A strain is when a muscle is stretched too much and tears. It is also called a pulled muscle. A strain is a painful injury. It can be caused by an accident, overusing a muscle, or using a muscle in the wrong way.
strain
This strain of gonorrhea has been previously seen in Asia-Pacific countries and in the United Kingdom, but not in the US. A genetic marker common to these two Massachusetts residents was also previously seen in a case in Nevada, though that strain retained sensitivity to at least one class of antibiotics. Overall, these cases are an important reminder that strains of gonorrhea in the US are becoming less responsive to a limited arsenal of antibiotics.
Gonorrhea has been increasing in Massachusetts and nationally, adding to concerns about the potential spread of this strain which is more difficult to treat. In Massachusetts, laboratory-confirmed cases of gonorrhea have increased 312% since a low point of 1,976 cases in 2009 to 8,133 in 2021. Nationally, confirmed cases have risen by 131% between 2009 and 2021, with 696,764 cases reported in the US in 2021 according to preliminary data released by the CDC.
A strain is an injury to either a muscle or tendon. Tendons are the tough, fibrous bands of tissue that connect muscle to bone. With a back strain, the muscles and tendons that support the spine are twisted, pulled or torn.
Twisting or pulling a muscle or tendon can result in a strain. It can also be caused by a single instance of improper lifting or by overstressing the back muscles. A chronic (long-term) strain usually results from overuse after prolonged, repetitive movement of the muscles and tendons.
The most common complication of a back strain or sprain is a reduction in activity, which can lead to weight gain, loss of bone density, and loss of muscle strength and flexibility in other areas of the body.
Changes in microbial compositions following FMT have been studied with regard to phages22 or fungi23,24, yet the bulk of current knowledge is focused on bacteria and archaea where colonization by donor microbes and the persistence of indigenous recipient microbes emerge at the strain level of microbial populations25. Strain-level studies suggest that colonization levels following FMT vary across indications: whereas donor and recipient strains coexist long term in metabolic syndrome (MetS) patients25, donor takeover is the most common outcome in rCDI26,27,28, with intermediate outcomes in UC29 or obesity30,31. However, the factors shaping these differential strain-level outcomes remain poorly understood. In small pilot study cohorts, colonization success of donor strains leading to short-term persistence was associated with species phylogeny, broad microbial phenotypes and relative fecal abundances in rCDI26,27, but with more adaptive metabolic phenotypes in UC32.
In the study population used here, rCDI state was associated with a higher fraction of successfully colonizing donor strains in the post-FMT microbiome. However, we note that while >90% of patients with rCDI in our dataset received antibiotics before intervention, most patients for other indications did not (or underwent extended washout periods), hence rCDI and the effect of antibiotics cannot be disentangled. Moreover, in full models choosing from all variables, higher species richness in the recipient and individual species abundances were more robust predictors for the persistence of recipient strains than rCDI state. This suggests that the high levels of donor strain colonization observed in patients with rCDI may be due in part to a more precarious microbial community (possibly instigated or exacerbated by antibiotic use), rather than being a disease-specific effect.
We built LASSO models that were restricted to different subcategories of predictor variables and compared their performance with full models trained on the entire complements of ex ante or post hoc variables (Fig. 5a). Models trained exclusively on recipient pre-FMT species abundances, on abundance and strain population characteristics of the focal species and, to a lesser degree, on microbiome community diversity variables achieved highest accuracies, comparable to those of full models. Notably, predictive power of individual recipient species was due almost entirely to exclusion effects, meaning that the enrichment of certain species in the recipient was associated with less donor takeover or recipient strain turnover of others, while facilitation effects did not have a contributing role. Models restricted to procedural factors (including disease indication), pre-FMT metabolic state or donor species abundances achieved much lower accuracies than full models, indicating that these variable groups were less predictive of strain-level outcomes. Overall, we observed similar trends for models trained on post hoc variables (Fig. 5a, right).
We observed few prominent predictive species in the donor microbiota, most notably B. vulgatus and Evtepia gabavorous. Facilitation and inhibition effects of donor species were generally limited and overall less predictive of colonization success, indicating that the donor microbiota has limited impact on colonization outcome beyond intraspecific strain dynamics.
