbiome post disease modifying therapy to treat the MS. Our study showed that there seems to be an association between genetic risk score and gut microbiome dysbiosis in triggering the disease in a small cohort of MS patients. The MS Cluster A who have a higher genetic risk score but microbiome profile similar to that of healthy controls could be due to the remitting phase of the disease or due to the effect of disease modifying therapies.

Background Multiple sclerosis (MS) is a complex autoimmune disease in which both the roles of genetic susceptibility and environmental/microbial factors have been investigated. More than 200 genetic susceptibility variants have been identified along with the dysbiosis of gut microbiota, both independently have been shown to be associated with MS. We hypothesize that MS patients harboring genetic susceptibility variants along with gut microbiome dysbiosis are at a greater risk of exhibiting the disease. We investigated the polygenic risk score for MS in conjunction with gut microbiota in the same cohort of 117 relapsing remitting MS (RRMS) and 26 healthy controls. DNA samples were genotyped using Illumina’s Infinium Immuno array-24 v2 chip followed by calculating polygenic risk score and the microbiota was determined by sequencing the V4 hypervariable region of the 16S rRNA gene. Results We identified two clusters of MS patients, Cluster A and B both having a higher polygenic risk score than the control group. The Cluster B with the higher polygenic risk score had a distinct gut microbiota, different than the Cluster A. MS group whose microbiome was similar to that of the control group despite a higher genetic risk score than the control group. This could be due to i) the non-active state of the disease in that group of MS patients at the time of fecal sample collection and/or ii) the restoration of the gut microbiome post disease modifying therapy to treat the MS. Conclusion Our study showed that there seems to be association between polygenic risk score and gut microbiome dysbiosis in triggering the disease in a small cohort of MS patients. The MS Cluster A who have a higher polygenic risk score but microbiome profile similar to that of healthy controls could be due to the remitting phase of the disease or due to the effect of disease modifying therapies.

The etiological complexity of multiple sclerosis, an immune-mediated, neurodegenerative disease with multifactorial etiology is still elusive because of an incomplete understanding of the complex synergy between contributing factors such as genetic susceptibility and aberrant immune response. Recently, the disease phenotypes have also been shown to be associated with dysbiosis of the gut microbiome, a dynamic reservoir of billions of microbes, their proteins and metabolites capable of mimicring the autoantigens. Microbial factors could potentially trigger the neuroinflammation and symptoms of MS. In this perspective article, we discussed how microbial molecules resulting from a leaky gut might mimic a host’s autoantigen, potentially contributing to the disease disequilibrium. It further highlights the importance of targeting the gut microbiome for alternate therapeutic options for the treatment of MS.

Background: Staphylococcus aureus, the most common pathogen in skin and soft tissue infections (SSTI), harbors many well-characterized virulence genes. However, the expression of many of them in SSTIs is unknown. In this study, S. aureus virulence genes expressed in SSTI were investigated. Methods: Fifty-three subjects presenting to the outpatient’s care and emergency departments with a purulent SSTI at two medical centers in Wisconsin, USA, were enrolled in the study. Total mRNA was extracted from the purulent or swab materials, made into cDNA and sequenced on MiSeq platform. The relative cDNA counts to gmk and identifications of the transcripts were carried out with respect to USA300 reference genome and using SAMTOOLS v.1.3 and BWA, respectively. Result: A significantly higher cDNA count was observed for many of the virulence and regulatory gene transcripts in the pus samples compared to the swab samples relative to the cDNA counts for gmk, a housekeeping gene. They were for lukS-PV (18.6 vs. 14.2), isaA (13.4 vs. 8.5), ssaA (4.8 vs. 3.1), hlgC (1.4 vs. 1.33), atl (17.7 vs. 8.33), clfA (3.9 vs. 0.83), eno (6.04 vs. 3.16), fnbA (5.93 vs. 0.33), saeS (6.3 vs. 1.33), saeR (5.4 vs. 3.33) and agrC (5.6 vs. 1.5). Conclusions: A relative increase in the transcripts of several toxins, adhesion and regulatory genes with respect to a gmk in purulent materials suggests their role in situ during SSTIs, perhaps in an orchestrated manner.

The addition of dairy cow manure—stored in manure pits—to field soil has the potential to introduce not only organic nutrients but also mammalian microbial communities and antimicrobial resistance genes (ARGs) to soil communities. Using shotgun sequencing paired with functional metagenomics, we showed that microbial community composition changed between fresh manure and manure pit samples with a decrease in gut-associated pathobionts, while ARG abundance and diversity remained high.