temperature optimum mid1

Deep research

Generated by just research-trait; source: research/traits/environment/temperature_optimum_mid1-deep-research-falcon.md

# Curation report: microbial **temperature optimum mid1**

## Executive assessment

**Target trait:** `METPO:1000443`  
**Label:** temperature optimum mid1  
**Parent:** `METPO:1000304`  
**Definition supplied for curation:** best-growth ambient temperature approximately **22–27 °C**, characteristic of lower-mesophilic physiology.

The trait should be modeled primarily as an **assay-derived cardinal-temperature phenotype**, not as a single molecular mechanism. A strain qualifies when its measured specific growth rate, under explicitly stated medium, atmosphere, pH, salinity, and measurement protocol, reaches its maximum in the 22–27 °C interval. The strongest mechanistic graph presently supportable is a generic chain in which temperature affects enzyme kinetics, proteostasis, and membrane physical state; lipid remodeling then preserves a growth-compatible membrane state. However, the literature retrieved does **not** directly establish that any one lipid, gene, or pathway is sufficient to set an organism’s optimum specifically at 22–27 °C.

The most important recent advance is a 2024 quantitative *E. coli* study showing that temperature-dependent competition between FabI and FabB, counteracted by FabR transcriptional feedback, rapidly changes membrane acyl composition. This provides strong causal edges for homeoviscous adaptation, but *E. coli* itself has an optimum near 37 °C, so these edges are mechanistic support rather than direct evidence for `METPO:1000443`. (hoogerland2024atemperaturesensitivemetabolic pages 5-6, hoogerland2024atemperaturesensitivemetabolic pages 3-4)

## 1. Trait scope and boundaries

### Operational meaning

`METPO:1000443` should represent the temperature at which a microorganism has its **maximum specific growth rate** or another explicitly accepted best-growth measure. Cardinal-temperature terminology separates minimum growth temperature, optimum growth temperature (`T_OPT`), and maximum growth temperature. A direct experimental-evolution study illustrates the appropriate assay logic: growth curves were measured over a temperature series, exponential rates were compared, and the temperature with the highest rate was assigned as `T_OPT`. (lehmann2023adaptivelaboratoryevolution pages 1-2, lehmann2023adaptivelaboratoryevolution pages 3-4)

Recommended annotation requirements are:

1. Record the tested temperature series and confirm that it brackets 22–27 °C.
2. Prefer maximum specific growth rate, μmax, derived from exponential growth. If yield, endpoint OD, colony size, or substrate turnover is used instead, identify the result as assay-specific.
3. Record medium composition, carbon and energy source, electron acceptor, pH, salinity/water activity, atmosphere, pressure, inoculum history, and acclimation time.
4. Where the temperature series is coarse, annotate an interval rather than an exact optimum.
5. Treat strain-level evidence as primary; do not infer the trait for every member of a species or genus.

### Boundary cases

- **Not growth range:** growth at 25 °C does not imply that 25 °C is optimal.
- **Not minimum or maximum temperature:** `T_MIN` and `T_MAX` delimit growth, whereas `T_OPT` identifies the peak of the reaction norm.
- **Not survival or tolerance:** persistence after cold or heat exposure is not equivalent to active growth, much less maximal growth.
- **Not acclimation:** lipid remodeling after a temperature shift can improve performance without changing the inherited optimum.
- **Not broad mesophily:** one recent study uses 25–45 °C as a broad mesophilic `T_OPT` category; `METPO:1000443` is a much narrower lower-mesophile bin. (lehmann2023adaptivelaboratoryevolution pages 3-4)
- **Not enzyme-activity optimum:** the temperature optimum of an isolated enzyme, such as EF-1A ligand binding, may correlate with organismal optimum but is not equivalent to measured whole-cell growth.
- **Conditional phenotype:** a strain’s apparent optimum can change with nutrients, oxygen, pH, or salinity. The *Thermoanaerobacter kivui* study explicitly tested medium dependence after detecting a shifted optimum. (lehmann2023adaptivelaboratoryevolution pages 3-4)

## 2. Current mechanistic understanding

The most defensible model is a **multi-constraint optimum**. At lower temperatures, biochemical reaction rates decline and membranes become more ordered. At higher temperatures, membranes become excessively fluid and proteins increasingly unfold. The observed optimum is therefore the temperature at which integrated flux through metabolism, translation, membrane transport, energy conservation, cell division, and proteostasis produces the highest net growth rate.

Membrane homeoviscous adaptation is the strongest experimentally resolved module. Cooling drives lipid bilayers toward a more ordered or gel-like state. Bacteria commonly compensate by increasing unsaturated or branched-chain fatty acids, thereby restoring membrane fluidity and the function of membrane-associated processes. (mendoza2014temperaturesensingby pages 1-2, gohrbandt2022lowmembranefluidity pages 1-2)

In *E. coli*, temperature also acts directly on fatty-acid pathway flux. FabA interconverts branch-point intermediates; FabI directs substrate toward saturated fatty acids, whereas FabB initiates the unsaturated branch and FabF can elongate C16:1-ACP to C18:1-ACP. PlsB and PlsC then incorporate acyl chains into phosphatidic acid and downstream phospholipids. FabR provides transcriptional feedback responsive to acyl-ACP pools. This pathway topology and its temperature-dependent output were directly visualized across 12–42 °C. (hoogerland2024atemperaturesensitivemetabolic pages 3-4, hoogerland2024atemperaturesensitivemetabolic media 44b7d377, hoogerland2024atemperaturesensitivemetabolic media 68fb0e6e)

The second major module is proteostasis. A coarse-grained model calibrated with quantitative *E. coli* proteomics predicts Arrhenius-like growth-rate increases over a moderate interval, but at temperature extremes protein unfolding diverts proteome resources to chaperones at the expense of ribosomal and metabolic sectors. The paper notes experimental observations of misfolding and chaperone responses, but the allocation-to-growth edges remain primarily model-supported. (mairet2021optimalproteomeallocation pages 1-2)

## 3. Candidate nodes grouped by type

### Trait and environmental nodes

- **temperature optimum mid1** — `METPO:1000443`
- **parent temperature-optimum phenotype** — `METPO:1000304`
- ambient/growth temperature — label-only pending verified ENVO or assay ontology mapping
- cooling; warming; cold shock; heat shock — label-only experimental-factor nodes
- temperature interval 22–27 °C — literal/measurement node, not an ontology class
- specific growth rate; maximum specific growth rate; growth arrest; biomass yield; lag duration — label-only phenotype/measurement nodes

### Pathways and metabolic modules

Showing the first 60 of 271 lines of findings; the linked file also carries the run's front matter and the prompt it was given — read the full report.