The strongest effects toward donor strain colonization emerged at species and strain level. Incoming species were more likely to colonize if they were phylogenetically or metabolically complementary to the residual community, implying that they were able to take over unoccupied niches. Colonization success was associated with complementarity specifically to the local community. High conspecific diversity in the donor and low diversity in the recipient were also linked with engraftment success: recipient populations dominated by single strains were less resilient, and donor strains from more diverse panels were more likely to colonize, probably due to strain-level-limiting similarity effects. Indeed, conspecific donor strain populations colonized more successfully if they were dissimilar to recipient strains, indicating strong inhibitive intraspecific priority effects.
We found that the turnover of recipient strains was very accurately predictable for almost all studied species, using a consistent and surprisingly small selection of ex ante microbiome variables. In contrast, our models achieved only moderate predictive accuracies when predicting takeover by donor strains, indicating that colonization is, to a large extent, stochastic or influenced by other factors outside the scope of our study, such as viral or eukaryotic microbiome members, recipient immune state, medication or reduced viability of anaerobic donor fecal cells following the intervention.
Recipient factors consistently outweighed donor factors in driving FMT strain-level outcomes. Thus, our data did not support the super-donor hypothesis15 which states that certain donor microbiome properties are crucial to colonization and, by proxy, clinical success. Rather, we found that complementarity of donor and recipient microbiomes promoted donor colonization and recipient turnover. This phenomenon was observed across microbial resolutions, from community-level effects to conspecific strain population dissimilarity. Indeed, strain-level diversity and complementarity were the strongest determinants of FMT outcome, with relevance to rational donor selection in clinical practice16,35. Beyond screening for donor health, matching of donors to recipients based on microbiome complementarity at community, species and, in particular, strain levels may increase colonization success, make clinical outcomes more predictable and reduce adverse effects.
Our data suggest that the gut microbiome is shaped by both neutral and adaptive processes post FMT, reconciling previous reports27,32. We found that limits to gut microbiome resilience at community, species and strain level can be defined by a relatively small set of measurable variables that point to distinct underlying processes. The (complementary) interplay between propagule pressure and residual species abundance provided a neutral baseline for colonization although, again, recipient effects outweighed donor effects. At the same time, our data also suggested niche effects, in particular at the level of complementary intraspecific strain populations, although no consistently adaptive traits emerged in the analysis. Previous hypotheses pertaining to the importance of metabolic capabilities such as SCFA synthesis were not supported, although we note that the inference of SCFA biosynthesis pathways from metagenomic data remains challenging and does not capture putatively differential expression of SCFA synthesis genes.
Our results indicate that microbiome dynamics following FMT are impacted by defined parameters that are tunable in clinical practice, thus supporting the notion that predictable and efficacious microbiome modulation using personalized probiotic mixtures, rather than entire complex fecal samples, is possible and may profit from an ecological perspective. In particular, our findings suggest that the targeted depletion of selected microbes in the recipient, with concurrent introduction of diverse strain populations of the same species rather than a single strain, presents a promising approach to enhancing colonization and turnover in the recipient, although links to clinical outcomes remain to be established. Thus, levering of both neutral and relevant adaptive ecological processes may pave the way towards targeted modulatory interventions on the gut microbiome, personalized to patients, with predictable microbiome-level outcomes.
We explored a large set of covariates as putative predictor variables for FMT outcomes, grouped into the following categories: (1) host clinical and procedural variables (for example, FMT indication, pre-FMT bowel preparation, FMT route and so on); (2) community-level taxonomic diversity (species richness, community composition and so on); (3) community-level metabolic profiles (abundance of specific pathways); (4) abundance profiles of individual species; (5) strain-level outcomes for other species in the system; and (6) focal species characteristics, including strain-level diversity; see Supplementary Table 6 for a full list of covariates and their definitions. We further classified covariates as either predictive ex ante variables (that is, knowable before the FMT is conducted) or post hoc variables (that is, pertaining to the post-FMT state, or the relation between pre- and post-FMT states). 2ff7e9595c
Comments